Review

Potential of mucilage-based hydrogel for passive cooling technology: Mucilage extraction techniques and elucidation of thermal, mechanical and physiochemical properties of mucilage-based hydrogel

  • Received: 21 September 2023 Revised: 10 November 2023 Accepted: 15 November 2023 Published: 29 November 2023
  • Current air-conditioning and refrigeration systems utilize active cooling technology, which consumes a lot of energy from fossil fuels, thereby increasing global warming and depletion of the ozone layer. Passive cooling is considered an alternative to active cooling because it is effective and less expensive and does not require the use of electricity, so cooling can be achieved in locations where there is no electricity. Hydrogels are flexible and soft 3-dimensional networks with high water content and evaporative and radiative cooling properties that make them suitable for use in passive cooling technology. Natural hydrogels are considered alternatives to synthetic hydrogels because they are biodegradable, biocompatible, sensitive to external environments and mostly sourced from plant-based sources. There are limited studies on the application of mucilage-based hydrogel for passive cooling, despite its excellent thermal, mechanical and physiochemical properties. Therefore, this study evaluates the properties of mucilage-based hydrogel as a plausible alternative to synthetic hydrogel for passive cooling. The possibility of using mucilage-based hydrogel in passive cooling technology depends on the mucilage biomass feedstock, mucilage extraction techniques, polymerization techniques and additives introduced into the hydrogel matrix. Different mucilage extraction techniques; mucilage percentage yield; the effects of crosslinkers, polymers and nanoparticle additives on the properties of mucilage-based hydrogel; and the potential of using mucilage-based hydrogel for passive cooling technology are examined in this review.

    Citation: Mercy Ogbonnaya, Abimbola P.I Popoola. Potential of mucilage-based hydrogel for passive cooling technology: Mucilage extraction techniques and elucidation of thermal, mechanical and physiochemical properties of mucilage-based hydrogel[J]. AIMS Materials Science, 2023, 10(6): 1045-1076. doi: 10.3934/matersci.2023056

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  • Current air-conditioning and refrigeration systems utilize active cooling technology, which consumes a lot of energy from fossil fuels, thereby increasing global warming and depletion of the ozone layer. Passive cooling is considered an alternative to active cooling because it is effective and less expensive and does not require the use of electricity, so cooling can be achieved in locations where there is no electricity. Hydrogels are flexible and soft 3-dimensional networks with high water content and evaporative and radiative cooling properties that make them suitable for use in passive cooling technology. Natural hydrogels are considered alternatives to synthetic hydrogels because they are biodegradable, biocompatible, sensitive to external environments and mostly sourced from plant-based sources. There are limited studies on the application of mucilage-based hydrogel for passive cooling, despite its excellent thermal, mechanical and physiochemical properties. Therefore, this study evaluates the properties of mucilage-based hydrogel as a plausible alternative to synthetic hydrogel for passive cooling. The possibility of using mucilage-based hydrogel in passive cooling technology depends on the mucilage biomass feedstock, mucilage extraction techniques, polymerization techniques and additives introduced into the hydrogel matrix. Different mucilage extraction techniques; mucilage percentage yield; the effects of crosslinkers, polymers and nanoparticle additives on the properties of mucilage-based hydrogel; and the potential of using mucilage-based hydrogel for passive cooling technology are examined in this review.



    In eutrophic lakes, cyanobacterial Microcystis colonies usually aggregate and float upwards to form nuisance mucilaginous Microcystis blooms, the development of mucilaginous cyanobacterial Microcystis blooms is a serious environmental and ecological problem, and information on the bloom-formation mechanism has been lacking until now [1]. Based on their long-term research work on cyanobacteria bloom in Taihu Lake, Qin et al. [2] believed that the outbreak of cyanobacteria bloom actually experienced four stages: 1) cyanobacteria cell proliferation stage; 2) cyanobacteria cell groups formation stage; 3) cyanobacteria cell groups floating stage; 4) cyanobacteria bloom outbreak stage. At the same time, they also deemed that in the process of cyanobacteria cell aggregation, the collision probability of cell groups could increase, and smaller cell masses were easier to bond, which could form larger groups and accelerate the emergence of cyanobacteria bloom. Furthermore, Chen et al. [3] pointed out that the aggregation and migration of Microcystis had many ecological advantages, which could promote the formation of Microcystis bloom. Kong et al. [4] proposed that the most direct reason for the formation of cyanobacteria bloom was the massive aggregation, growth, migration and floating to the water surface stage of Microcystis. Obviously, the formation of cyanobacteria cell aggregation is one of the keys to the outbreak of cyanobacteria bloom. Therefore, how to prevent and disperse cyanobacteria cell groups is one of the key control measures to prevent cyanobacteria bloom.

    Cyanobacterial blooms are commonly referred to as harmful algal blooms (HABs) due to their vast ecological impacts and ability to generate metabolites and taste, a variety of in-lake/reservoir control measures are implemented to reduce the abundance of nuisance cyanobacteria biomass[5]. Kibuye [5,6] gave a critical review on operation and performance of source water control strategies for cyanobacterial bloom: chemical control methods, mechanical/physical control methods and biological control methods. And they pointed out that each control method had site-specific strengths, limitations, and ecological impacts, which should hint that none of the reviewed control strategies can provide a comprehensive solution to cyanobacterial blooms. In view of the cyanobacteria blooms in Taihu Lake, the competent authorities mainly adopt the comprehensive method of combining mechanical control method and biological control method, this is because that Taihu Lake is a drinking water source. Although most mechanical and biological control strategies can offer long-term control, their application can be cost-prohibitive and treatment efficacy is influenced by source water geometry and continual nutrient inputs from external sources[6]. Therefore, how to coordinate mechanical control methods with biological control methods is very important, and it is also one of the problems worthy of our in-depth study.

    In order to curb the scale of cyanobacteria bloom in Taihu Lake, the management department of Taihu Lake should realize the transformation from focusing on source control and pollution interception to paying equal attention to source control, pollution interception and ecological regulation[7]. At the same time, there are many kinds of cyanobacteria in Taihu Lake, usually Microcystis[8], and allowing cyanobacteria cells to aggregate is one of the necessary conditions for cyanobacteria bloom [9]. Furthermore, once the cyanobacteria bloom breaks out, the regeneration from cell turnover and nutrient recycling by closely associated heterotrophic bacteria and microzooplankton grazers can help sustain bloom biomass, so the key biological factors to control cyanobacteria bloom must include grazing of zooplankton (possibly benthos and fish)[10]. However, some literatures have studied the dynamic relationship between cyanobacteria cell aggregation and zooplankton/fish grazing. Yang et al.[11] investigated the effect of grazing by different sorts of zooplankton on the induction of defensive morphology in the cyanobacterium Microcystis aeruginosa, and pointed out that cyanobacterium Microcystis aggregation could effectively block zooplankton predation and thus increase the survival of Microcystis aeruginosa. Burkert et al.[12] inquired into the interactions between the cyanobacteria and the potential grazer, pointed out that the predator came in direct contact with Microcystis, aggregates were formed. Chow-Fraser et al.[13] had evidence that grazers in oligotrophic lakes exert a greater impact on algae than those in eutrophic lakes. To summarise, cyanobacteria species can also benefit by their tendencies to congregate as large filamentous and colonial colonies, which reduces zooplankton predation.

    At present, the cyanobacteria bloom research team of Wenzhou University is trying to use a novel comprehensive method to deal with the problem of cyanobacteria bloom in Taihu Lake, which is a combination of mechanical control method and biological control method. Firstly, in the initial stage of cyanobacteria bloom, a part of cyanobacteria agglomerates were broken by physical jet treatment device (a cyanobacteria bloom treatment ship is developed by Wenzhou University), its purpose is to destroy the aggregation stage of cyanobacteria cells and improve the chance of individual cyanobacteria cells being grazed. Secondly, some streamline ecosystems and food web structures will be reconstructed to create unfavorable conditions for cyanobacteria, some filter-feeding organisms (such as zooplankton, mussels, bivalves and fish) can be added to feed on cyanobacteria [14,15,16]. Finally, with the help of the internal operation characteristics of aquatic ecosystem, we expect that aquatic ecosystem can quickly change from algae bloom ecosystem to algae-zooplankton-fish cycle ecosystem. However, whether or not cyanobacterial bloom can be successfully controlled, the novel comprehensive method can influence changes in competition, predation, and behavioral response to predation and competition in the restored habitat. Therefore, after the cyanobacteria bloom is treated by physical jet technology, the restoration and increase of zooplankton and filter-feeding fish how to affect the self purification ability and balance of aquatic ecosystem are worthy of our further study, whether algal population can coexist with zooplankton or filter-feeding fish in the range of relatively low algal population is also worth exploring.

    With the rapid development of numerical analysis and simulation technology, dynamic models have gradually become one of the powerful tools for studying natural science phenomena and problems, which can promote the relevant scientific problems to obtain some excellent research results, such as relevant literatures [17,18,19,20,21,22,23,24,25,26,27]. In references [17,18,19] applied reaction-diffusion equation to examine the calcium diffusion in the cells, investigated some nonlinear dynamics problems and got some interesting results. In reference [20] explored some dynamical behaviors of forced KdV equation to describe the free surface critical flow over a hole, and some meaningful results were given. In reference [21] inquired into chaotic dynamics of a fractional order HIV-1 model involving AIDS-related cancer cells, some meaningful results showed that order of the fractional derivative had a significant effect on the dynamics process. The paper [22] studied the dynamics of infectious diseases within a human host and in the population, and gave some meaningful theoretical and numerical simulation results. In reference [23] gave some good results to verify the effectiveness of linear feedback control for chaos. In reference [24] investigated a newly developed model for Hepatitis-B infection in sense of the Atangana-Baleanu Caputo fractional-order derivative, and obtained some good research conclusions. In references [25,26,27] looked into multiple bifurcations of discrete-time dynamic model, and some important theoretical and numerical results are obtained. In a word, the research on dynamic models has been developed rapidly, and a large number of excellent research results have emerged.

    Now, the disaster of aquatic ecosystem caused by cyanobacteria bloom is one of the hot issues in the world. After the cyanobacteria bloom is treated by chemical control methods, mechanical control methods and biological control methods, the essential characteristics of the internal self recovery and strengthening mechanism of aquatic ecosystem is one of the problems that need to be studied urgently. Furthermore, the algae bloom research team of Wenzhou University has built a physical spraying treatment ship, which mainly deals with the algae blooms in subtropical lakes and reservoirs, and reconstructs the biological food chain for ecological algae control after treatment. Thus, in order to understand the internal self recovery and strengthening mechanism of aquatic ecosystem, we must deeply explore the interactions between cyanobacteria (Microcystis aeruginosa) and potential grazer after cyanobacteria bloom treatment, and make clear the coexistence mode and change trend of cyanobacteria (Microcystis aeruginosa) and potential grazers based on water ecological model and dynamic simulation. Therefore, we believe that the originality of this paper is to illustrate the dynamic evolution relationship between cyanobacteria and potential grazers by means of the bifurcation dynamic evolution process of the model (2.1), and give the evolutionary characteristics of the coexistence mode of cyanobacteria and potential grazers.

    Bertalanffy [28] proposed a method to study the biological system using mathematical model in 1932, this approach is a combination of coordination, order and purpose, which can form three basic ideas of studying the biological system, namely: trophic-level analysis, system perspective and dynamic view. Since then, the research on ecological model and its related dynamics has developed unprecedentedly, and a large number of literature and works have appeared, such as fundamentals of ecological modelling [29], a nitrogen-phosphorus-blue-green algae model [30], some nitrogen and phosphorus dose-response models [31], a mathematical model including three types of zooplankton and two types of fish as well as two types of algae and nutrients [32], an aquatic bacteria-algae amensalism model [33], an ecological model considering two types of phytoplankton, three types of zooplankton, planktivorous fish, detritus and dissolved matters [34], an aquatic algae-fish model [35], one dimensional hydrodynamic-ecological model [36], a two-component model with nutrients and cyanobacteria [37], a four-component model including nutrient, unicellular algae, colonial algae and herbivorous zooplankton [38], an algal bloom mathematical model involving zooplankton-phytoplankton population [39]. Obviously, the ecological model has been developed rapidly in water eutrophication and algal bloom, and some meaningful theoretical results have been obtained. Although there is a certain understanding of the mechanism and control effect of cyanobacteria bloom in recent years, there are still many unsolved mysteries in the outbreak and control of cyanobacteria bloom.

    In order to make clear the coexistence mode and change trend of cyanobacteria (Microcystis aeruginosa) and potential grazers (mussels, bivalves, and silver carp) after cyanobacteria bloom is treated by comprehensive control strategy, which can help us explore the interactions between cyanobacteria and potential grazers, and explicit the effectiveness and feasibility of the comprehensive control strategy, we will construct a new aquatic ecological model based on the comprehensive control strategy and internal operation law of aquatic ecosystem. Therefore, some model assumptions and cyanobacteria bloom background will be given as follows:

    1) The growth environment of cyanobacteria and potential grazers is assumed to be a semi closed aquatic ecosystem, species biomass is always evenly distributed in space and changed instantaneously with time t. x(t) and y(t) represent respectively the biomass of total cyanobacteria (unicellular cyanobacteria and colonial cyanobacteria) and potential grazers. After cyanobacteria bloom is treated by mechanical control method (physical jet processing device), total cyanobacteria will be divided into colonial cyanobacteria α1x(t) and unicellular cyanobacteria α2x(t) with α1+α2=1. If α1=0 and α2=1, this shows that cyanobacteria population mainly exists in the form of unicellular cyanobacteria, and the cyanobacteria population has no aggregation behavior and can not form a group aggregation state. If α1=1 and α2=0, this indicates that cyanobacteria bloom has erupted in a large area, especially serious, and all cyanobacteria populations are clustered, which is an extreme state of existence and almost does not exist in reality.

    2) Because when unicellular cyanobacteria gather into groups, the light utilization rate of algae cells can be enhanced [40], the salt stress resistance and high light stress of algal cells can be improved [41,42], the ability to avoid the invasion of viruses, bacteria and algae phages can be strengthened [43], nutrient and inorganic carbon absorption capacity can be greatly promoted[44,45], the risk of predation can be greatly reduced [46]. In light of the above, cyanobacteria aggregation can find more suitable niche, obtain greater ecological advantages and enhance growth rate. Therefore, we assume that the growth kinetics function of cyanobacteria x(t) is f(α1α2)x(t)(1x(t)k) with intrinsic growth rate function f(α1α2) and maximum environmental capacity k,

    f(α1α2)={r1α1α2,0<α1<1,0<α2<1,rmin,α1=0,α2=1,rmax,α1=1,α2=0,

    where r1 is a median growth rate with α1=12 and α2=12, rmin is a growth rate without aggregation, and, rmax is a growth rate with overall aggregation state.

    3) During cyanobacteria blooms, cyanobacteria can induce morphological changes to colonial forms in order to avoid zooplankton grazing, this mechanism can be an adaptive reaction for survival that has developed through evolutionary processes[47,48]. For example, Microcystis exists as colonial forms, the relevant research results are in the papers[35,38,43]. However, unicellular cyanobacteria do not have this protective mechanism. Hence, we use Holling type II functional responses a2α2x(t)y(t)b+α2x(t) to describe the predation mechanism of potential grazers on unicellular cyanobacteria, this is because the predation process of potential grazers not only take time to capture, but also depend on the abundance of unicellular cyanobacteria, where a2 is a grazing coefficient and b is a half-saturation constant. At the same time, we use mathematical functional a1(α1x(t)g(α1α2))y(t) to describe the predation mechanism of potential grazers on colonial cyanobacteria, a1 is a grazing coefficient and predation function g(α1α2) avoided by colonial cyanobacteria, and

    g(α1α2)={mα1α2,0<α1<1,0<α2<1,0,α1=0,α2=1,mmax,α1=1,α2=0,

    where m is a median biomass of colonial cyanobacteria (which can never be hunted) with α1=12 and α2=12, 0 indicates that unicellular cyanobacteria do not have this ability, mmax is a maximum biomass of colonial cyanobacteria (which can never be hunted) with overall aggregation state.

    4) It is widely known that potential grazers can not eat only cyanobacteria, and must forage other species as alternate foods. Thus, the growth kinetics function of potential grazers is r2y(t)+e1a1(α1x(t)g(α1α2))y(t)+e2a2α2x(t)y(t)b+α2x(t) with alternate coefficient r2 and absorption conversion coefficients e1 and e2. Furthermore, cyanobacteria and potential grazers have their own other losses (such as natural death), we might as well set them to m1x(t) and m2y(t) with loss coefficient m1,m2.

    Based on the above assumptions and analysis, we can construct a new aquatic ecological control model and two special aquatic ecological models with non-negative initial conditions x(0)x00 and y(0)y00, which can be described as following:

    {dx(t)dt=r1α1α2x(1xk)a1(α1xα1α2m)ya2α2xyb+α2xm1x,dy(t)dt=r2y+e1a1(α1xα1α2m)y+e2a2α2xyb+α2xm2y, (2.1)
    {dx(t)dt=rminx(1xk)a2xyb+xm1x,dy(t)dt=r2y+e2a2xyb+xm2y, (2.2)
    {dx(t)dt=rmaxx(1xk)a1(xmmax)ym1x,dy(t)dt=r2y+e1a1(xmmax)ym2y. (2.3)

    The model (2.1) represents the mixed state of unicellular cyanobacteria and colonial cyanobacteria, the biomass of unicellular cyanobacteria and colonial cyanobacteria is controlled by critical parameter α1 or α2 because of α1+α2=1. If the value of α1 belongs to (0,0.2], the model (2.1) represents cyanobacteria cell proliferation stage. If the value of α1 belongs to (0.2,0.4], the model (2.1) represents cyanobacteria cell groups formation stage. If the value of α1 belongs to (0.4,0.6], the model (2.1) represents cyanobacteria cell groups floating stage. If the value of α1 belongs to (0.6,1), the model (2.1) represents cyanobacteria bloom outbreak stage. Overall, the model (2.1) can describe that the outbreak of cyanobacteria bloom in Taihu Lake actually experienced four stages [2]. The models (2.2) and (2.3) represent the stage of unicellular cyanobacteria and colonial cyanobacteria respectively, neither of these two states will exist in reality, therefore, we only study them as two limit states.

    With the help of bifurcation dynamic analysis and dynamic simulation of the model (2.1), the main purpose of this paper is to explore how the aquatic ecosystem can recover and strengthen itself after the comprehensive treatment of cyanobacteria bloom. To solve this problem, we firstly make a qualitative analysis of the model (2.1), deduce some critical threshold conditions to ensure the existence and stability of some equilibrium points, and give some critical conditions under which the model can induce Transcritical and Hopf bifurcation. Secondly, we will comprehensively investigate how comprehensive management measures affect the dynamic characteristics of cyanobacteria bloom and evaluate the effectiveness of comprehensive management measures in controlling cyanobacteria bloom. Finally, we carry out relevant dynamic experiments to verify the feasibility of the theoretical derivation results and identify the possible coexistence mode of cyanobacteria and potential grazers, then we can analyze interaction mechanism between cyanobacteria and potential grazers in the process of cyanobacteria bloom outbreak and control.

    In order to investigate the superiority of the model (2.1), the dynamic relationship between the biomass of cyanobacteria and potential grazers was qualitatively analyzed and numerically simulated with r1=0.56, r2=0.2, k=5, a1=0.6, a2=0.4, b=0.15, m1=0.18, m2=0.28, e1=0.6, e2=0.28, α1=0.35, α2=0.65, rmin=0.2, rmax=0.8, mmax=3, m=0.1.

    From the model (2.2), we can find that the relation expression of cyanobacteria x and potential grazers y is given by

    y=(rmin  (1xk)m1)(b+x)a2.

    It is easy to see from Figure 1(a) that the dynamic relationship of cyanobacteria x and potential grazers y is a concave function as the value of cyanobacteria x increases, and there is a unique maximum value, and the maximum value of cyanobacteria population is still low. Unfortunately, the value range of cyanobacteria x is relatively small, and the biomass of potential grazers y becomes negative with the increase of cyanobacteria x. Obviously, this dynamic result can not better reflect the running law of cyanobacteria bloom control test in the laboratory.

    Figure 1.  The dynamic evolution relationship between cyanobacteria and potential grazers.

    From the model (2.3), we can find that the relation expression of cyanobacteria x and potential grazers y is given by

    y=a1x+m1xrmax   (1xk)a1mmax.

    It can clearly find from Figure 1(b) that the dynamic relationship of cyanobacteria x and potential grazers y is a convex function as the value of cyanobacteria x increases, and there is a unique minimum value 0, which is not in line with the wild growth law of cyanobacteria population because cyanobacteria population will never die out. Furthermore, it is even more regrettable that the value of potential grazers y approaches positive infinity with the increase of cyanobacteria x value, which completely violates the dynamic relationship between cyanobacteria x and potential grazers y in the process of cyanobacteria bloom.

    From the model (2.1), we can find that the relation expression of cyanobacteria x and potential grazers y is given by

    y=r1α1xα2m1xa1(α1xα1mα2)+a2α2xb+α2x.

    It is worth pointing out that potential grazers y is monotonically increasing and has supremum r1a1α2m1a1α1 as the value of cyanobacteria x increases to positive infinity. Furthermore, the better result is that cyanobacteria x approaches a small fixed value when potential grazers y approaches positive infinity, this result is more in line with the natural law (see Figure 1(c)). Moreover, what is more gratifying is that there is a unique minimum value, through which we can regulate the recyclability of cyanobacteria bloom test, which is also our favorite routine manipulation.

    In a word, through the comparative analysis of numerical simulation results, it can be seen that the model (2.1) can more accurately describe the dynamic relationship between cyanobacteria and potential grazers under the mechanical and ecological integrated control, so we mainly study the relevant dynamic problems of the model (2.1).

    The existence and local stability of all possible equilibrium points will be investigated in detail. The equilibrium point is a kind of special solution of the model (2.1), which can determine some coexistence modes of cyanobacteria and potential grazers, and it is also the basis of subsequent bifurcation dynamics research. Thus, the existence and local stability of all possible equilibrium points will be futher investigated in this section.

    Now, we consider the algae isocline vertically and potential grazers isocline horizontally, respectively:

    { r1α1α2x(1xk)a1α1(xmα2)ya2α2xyb+α2xm1x=0, r2+e1a1α1(xmα2)+e2a2α2xb+α2xm2=0&y=0. (2.4)

    By solving the above equations with zero dash, there are two classes of equilibrium points: boundary equilibrium points E0 and E1, the coordinates of internal equilibria E1 and E2.

    It is obvious that the equilibrium points are the intersections of these nullclines. Thus, we easily see that the model (2.1) possesses two boundary equilibrium points E0(0,0) and E1(x1,0). For the possible positive equilibria, we only need consider the positive solutions of the following equations:

    { a1e1α1α2x2+(α2r2α2m2+a2e2α2+a1be1α1a1e1α1m)x+b(r2m2a1e1α1bmα2)=0, r1α1α2x(1xk)a1α1(xmα2)ya2α2xyb+α2xm1x=0. (2.5)

    For positive equilibrium points, the algae population x must satisfy mα2<x<k, Let Δ(x) denote the discriminant of the first equation of (2.5) and express Δ(x) in terms of, i.e,

    Δ(x)=B24AC,

    where, B=(r2m2+a2e2)α2+a1e1α1ba1e1α1m,

    A=a1e1α1α2, C=b(r2m2a1e1α1bmα2).

    Hence, we have

    Δ=((r2m2+a2e2)α2+a1e1α1ba1e1α2m)24a1e1α1α2b(r2m2+a1e1m).

    With respect to the conditions of the equilibrium points, we obtain the following theorem:

    Theorem 1. The equilibrium points of the model (2.1) are as follows: the model(2.1) always has a boundary equilibrium point E0(0,0). If α1α2>m1r1, then the model (2.1) has a boundary equilibrium point E1(x1,0) with x1=k(r1α1m1α2)r1α1.

    Theorem 2. For the positive equilibrium point, we have:

    1) When mI1, the model (2.1) has no positive equilibrium point E1(x1,y1),

    2) When r2m2+a2e2>0 and r2m2<0, if max{0,I1}<m<min{I2,I3,I4} or max{0,I1,I2}<m<min{I3,I4}, then the model (2, 1) has a unique positive equilibrium point E1(x1,y1),

    3) When r2m2+a2e2<0, if max{0,I1,I3}<m<min{I2,I4} or max{0,I1,I2,I3}<m<I4, then the model (2.1) has a unique positive equilibrium point E1(x1,y1),

    where

    x1=((r2m2+a2e2)α2+a1e1α1ba1e1α2m)+Δ2a1e1α1α2,y1=(r1α1α2m1)x1r1α1kα2x21a1(α1x1α1mα2)+a2α2x1b+α2x1,
    I1=(r2m2)α2a1e1α1,I2=α2(r2m2+a2e2)α1a1e1,I3=b(m2r2)r2m2+a2e2,I4=(kα22+bα2)(r2m2+a1e1α22ka1e1α1(α2k+b).

    Proof. Let the solution of the nullclines g(x)=Ax2+Bx+C=0, we know that the model (2.1) has the positive roots only if r2m2a1e1α1mα2<0, which means mI1. At the same time, we also show that if mI1, then Δ>0 and C<0, which means that g(x) has a unique positive root E1(x1,y1). Hence, if g(mα2)<0 and g(k)>0, there is a positive real root in mα2<x<k. Due to the fact that Δ>0, hence, if g(mα2)<0 and g(k)>0, we have the conditions with I3 and I4. If g(mα2)<0, it follows that r2m2+a2e2>0 for m<I3 or r2m2+a2e2<0 for m>I3. If m<I4, we have f(k)>0. Next, we just discuss the symbols of B.

    If m=I2, we have B=0, then g(x)=0 has a unique positive root x1=b(α2m2α2r2+a1e1α1m)a1e1α1α22. Similarly, if m<I2 or m>I2, then B>0 or B<0, we must know that g(x)=0 has a unique positive root x. From the above analysis, the model (2.1) has a unique positive equilibrium point if and only if one of the following situation:

    1) When m=I2, the model (2.1) has a unique equilibrium point E1(x1,y1).

    2) When r2m2+a2e2>0 and r2m2<0, if max{0,I1}<m<min{I2,I3,I4} or max{0,I1,I2}<m<min{I3,I4}, then the model (2.1) has a unique positive equilibrium point E1(x1,y1).

    3) When r2m2+a2e2<0, if max{0,I1,I3}<m<min{I2,I4} or max{0,I1,I2,I3}<m<I4, then the model (2.1) has a unique positive equilibrium point E1(x1,y1).

    Next, we will study the types and stability of the equilibrium points. Firstly, the Jacobian matrix of the model (2.1) is given as follows:

    J(x,y)=(r1α1α2(12xk)a1α1ya2α2by(b+α2x)2m1a1α1(xmα2)a2α2xb+α2xe1a1α1y+a2e2α2by(b+α2x)2r2m2+e1a1α1(xmα2)+e2a2α2xb+α2x).

    Theorem 3. With respect to the origin of this equilibrium point E0, we have:

    1) When α1α2<m1r1 and α1α2<m2r2e1a1m hold, the equilibrium point E0 is a stable node.

    2) When α1α2>m1r1 and α1α2<m2r2e1a1m hold, or α1α2<m1r1 and α1α2>m2r2e1a1m hold, the equilibrium point E0 is a saddle.

    Proof. The Jacobian matrix of the equilibrium point E0 is

    JE0=(r1α1α2m1α1a1mα20r2m2+α1e1a1mα2).

    Obviously, JE0 has two characteristic roots λ1=r1α1α2m1, λ2=r2m2+α1e1a1mα2. We applied the Routh-Hurwitz criterion to analyze the stability. The equilibrium point E0 is a stable node since λ1<0 and λ2<0, whiler1α1α2<m1 and r2+e1a1α1mα2<m2 hold. The equilibrium point E0 is a saddle since λ1<0 and λ2<0, while r1α1α2<m1 and r2+e1a1α1mα2<m2 hold. Similarly it can be proved that the equilibrium point E0 is a saddle if r1α1α2<m1 and r2+e1a1α1mα2>m2.

    Theorem 4. When α1α2>m1r1, then the equilibrium point E1(x1,0) is a stable node if r2m2α1e1a1mα2+e1η1+e2η2<0, and the equilibrium point E1(x1,0) is a saddle if r2m2α1e1a1mα2+e1η1+e2η2>0.

    Proof. The Jacobian matrix of E1 is

    JE1=(m1α1r1α2α1a1mα2η1η20r2m2α1e1a1mα2+e1η1+e2η2),

    where

    η1=a1k(α1r1α2m1)r1,η2=a2kα2(α1r1α2m1)α1r1bα2k(α1r1α2m1).

    Then the eigenvalues of JE1 are λ3=m1α1r1α2, λ4=r2m2α1e1a1mα2+e1η1+e2η2. It is obvious that λ3<0, hence the equilibrium point E1(x1,0) is a stable node if λ4<0, and the equilibrium point E1 is a saddle if λ4>0.

    Theorem 5. The conditions for the existence of the internal equilibrium point E1(x1,y1) are shown in Theorem 2. In this paper, it has been proved that there is and only one internal equilibrium point E1(x1,y1). If Tr(JE1(x1,y1))<0, then the internal equilibrium point E1(x1,y1) is locally asymptotically stable; otherwise, the internal equilibrium point E1(x1,y1) is unstable.

    Proof. The Jacobian matrix of the internal equilibrium point E1 is

    JE1=(r1α1α2(12x1k)a1α1y1a2α2by1(b+α2x1)2m1a1α1(x1mα2)a2α2x1b+α2x1e1a1α1y1+a2e2α2by1(b+α2x1)20).

    The determinant of the matrix JE1 is

    Det(JE1)=(e1a1α1y1+a2e2α2by1(b+α2x1)2)(a1α1(x1mα2)+a2α2x1b+α2x1).

    The trace of the matrix JE1 is

    Tr(JE1)=r1α1α2(12x1k)a1α1y1a2α2by1(b+α2x1)2m1.

    It is obvious that all terms of the product of determinants are positive, hence, we can judge that the determinants of the Jacobian matrix of E1 is always positive. Thus, when Tr(JE1)<0, the internal equilibrium point E1 has two negative eigenvalues, which means that the internal equilibrium point E1 is locally asymptotically stable. Otherwise, both characteristic roots are positive, and the internal equilibrium point E1 is unstable.

    In order to probe some possible coexistence evolution modes of cyanobacteria and potential grazers during the outbreak of cyanobacteria bloom, and clarify how the sheltering effect of colonial cyanobacteria affects the coexistence mode of cyanobacteria and potential grazers, we choose m as the control parameter to study the bifurcation dynamic behavior of the model (2.1), mainly including Transcritical bifurcation and Hopf bifurcation. If the model (2.1) can have Transcritical bifurcation, which implies that the cyanobacteria and potential grazers can coexist in a certain range at least, and there will be no extinction of at least one population with the advance of time. If the model (2.1) can have Hopf bifurcation, which shows that cyanobacteria and potential grazers can coexist in a periodic oscillation mode in a certain range, and the two populations can continue to survive with the advance of time. Therefore, bifurcation dynamics of the model (2.1) has important significance in population ecology and is worthy of our in-depth study.

    The existence of Transcritical bifurcation is studied at the boundary equilibrium point E1(x1,0), we should ensure that α1α2>m1r1 is satisfied. Thus, we state the following theorem.

    Theorem 6. The model (2.1) can undergo a Transcritical bifurcation at the boundary equilibrium poin E1(x1,0) when mmTC=α2α1e1a1(r2m2+e1η1+e2η2).

    Proof. Now, we use Sotomayor's Theorem to verify the transversality condition for the occurrence of Transcritical bifurcation at mmTC=α2α1e1a1(r2m2+e1η1+e2η2), it can be easily verified that Det(JE1)=0. Since λ3λ4=Det(JE1), which implies that JE1TC has a zero eigenvalue, that is to say λ4=0, thus the Jacobian matrix at E1 is

    JE1TC=(m1α1r1α2α1a1mα2η1η200).

    Let v and w be the two eigenvectors corresponding to the zero eigenvalue of the matrices JE1TC and JTE1TC respectively, and they are given by

    v=(v11),w=(01),

    where v1=α2(r2m2e1η2+e2η2)e1(α1r1α2m1).

    Furthermore, we can get

    Fm(E1,mTC)=(α1a1yα2α1e1a1yα2)(E1,mTC)=(00),
    DFm(E1,mTC)v=(0α1a1α20α1e1a1α2)(v11)(E1,mTC)=(α1a1α2α1e1a1α2),
    D2Fm(E1,mTC)(v,v)=(2F1x2v21+22F1xyv1v2+2F1y2v222F2x2v21+22F2xyv1v2+2F2y2v22)(E1,mTC)
    =(0α2(r2m2e1η2+e2η2)e1(α1(r1+m1)m1)(a1e1α1+a2e2bα2(b+α2(α1r1α2m1)r1α1)2)),

    clearly, they can satisfy the transversality conditions:

    wTFm(E1,mTC)=0,

    wT[DFm(E1,mTC)v]=α1e1a1α20,

    wT[D2Fm(E1,mTC)(v,v)]=2α2(r2m2e1η2+e2η2)e1(α1(r1+m1)m1)(a1e1α1+a2e2bα2(b+α2(α1r1α2m1)r1α1)2)0.

    Therefore, when mmTC=α2α1e1a1(r2m2+e1η1+e2η2), the model (2.1) can induce Transcritical bifurcation at the boundary equilibrium point E1.

    In the view of the proof of Theorem 5, we can know that Hopf bifurcation can occur at the positive equilibrium point E1. At the same time, the positive equilibrium point E1 can change its stability when the value of m passes through the critical magnitude. Thus, we summarize our findings in the following theorem.

    Theorem 7. Based on Theorem 5, the model (2.1) can induce a Hopf bifurcation when m=mHP.

    Proof. The characteristic equation of matrix JE1 is λ2Tr(JE1)λ+Det(JE1)=0, and three cross-sectional conditions of Hopf bifurcation occurs, i.e.,

    1) Tr(JE1)m=mHP=0,

    2) Det(JE1)m=mHP>0,

    3) dTr(JE1)dαm=mHP=0.

    Previously, we can show that Det(JE1)m=mHP>0. And when m=mHP, we have Tr(JE1)m=mHP=0. Thus we have dTr(JE1)dαm=mHP=0, where

    Tr(JE1)m=mHP=r1α1α2(12x1k)a1α1y1a2α2by1(b+α2x1)2m1=0.

    In conclusion, the model (2.1) can induce a Hopf bifurcation when m=mHP. Next, we will discuss the stability of the limit cycle by computing the first Lyapunov number. Translate the equilibrium point E1 to the origin by using the following transformation:

    x=xmx1,y=ymy1.

    Hence, we obtain

    {˙xm=a10xm+a01ym+a20x2m+a11xmym+a02y2m+a30x3m+a21x2mym+a12xmy2m+a03y3m+Q(xm,ym),˙ym=b10xm+b01ym+b20x2m+b11xmym+b02y2m+b30x3m+b21x2mym+b12xmy2m+b03y3m+P(xm,ym),

    where

        a10=a1α1y1+a2α2y1bx1α2+r1α1k+2r1α1x1α2k+a2α22x1y1(bx1α2)2m1,    a20=r1α1α2ka2α22y1(bx1α2)2a2α32x1y1(bx1α2)3,        a30=a2α32y1(bx1α2)3+a2α42x1y1(bx1α2)4,    a21=a2α22(bx1α2)2+a2α32x1(bx1α2)3,        a11=a1α1a2α2bx1α2a2α22x1(bx1α2)2,    a01=α1a1mα2+a1x1y1+a2α2x1bx1α2,        b10=ya1e1α1e2a2α2y1bx1α2e2a2α22x1y1(bx1α2)2,    b20=e2a2α22y1(bx1α2)2+e2a2α32x1y1(bx1α2)3,        b30=e2a2α32y1(bx1α2)3e2a2α42x1y1(bx1α2)4,    b21=e2a2α22(bx1α2)2e2a2α32x1(bx1α2)3,        b11=e2a2α2bx1α2+e2a2α22x1(bx1α2)2+a1e1α1,    b01=α1a1e1mα2m2+r2a1e1α1x1e2a2α2x1bx1α2,        a02=a12=a03=b02=b12=b03=0.

    And then Q(xm,ym), P(xm,ym) are power series in (xm,ym) with terms ximyjm, which can satisfy i+j4. Therefore, the first Lyapunov number to determine the stability of limit cycle is given by the formula

    l1=3π2a01Δ3/2{[a10b10(a211+a11b02+a02b11)+a10a01(b211+a20b11+a11b02)+b210(a11a02+2a02b02)2a10b10(b202a20a02)2a10a01(a220b20b02)a201(2a20b20+b11b20)+(a01b102a210)(b11b02a11a20)](a210+a01b10)[3(b10b03a01a30)+2a10(a21+b12)+(b10a12a01b21)]}. 

    It is well known that the limit cycle generated by the Hopf bifurcation is unstable if l1>0 and stable if l1<0. Nevertheless, the expression of the first Lyapunov number is too complex to judge the positive and negative. Therefore, we will give some numerical solutions in Section 6.

    Under the research framework of theoretical derivation, we will not only verify the feasibility and effectiveness of the theoretical results, but also explore how clustering affects the dynamic relationship between cyanobacteria and potential grazers, thus the parameter m and β=α1α2 are selected as control variables for numerical simulation with r1=0.56, r2=0.2, k=5, a1=0.6, a2=0.4, b=0.15, m1=0.18, m2=0.28, e1=0.6, e2=0.28, it should be noted that all parameter values are estimated according to biological significance and theoretical conditions. Furthermore, β=α1α2 represents the proportion of colonial cyanobacteria and unicellular cyanobacteria, which can also indicate the outbreak degree of cyanobacteria bloom. If β=α1α2>1 indicates that the manifestation of cyanobacteria is mainly colonial cyanobacteria, which is not conducive to the capture of potential grazers. If β=α1α2<1 indicates that the manifestation of cyanobacteria is mainly unicellular cyanobacteria, which is conducive to the capture of potential grazers. Moreover, it should be pointed out that the predation behavior of potential grazers on cyanobacteria is essentially the implementation of ecological control technology.

    When we take β=0.5385 with α1=0.35 and α2=0.65, which represents that the cyanobacteria in the whole ecosystem are in the bloom period, showing the phenomenon of aggregation. In other words, the current situation is not conducive to potential grazers to prey on cyanobacteria. Based on numerical simulation work, the bifurcation diagram of the model (2.1) with control parameter m has been shown in Figure 2(a). It is easy to find that the model (2.1) will experience a Hopf bifurcation and Transcritical bifurcation with the value of m changing within [0,1.8]. When the value of m gradually decreases from 1.8 to mTC=1.42282, the model (2.1) will occur a Transcritical bifurcation (Figure 3), the boundary equilibrium point E1 will change from a stable state to an unstable state, which suggests that potential grazers will not always be close to extinction.

    Figure 2.  The evolution process of coexistence mode of cyanobacteria and potential grazers. The red line represents the internal equilibrium point x1 changing by m, solid line represents the equilibrium point stable, dashed line represents the equilibrium point unstable and two vertical dashed lines represent the critical value of bifurcation. More detailed, Hp and TC are the critical values for the Hopf bifurcation, Transcritical bifurcation of the model, respectively. (a) mHP=0.2957018, mTC=1.42282; (b) mHP=0.1412414, mTC=1.74941.
    Figure 3.  Evolution process of steady-state coexistence mode with α1=0.35 and α2=0.65.

    And when the value of m is between (mHP,mTC), the model (2.1) has an asymptotically stable internal equilibrium point E1 (seeing Figure 3(b)), which indicates that cyanobacteria and potential grazers can coexist in a stable equilibrium state. If the value of m gradually decreases to mHP=0.2957018, the model (2.1) will occur Hopf bifurcation, the internal equilibrium point will change from a stable state to an unstable state, and a stable limit cycle will appear, the detailed numerical simulation results are shown in Figures 4 and 5. Furthermore, we can get that the Lyapunov exponent is 2.57010×107, which can further verify that the limit cycle is stable. Moreover, it should be explained that the dynamic characteristics of the model (2.1) have changed substantially, which means that the coexistence state of cyanobacteria and potential grazers changes from a stable equilibrium state to a stable periodic oscillation state.

    Figure 4.  Evolution process of periodic oscillation coexistence mode with α1=0.35 and α2=0.65. (a) We have stable periodic orbits bifurcate through Hopf bifurcation around E1(0.48682,0.16138) with m=mHP=0.2957018; (b) Local amplification of (a); (c) E1(0.50822,0.16493) is a locally asymptotically stable with m=0.315>mHP; (d) E1(0.46966,0.15840) is an unstable focus with m=0.28<mHP.
    Figure 5.  Dynamic evolution diagram of Hopf bifurcation with critical value m=mHP=0.2957018.

    When we take β=1.083 with α1=0.52 and α2=0.48, which represents that the cyanobacteria in the whole ecosystem are still in the bloom period, but the current situation is quite disadvantageous to potential grazers to prey on cyanobacteria because unicellular cyanobacteria account for 0.48. It is easy to discover from Figure 2(b) that the model (2.1) has almost the same dynamic evolution process. When the value of m gradually decreases from 2 to mTC=1.74941, a Transcritical bifurcation will occur (Figure 6). When the value of m continues to decrease to mHP=0.125, the instability of the equilibrium point can produce a stable limit cycle, the detailed dynamic results are shown in Figures 7 and 8.

    Figure 6.  Evolution process of steady-state coexistence mode with α1=0.52 and α2=0.48.
    Figure 7.  Evolution process of periodic oscillation coexistence mode with α1=0.35 and α2=0.65. (a) We have stable periodic orbits bifurcate through Hopf bifurcation around E1(0.38961,0.58723) with m=mHP=0.14124142; (b) Local amplification of (a); (c) E1(0.41055,0.60801) is locally asymptotically stable with m=0.155>mHP; (d) E1(0.36531,0.56250) is unstable focus with m=0.125<mHP.
    Figure 8.  Dynamic evolution diagram of Hopf bifurcation with critical value m=mHP=0.1412414.

    At the same time, the Lyapunov exponent is 5.15158×1014, which can show the stability of the limit cycle. From the essence of system dynamics evolution, whether the value of β is greater than 1 or less than 1, the model (2.1) has exactly the same dynamic evolution characteristics, that is, Transcritical bifurcation and Hopf bifurcation. That is to say, the ratio of colonial cyanobacteria to unicellular cyanobacteria does not affect the evolution characteristics of the dynamic state of the model (2.1). Moreover, it is also worth emphasizing that the value of parameter m seriously affects the dynamic relationship and coexistence mode between cyanobacteria and potential grazers.

    Based on the comparative analysis of numerical simulation results, a Transcritical bifurcation will occur at mTC=1.42282 when the value of β is less than 1, however a Transcritical bifurcation will occur at mTC=1.74941 when the value of β is greater than 1. This result means that during the outbreak of cyanobacteria bloom, the earlier the clustered cyanobacteria population is crushed by physical spray treatment technology, the more opportunities there are to change the dynamic relationship between cyanobacteria and potential grazers, and provide an entry point for the coexistence of cyanobacteria and potential grazers. However, the critical value of inducing Hopf bifurcation is just the opposite. When the value of β is less than 1, Hopf bifurcation can be induced at m=0.2957018, which suggests that potential grazers actively prey on unicellular cyanobacteria to obtain sufficient food, which can lead to a large increase in the biomass of potential grazers. This increased predation ability can lead to essential changes in the dynamic relationship between them, and then play a role in controlling the large-scale outbreak of cyanobacteria bloom. However, when the value of β is greater than 1, Hopf bifurcation can be induced at m=0.14124142, which means that the number of unicellular cyanobacteria can not basically maintain the growth needs of potential grazers, and a large number of colonial cyanobacteria need to be prey, which will cause colonial cyanobacteria to be gradually broken. This result also leads to a stable equilibrium between cyanobacteria and potential grazers in the range of m. Therefore, from the perspective of controlling cyanobacteria bloom by physical spray treatment technology, the colonial cyanobacteria turns into unicellular cyanobacteria after the cyanobacteria bloom is treated by physical spray, which is conducive to potential grazers to prey, and then lead to the coexistence mode of potential grazers and cyanobacteria in a stable state.

    Within the framework of physical and ecological integrated control and management of cyanobacteria bloom, according to the growth characteristics and comprehensive management characteristics of wild cyanobacteria population, we mainly focus on the impact of physical spraying treatment technology on the structural characteristics of cyanobacteria population and the predation effect of potential grazers, and give some ecological modeling assumptions. On this basis, a aquatic ecological dynamic new model is proposed to describe the dynamic relationship between cyanobacteria and potential grazers. At the same time, it is worth emphasizing from Figure 1 that the biggest advantage of this new model is that it integrates three kinds of dynamic action modes, of which the sub model (2.1) is the most distinctive and has not been studied by predecessors. Furthermore, the biggest advantage of the model (2.1) is that it can perfectly describe the dynamic relationship between cyanobacteria and potential grazers under the physical and ecological integrated control, most commendably, it can control cyanobacteria bloom from the perspective of minimum value, and the consumption of potential predators is limited.

    Underlying the analysis of mathematical theory, we first explore the existence and stability of the equilibrium point of the model (2.1), and give some critical conditions of some critical parameters. Then, we obtain some key threshold conditions to ensure that the model (2.1) has specific dynamic behaviors, such as Transcritical bifurcation and Hopf bifurcation. These theoretical results are the basis for our numerical simulation analysis, which provides theoretical support for in-depth exploration of the dynamic relationship between cyanobacteria and potential grazers.

    Based on the results of numerical analysis, the dynamic behavior type and dynamic evolution process of the model (2.1) are clarified, focusing on the Transcritical bifurcation and Hopf bifurcation. It is easy to find from Figure 2 that when cyanobacteria bloom break out and gather into clusters, we implement a physical and ecological comprehensive control strategy for cyanobacterial bloom, the dynamic relationship between cyanobacteria and potential grazers suddenly changes by Transcritical bifurcation, which can form coexistence mode in a stable equilibrium state, the detailed dynamic simulation results are shown in Figures 3 and 6. After then, the biomass of cyanobacteria and potential grazers gradually decrease with the reduction of agglomeration, finally, a stable periodic oscillation coexistence mode is formed to maintain a good cycle of ecological algae control strategy, these dynamic simulation results are shown in Figures 4, 5, 7 and 8. Furthermore, it is more meaningful to obtain from Figures 3 and 6 that Transcritical bifurcation represents evolution process of steady-state coexistence mode, and it is the most ecologically significant result form Figures 4 and 7 that Hopf bifurcation represents evolution process of periodic oscillation coexistence mode. In addition, it should be emphasized that the most important role of physical spraying treatment technology is to break up clumps of cyanobacteria in the process of controlling cyanobacteria bloom, which will not change the dynamic essential characteristics of cyanobacteria and potential grazers represented by the model (2.1).

    Based on theoretical and numerical research results, the main results of this paper are as follows: 1) The population density of cyanobacteria decreases gradually as the value of key parameter m decreases, that is to say, the implementation of physical spraying treatment technology can effectively control the population density of cyanobacteria. 2) Transcritical bifurcation can induce steate-state coexistence mode between cyanobacteria and potential grazers, and Hopf bifurcation can force cyanobacteria and potential grazers to form periodic oscillation coexistence mode. 3) The evolutionary characteristics of bifurcation dynamics suggest that physical and ecological integrated control technique can effectively inhibit the growth of cyanobacteria, and promote the formation of a stable coexistence mode between cyanobacteria and potential grazers within a controllable range.

    In general, some results have been obtained by proposing a new aquatic ecological model, however, the work of this paper still has some limitations, such as: 1) the characteristics of toxicity released by cyanobacteria population were not considered in the modeling, 2) cyanobacteria migration behavior is not mentioned, 3) modeling assumptions make that the application of the model (2.1) has certain limitations. Thus there is still much work to be further studied, for example, how does the release of cyanobacteria toxins affects the predatory ability of potential grazers during physical spray treatment, effect of cyanobacteria migration behavior on algae control effect of physical spraying technology, etc. In a word, it is hoped that the research results of this paper will provide some theoretical support for the application of physical and ecological comprehensive management of cyanobacteria bloom.

    This work was supported by the key International Cooperation Projects of China (Grant No: 2018YFE0103700), the National Natural Science Foundation of China (Grant No: 31570364, 61871293, 41876124, 61901303), by the Zhejiang Provincial Natural Science Foundation of China (Grant No: 2Q18C030002), by key research and development projects of Zhejiang Province (Grant No: 2021C03166), by the Science and Technology program of Cangnan, China (Grant No: 2018ZG29), by Science and Technology Major Program of Wenzhou, China (Grant No: 2018ZG002), by the Science and Technology Program of Wenzhou, China (Grant No.X20210097).

    The authors declare there is no conflict of interest.



    [1] Lickley M, Solomon S, Fletcher S, et al. (2020) Quantifying contributions of chlorofluorocarbon banks to emissions and impacts on the ozone layer and climate. Nat Commun 11: 1380. https://doi.org/10.1038/s41467-020-15162-7 doi: 10.1038/s41467-020-15162-7
    [2] Madduma-Bandarage USK, Madihally SV (2021) Synthetic hydrogels: Synthesis, novel trends, and applications. J Appl Polym Sci 138: e50376. https://doi.org/10.1002/app.50376 doi: 10.1002/app.50376
    [3] Zhao W, Jin X, Cong Y, et al. (2013) Degradable natural polymer hydrogels for articular cartilage tissue engineering. J Chem Technol Biotechnol 88: 327–339. https://doi.org/10.1002/jctb.3970 doi: 10.1002/jctb.3970
    [4] Rehman WU, Asim M, Hussain S, et al. (2020) Hydrogel: A promising material in pharmaceutics. Curr Pharm Des 26: 5892–5908. https://doi.org/10.2174/1381612826666201118095523 doi: 10.2174/1381612826666201118095523
    [5] Rajendran JV, Thomas S, Jafari Z, et al. (2022) Recent advances on large-scale manufacture of curcumin and its nanoformulation for cancer therapeutic application. Biointerface Res Appl Chem 12: 7863–7885. https://doi.org/10.33263/BRIAC126.78637885 doi: 10.33263/BRIAC126.78637885
    [6] Loo HL, Goh BH, Lee LH, et al. (2022) Application of chitosan-based nanoparticles in skin wound healing. Asian J Pharm Sci 17: 299–332. https://doi.org/10.1016/j.ajps.2022.04.001 doi: 10.1016/j.ajps.2022.04.001
    [7] Liu RR, Shi QQ, Meng YF, et al. (2023) Biomimetic chitin hydrogel via chemical transformation. Nano Res 4: 1–7. https://doi.org/10.1007/s12274-023-5886-5 doi: 10.1007/s12274-023-5886-5
    [8] Michelini L, Probo L, Farè S, et al. (2020) Characterization of gelatin hydrogels derived from different animal sources. Mater Lett 272: 127865. https://doi.org/10.1016/j.matlet.2020.127865 doi: 10.1016/j.matlet.2020.127865
    [9] Tosif MM, Najda A, Bains A, et al. (2021) A comprehensive review on plant-derived mucilage: Characterization, functional properties, applications, and its utilization for nanocarrier fabrication. Polymers 13: 1066. https://doi.org/10.3390/polym13071066 doi: 10.3390/polym13071066
    [10] Cakmak H, Ilyasoglu-Buyukkestelli H, Sogut E, et al. (2023) A review on recent advances of plant mucilages and their applications in food industry: Extraction, functional properties and health benefits. Food Hydrocolloids Health 3: 100131. https://doi.org/10.1016/j.fhfh.2023.100131 doi: 10.1016/j.fhfh.2023.100131
    [11] Beikzadeh S, Khezerlou A, Jafari SM, et al. (2020) Seed mucilages as the functional ingredients for biodegradable films and edible coatings in the food industry. Adv Colloid Interfac 280: 102164. https://doi.org/10.1016/j.cis.2020.102164 doi: 10.1016/j.cis.2020.102164
    [12] Chiang JH, Ong DSM, Ng FSK, et al. (2021) Application of chia (Salvia hispanica) mucilage as an ingredient replacer in foods. Trends Food Sci Tech 115: 105–116. https://doi.org/10.1016/j.tifs.2021.06.039 doi: 10.1016/j.tifs.2021.06.039
    [13] Timilsena YP, Adhikari R, Kasapis S, et al. (2016) Molecular and functional characteristics of purified gum from Australian chia seeds. Carbohyd Polym 136: 128–136. https://doi.org/10.1016/j.carbpol.2015.09.035 doi: 10.1016/j.carbpol.2015.09.035
    [14] Rostamabadi MM, Falsafi SR, Nishinari K, et al. (2023) Seed gum-based delivery systems and their application in encapsulation of bioactive molecules. Crit Rev Food Sci 63: 9937–9960. https://doi.org/10.1080/10408398.2022.2076065 doi: 10.1080/10408398.2022.2076065
    [15] Hesarinejad MA, Sami Jokandan M, Mohammadifar MA, et al. (2018) The effects of concentration and heating-cooling rate on rheological properties of Plantago lanceolata seed mucilage. Int J Biol Macromol 115: 1260–1266. https://doi.org/10.1016/j.ijbiomac.2017.10.102 doi: 10.1016/j.ijbiomac.2017.10.102
    [16] Olawuyi IF, Kim SR, Lee WY (2021) Application of plant mucilage polysaccharides and their techno-functional properties' modification for fresh produce preservation. Carbohyd Polym 272: 118371. https://doi.org/10.1016/j.carbpol.2021.118371 doi: 10.1016/j.carbpol.2021.118371
    [17] Sacco P, Lipari S, Cok M, et al. (2021) Insights into mechanical behavior and biological properties of chia seed mucilage hydrogels. Gels 7: 47. https://doi.org/10.3390/gels7020047 doi: 10.3390/gels7020047
    [18] Liu Y, Liu Z, Zhu X, et al. (2021) Seed coat mucilages: Structural, functional/bioactive properties, and genetic information. Compr Rev Food Sci Food Saf 20: 11–26. https://doi.org/10.1111/1541-4337.12742 doi: 10.1111/1541-4337.12742
    [19] Halász K, Tóth A, Börcsök Z, et al. (2022) Edible antioxidant films from ultrasonically extracted Plantago psyllium seed husk flour mucilage. J Polym Environ 30: 2685–2694. https://doi.org/10.1007/s10924-022-02409-1 doi: 10.1007/s10924-022-02409-1
    [20] Safdar B, Zhihua P, Xinqi L, et al. (2020) Influence of different extraction techniques on recovery, purity, antioxidant activities, and microstructure of flaxseed gum. J Food Sci 85: 3168–3182. https://doi.org/10.1111/1750-3841.15426 doi: 10.1111/1750-3841.15426
    [21] Niknam R, Ghanbarzadeh B, Ayaseh A, et al. (2020) Barhang (Plantago major L.) seed gum: Ultrasound-assisted extraction optimization, characterization, and biological activities. J Food Process Preserv 44: e14750. https://doi.org/10.1111/jfpp.14750 doi: 10.1111/jfpp.14750
    [22] Dybka-Stępień K, Otlewska A, Góźdź P, et al. (2021) The renaissance of plant mucilage in health promotion and industrial applications: A review. Nutrients 13: 3354. https://doi.org/10.3390/nu13103354 doi: 10.3390/nu13103354
    [23] Ang AMG, Raman IC (2019) Characterization of mucilages from abelmoschus manihot linn., amaranthus spinosus linn. and talinum triangulare (jacq.) willd. leaves for pharmaceutical excipient application. Asian J Biol Life Sci 8: 16–24. https://doi.org/10.5530/ajbls.2019.8.3 doi: 10.5530/ajbls.2019.8.3
    [24] Ma F, Wang R, Li X, et al. (2020) Physical properties of mucilage polysaccharides from Dioscorea opposita Thunb. Food Chem 311: 126039. https://doi.org/10.1016/j.foodchem.2019.126039 doi: 10.1016/j.foodchem.2019.126039
    [25] Nazir S, Wani IA, Masoodi FA (2017) Extraction optimization of mucilage from Basil (Ocimum basilicum L.) seeds using response surface methodology. J Adv Res 8: 235–244. https://doi.org/10.1016/j.jare.2017.01.003 doi: 10.1016/j.jare.2017.01.003
    [26] Tosif MM, Najda A, Klepacka J, et al. (2022) Concise review on taro mucilage: Extraction techniques, chemical composition, characterization, applications, and health attributes. Polymers 14: 1163. https://doi.org/10.3390/polym14061163 doi: 10.3390/polym14061163
    [27] Estévez AM, Saenz C, Hurtado ML, et al. (2004) Extraction methods and some physical properties of mesquite (Prosopis chilensis (Mol) Stuntz) seed gum. J Sci Food Agric 84: 1487–1492. https://doi.org/10.1002/jsfa.1795 doi: 10.1002/jsfa.1795
    [28] Quintero-García M, Gutiérrez-Cortez E, Bah M, et al. (2021) Comparative analysis of the chemical composition and physicochemical properties of the mucilage extracted from fresh and dehydrated opuntia ficus indica cladodes. Foods 10: 2137. https://doi.org/10.3390/foods10092137 doi: 10.3390/foods10092137
    [29] Rodríguez-González S, Martínez-Flores HE, Chávez-Moreno CK, et al. (2014) Extraction and characterization of mucilage from wild species of opuntia. J Food Process Eng 37: 285–292. https://doi.org/10.1111/jfpe.12084 doi: 10.1111/jfpe.12084
    [30] Kassem IAA, Joshua Ashaolu T, Kamel R, et al. (2021) Mucilage as a functional food hydrocolloid: Ongoing and potential applications in prebiotics and nutraceuticals. Food Funct 12: 4738–4748. https://doi.org/10.1039/d1fo00438g doi: 10.1039/d1fo00438g
    [31] Waghmare R, Preethi R, Moses JA, et al. (2022) Mucilages: Sources, extraction methods, and characteristics for their use as encapsulation agents. Crit Rev Food Sci Nutr 62: 4186–4207. https://doi.org/10.1080/10408398.2021.1873730 doi: 10.1080/10408398.2021.1873730
    [32] Capello C, Fischer U, Hungerbuhler K (2007) What is a green solvent? A comprehensive framework for the environmental assessment of solvents. Green Chem 9: 927–934. https://doi.org/10.1039/b617536h doi: 10.1039/b617536h
    [33] Tian S, Hao C, Xu G, et al. (2017) Optimization conditions for extracting polysaccharide from Angelica sinensis and its antioxidant activities. J Food and Drug Anal 25: 766–775. https://doi.org/10.1016/j.jfda.2016.08.012 doi: 10.1016/j.jfda.2016.08.012
    [34] Cong Q, Chen H, Liao W, et al. (2016) Structural characterization, and effect on anti-angiogenic activity of a fucoidan from Sargassum fusiforme. Carbohyd Polym 136: 899–907. https://doi.org/10.1016/j.carbpol.2015.09.087 doi: 10.1016/j.carbpol.2015.09.087
    [35] Saifullah Md, McCullum R, Vuong QV (2021) Optimization of microwave–assisted extraction of polyphenols from lemon myrtle: Comparison of modern and conventional extraction techniques based on bioactivity and total polyphenols in dry extracts. Processes 9: 2212. https://doi.org/10.3390/pr9122212 doi: 10.3390/pr9122212
    [36] Desai M, Parikh J, Parikh PA (2010) Extraction of natural products using microwaves as a heat source. Sep Purif Rev 39: 1–32. https://doi.org/10.1080/15422111003662320 doi: 10.1080/15422111003662320
    [37] Chen C, Zhang B, Huang Q, et al. (2017) Microwave-assisted extraction of polysaccharides from Moringa oleifera Lam. leaves: Characterization and hypoglycemic activity. Ind Crop Prod 100: 1–11. https://doi.org/10.1016/j.indcrop.2017.01.042 doi: 10.1016/j.indcrop.2017.01.042
    [38] Shiehnezhad M, Zarringhalami S, Malekjani N (2023) Optimization of microwave-assisted extraction of mucilage from ocimum basilicum var. album (l.) seed. J Food Process 2023: 552462. https://doi.org/10.1155/2023/5524621 doi: 10.1155/2023/5524621
    [39] Al-Dhabi NA, Ponmurugan K (2020) Microwave assisted extraction and characterization of polysaccharide from waste jamun fruit seeds. Int J Biol Macromol 152: 1157–1163. https://doi.org/10.1016/j.ijbiomac.2019.10.204 doi: 10.1016/j.ijbiomac.2019.10.204
    [40] Han YL, Gao J, Yin YY, et al. (2016) Extraction optimization by response surface methodology of mucilage polysaccharide from the peel of Opuntia dillenii haw. fruits and their physicochemical properties. Carbohyd Polym 151: 381–391. https://doi.org/10.1016/j.carbpol.2016.05.085 doi: 10.1016/j.carbpol.2016.05.085
    [41] Felkai-Haddache L, Dahmoune F, Remini H, et al. (2016) Microwave optimization of mucilage extraction from Opuntia ficus indica Cladodes. Int J Biol Macromol 84: 24–30. https://doi.org/10.1016/j.ijbiomac.2015.11.090 doi: 10.1016/j.ijbiomac.2015.11.090
    [42] Zhao JL, Zhang M, Zhou HL (2019) Microwave-assisted extraction, purification, partial characterization, and bioactivity of polysaccharides from panax ginseng. Molecules 24: 1605. https://doi.org/10.3390/molecules24081605 doi: 10.3390/molecules24081605
    [43] Wei E, Yang R, Zhao H, et al. (2019) Microwave-assisted extraction releases the antioxidant polysaccharides from seabuckthorn (Hippophae rhamnoides L.) berries. Int J Biol Macromol 123: 280–290. https://doi.org/10.1016/j.ijbiomac.2018.11.074 doi: 10.1016/j.ijbiomac.2018.11.074
    [44] Castejón N, Luna P, Señoráns FJ (2017) Ultrasonic removal of mucilage for pressurized liquid extraction of omega-3 rich oil from chia seeds (Salvia hispanica L.). J Agric Food Chem 65: 2572–2579. https://doi.org/10.1021/acs.jafc.6b05726 doi: 10.1021/acs.jafc.6b05726
    [45] Huang S, Ning Z (2010) Extraction of polysaccharide from Ganoderma lucidum and its immune enhancement activity. Int J Biol Macromol 47: 336–341. https://doi.org/10.1016/j.ijbiomac.2010.03.019 doi: 10.1016/j.ijbiomac.2010.03.019
    [46] Hedayati S, Niakousari M, Babajafari S, et al. (2021) Ultrasound-assisted extraction of mucilaginous seed hydrocolloids: Physicochemical properties and food applications. Trends Food Sci Tech 118: 356–361. https://doi.org/10.1016/j.tifs.2021.10.022 doi: 10.1016/j.tifs.2021.10.022
    [47] Wang W, Ma X, Xu Y, et al. (2015) Ultrasound-assisted heating extraction of pectin from grapefruit peel: Optimization and comparison with the conventional method. Food Chem 178: 106–114. https://doi.org/10.1016/j.foodchem.2015.01.080 doi: 10.1016/j.foodchem.2015.01.080
    [48] Majzoobi M, Hedayati S, Farahnaky A (2015) Functional properties of microporous wheat starch produced by α-amylase and sonication. Food Biosci 11: 79–84. https://doi.org/10.1016/j.fbio.2015.05.001 doi: 10.1016/j.fbio.2015.05.001
    [49] Pereira GA, Silva EK, Peixoto Araujo NM, et al. (2019) Obtaining a novel mucilage from mutamba seeds exploring different high-intensity ultrasound process conditions. Ultrasonics Sonochem 55: 332–340. https://doi.org/10.1016/j.ultsonch.2019.01.010 doi: 10.1016/j.ultsonch.2019.01.010
    [50] Zhao X, Qiao L, Wu AM (2017) Effective extraction of Arabidopsis adherent seed mucilage by ultrasonic treatment. Sci Rep 7: 40672. https://doi.org/10.1038/srep40672 doi: 10.1038/srep40672
    [51] Akhtar MN, Mushtaq Z, Ahmad N, et al. (2019) Optimal ultrasound-assisted process extraction, characterization, and functional product development from flaxseed meal derived polysaccharide gum. Processes 7: 189. https://doi.org/10.3390/pr7040189 doi: 10.3390/pr7040189
    [52] Souza GS, de Cassia Bergamasco R, Stafussa AP, et al. (2020) Ultrasound-assisted extraction of Psyllium mucilage: Evaluation of functional and technological properties. Emir J Food Agr 32: 238–244. https://doi.org/10.9755/ejfa.2020.v32.i4.2089 doi: 10.9755/ejfa.2020.v32.i4.2089
    [53] Silva LA, Sinnecker P, Cavalari AA, et al. (2022) Extraction of chia seed mucilage: Effect of ultrasound application. Food Chem Adv 1: 100024. https://doi.org/10.1016/j.focha.2022.100024 doi: 10.1016/j.focha.2022.100024
    [54] Zhu C, Zhai X, Li L, et al. (2015) Response surface optimization of ultrasound-assisted polysaccharides extraction from pomegranate peel. Food Chem 177: 139–146. https://doi.org/10.1016/j.foodchem.2015.01.022 doi: 10.1016/j.foodchem.2015.01.022
    [55] Yeh YC, Lai LS (2022) Effect of extraction procedures with ultrasound and cellulolytic enzymes on the structural and functional properties of Citrus grandis Osbeck seed mucilage. Molecules 27: 612. https://doi.org/10.3390/molecules27030612 doi: 10.3390/molecules27030612
    [56] Kim JH, Lee HJ, Park Y, et al. (2013) Mucilage removal from cactus cladodes (Opuntia humifusa Raf.) by enzymatic treatment to improve extraction efficiency and radical scavenging activity. Lwt-Food Sci Technol 51: 337–342. https://doi.org/10.1016/j.lwt.2012.10.009 doi: 10.1016/j.lwt.2012.10.009
    [57] Chiang CF, Lai LS (2019) Effect of enzyme-assisted extraction on the physicochemical properties of mucilage from the fronds of Asplenium australasicum (J. Sm.) Hook. Int J Biol Macromol 124: 346–353. https://doi.org/10.1016/j.ijbiomac.2018.11.181 doi: 10.1016/j.ijbiomac.2018.11.181
    [58] Zeng W, Lai L (2016) Characterization of the mucilage extracted from the edible fronds of bird's nest fern (Asplenium australasicum) with enzymatic modifications. Food Hydrocolloid 53: 84–92. https://doi.org/10.1016/j.foodhyd.2015.03.02 doi: 10.1016/j.foodhyd.2015.03.02
    [59] Tavares LS, Junqueira LA, Guimarães ICO, et al. (2018) Cold extraction method of chia seed mucilage (Salvia hispanica L.): Effect on yield and rheological behavior. J Food Sci Technol 55: 457–466. https://doi.org/10.1007/s13197-017-2954-4 doi: 10.1007/s13197-017-2954-4
    [60] Mutlu S, Kopuk B, Palabiyik I (2023) Effect of cold atmospheric pressure argon plasma jet treatment on the freeze-dried mucilage of chia seeds (salvia hispanica l.). Foods 12: 1563. https://doi.org/10.3390/foods12081563 doi: 10.3390/foods12081563
    [61] Li X, Zhang ZH, Qi X, et al. (2021) Application of nonthermal processing technologies in extracting and modifying polysaccharides: A critical review. Compr Rev Food Sci Food Saf 20: 4367–4389. https://doi.org/10.1111/1541-4337.12820 doi: 10.1111/1541-4337.12820
    [62] Ramazzina I, Berardinelli A, Rizzi F, et al. (2015) Effect of cold plasma treatment on physicochemical parameters and antioxidant activity of minimally processed kiwifruit. Postharvest Biol Technol 107: 55–65. https://doi.org/10.1016/j.postharvbio.2015.04.008 doi: 10.1016/j.postharvbio.2015.04.008
    [63] Mehta D, Purohit A, Bajarh P, et al. (2022) Cold plasma processing improved the extraction of xylooligosaccharides from dietary fibers of rice and corn bran with enhanced in-vitro digestibility and anti-inflammatory responses. Innov Food Sci Emerg Technol 78: 103027. https://doi.org/10.1016/j.ifset.2022.103027 doi: 10.1016/j.ifset.2022.103027
    [64] Brasoveanu M, Nemtanu MR (2020) Pasting properties modelling and comparative analysis of starch exposed to ionizing radiation. Radiat Phys Chem 168: 108492. https://doi.org/10.1016/j.radphyschem.2019:108492 doi: 10.1016/j.radphyschem.2019.108492
    [65] Zielinska S, Cybulska J, Pieczywek P, et al. (2022) Structural morphology and rheological properties of pectin fractions extracted from okra pods subjected to cold plasma treatment. Food Bioprocess Technol 15: 1168–1181. https://doi.org/10.1007/s11947-022-02798-0 doi: 10.1007/s11947-022-02798-0
    [66] Muñoz LA, Cobos A, Diaz O, et al. (2012) Chia seeds: Microstructure, mucilage extraction and hydration. J Food Eng 108: 216–224. https://doi.org/10.1016/j.jfoodeng.2011.06.037 doi: 10.1016/j.jfoodeng.2011.06.037
    [67] Wang WH, Lu CP, Kuo MI (2022) Combination of ultrasound and heat in the extraction of chia seed (salvia hispanica l.) mucilage: Impact on yield and technological properties. Processes 10: 519. https://doi.org/10.3390/pr10030519 doi: 10.3390/pr10030519
    [68] Xue X, Hu Y, Wang S, et al. (2022) Fabrication of physical and chemical crosslinked hydrogels for bone tissue engineering. Bioact Mater 12: 327–339. https://doi.org/10.1016/j.bioactmat.2021.10.029 doi: 10.1016/j.bioactmat.2021.10.029
    [69] Hu W, Wang Z, Xiao Y, et al. (2019) Advances in crosslinking strategies of biomedical hydrogels. Biomater Sci 7: 843–855. https://doi.org/10.1039/C8BM01246F doi: 10.1039/C8BM01246F
    [70] Parhi R (2017) Cross-linked hydrogel for pharmaceutical applications: A review. Adv Pharm Bull 7: 515–530. https://doi.org/10.15171/apb.2017.064 doi: 10.15171/apb.2017.064
    [71] Palencia M, Lerma TA, Garcés V, et al. (2021) Eco-friendly hydrogels, In: Palencia M, Lerma TA, Garcés V, et al. Eco-friendly Functional Polymers, Amsterdam: Elsevier, 141–153. https://doi.org/10.1016/B978-0-12-821842-6.00015-4
    [72] Samateh M, Pottackal N, Manafirasi S, et al. (2018) Unravelling the secret of seed-based gels in water: The nanoscale 3D network formation. Sci Rep 8: 7315. https://doi.org/10.1038/s41598-018-25691-3 doi: 10.1038/s41598-018-25691-3
    [73] Sharma G, Kumar A, Devi K, et al. (2018) Guar gum-crosslinked-Soya lecithin nanohydrogel sheets as effective adsorbent for the removal of thiophanate methyl fungicide. Int J Biol Macromol 114: 295–305. https://doi.org/10.1016/j.ijbiomac.2018.03.093 doi: 10.1016/j.ijbiomac.2018.03.093
    [74] Deore UV, Mahajan HS (2022) Hydrogel for topical drug delivery based on Mimosa pudica seed mucilage: Development and characterization. Sustain Chem Pharm 27: 100701. https://doi.org/10.1016/j.scp.2022.100701 doi: 10.1016/j.scp.2022.100701
    [75] Sharma R, Gupta RK, Rani A (2023) Hydrogels based on mucilage of underutilized cereals: Synthesis and characterization. Indian J Chem Techn 30: 524–533. https://doi.org/10.56042/ijct.v30i4.70238 doi: 10.56042/ijct.v30i4.70238
    [76] Mahmood A, Erum A, Mumtaz S, et al. (2022) Preliminary investigation of linum usitatissimum mucilage-based hydrogel as possible substitute to synthetic polymer-based hydrogels for sustained release oral drug delivery. Gels 8: 170. https://doi.org/10.3390/gels8030170 doi: 10.3390/gels8030170
    [77] Choudhary V, Sharma S, Shukla PK, et al. (2023) Biocompatible stimuli responsive hydrogels of okra mucilage with acrylic acid for controlled release phytochemicals of Calendula officinalis: In vitro assay. Mater Today Proc (in press). https://doi.org/10.1016/j.matpr.2023.03.701
    [78] Rodríguez-Loredo NA, Ovando-Medina VM, Pérez E, et al. (2023) Preparation of poly(acrylic acid)/linseed mucilage/chitosan hydrogel for ketorolac release. J Vinyl Addit Technol 29: 890–900. https://doi.org/10.1002/vnl.22023 doi: 10.1002/vnl.22023
    [79] Hosseini MS, Hemmati K, Ghaemy M (2016) Synthesis of nanohydrogels based on tragacanth gum biopolymer and investigation of swelling and drug delivery. Int J Biol Macromol 82: 806–815. https://doi.org/10.1016/j.ijbiomac.2015.09.067 doi: 10.1016/j.ijbiomac.2015.09.067
    [80] Moya-Ortega MD, Alvarez-Lorenzo C, Sigurdsson HH, et al. (2012) Cross-linked hydroxypropyl-β-cyclodextrin and γ-cyclodextrin nanogels for drug delivery: Physicochemical and loading/release properties. Carbohyd Polym 87: 2344–2351. https://doi.org/10.1016/j.carbpol.2011.11.005 doi: 10.1016/j.carbpol.2011.11.005
    [81] Pathania D, Verma C, Negi P, et al. (2018) Novel nanohydrogel based on itaconic acid grafted tragacanth gum for controlled release of ampicillin. Carbohyd Polym 196: 262–271. https://doi.org/10.1016/j.carbpol.2018.05.040 doi: 10.1016/j.carbpol.2018.05.040
    [82] Dalwadi C, Patel G (2015) Application of nanohydrogels in drug delivery systems: Recent patents review. Recent Pat Nanotechnol 9: 17–25. https://doi.org/10.2174/1872210509666150101151521 doi: 10.2174/1872210509666150101151521
    [83] Sabzevar SM, Mehrshad ZM, Naimipour M (2021) A biological magnetic nano-hydrogel based on basil seed mucilage: Study of swelling ratio and drug delivery. Iran Polym J 30: 485–493. https://doi.org/10.1007/s13726-021-00905-0 doi: 10.1007/s13726-021-00905-0
    [84] Archana, Suman A, Singh V (2022) An inclusive review on mucilage: Extraction methods, characterization, and its utilization for nanocarriers manufacturing. J Drug Delivery Ther 12: 171–179. https://doi.org/10.22270/jddt.v12i1-S.5210 doi: 10.22270/jddt.v12i1-S.5210
    [85] Sen S, Bal T, Rajora AD (2022) Green nanofiber mat from HLM–PVA–Pectin (Hibiscus leaves mucilage–polyvinyl alcohol–pectin) polymeric blend using electrospinning technique as a novel material in wound-healing process. Appl Nanosci 12: 237–250. https://doi.org/10.1007/s13204-021-02295-4 doi: 10.1007/s13204-021-02295-4
    [86] Emadzadeh MK, Aarabi A, Aarabi Najvani F, et al. (2022) The effect of extraction method on physicochemical properties of mucilage extracted from yellow and brown flaxseeds. Jundishapur J Nat Pharm Prod 17: e123952. https://doi.org/10.5812/jjnpp-123952 doi: 10.5812/jjnpp-123952
    [87] Wang X, Wu Y, Chen G, et al. (2013) Optimisation of ultrasound assisted extraction of phenolic compounds from Sparganii rhizoma with response surface methodology. Ultrason Sonochem 20: 846–854. https://doi.org/10.1016/j.ultsonch.2012.11.007 doi: 10.1016/j.ultsonch.2012.11.007
    [88] Guo Y, Bae J, Fang Z, et al. (2020) Hydrogels and hydrogel-derived materials for energy and water sustainability. Chem Rev 120: 7642–7707. https://doi.org/10.1021/acs.chemrev.0c00345 doi: 10.1021/acs.chemrev.0c00345
    [89] Chai Q, Jiao Y, Yu X (2017) Hydrogels for biomedical applications: Their characteristics and the mechanisms behind them. Gels 3: 6. https://doi.org/10.3390/gels3010006 doi: 10.3390/gels3010006
    [90] Ganji F, Vasheghani FS, Vasheghani FE (2010) Theoretical description of hydrogel swelling: A review. Iran Polym J 19: 375–398.
    [91] Nerkar PP, Gattani S (2011) In vivo, in vitro evaluation of linseed mucilage based buccal mucoadhesive microspheres of venlafaxine. Drug Deliv 18: 111–121. https://doi.org/10.3109/10717544.2010.520351 doi: 10.3109/10717544.2010.520351
    [92] Sumaira, Ume RT, Alia E, et al. (2021) Fabrication, characterization, and toxicity evaluation of chemically cross-linked polymeric network for sustained delivery of metoprolol tartrate. Des Monomers Polym 24: 351–361. https://doi.org/10.1080/15685551.2021.2003995 doi: 10.1080/15685551.2021.2003995
    [93] Xu L, Sun DW, Tian Y, et al. (2022) Combined effects of radiative and evaporative cooling on fruit preservation under solar radiation: Sunburn resistance and temperature stabilization. ACS Appl Mater Interfaces 14: 45788–45799. https://doi.org/10.1021/acsami.2c11349 doi: 10.1021/acsami.2c11349
    [94] Ninan N, Muthiah M, Park IK, et al. (2013) Pectin/carboxymethyl cellulose/microfibrillated cellulose composite scaffolds for tissue engineering. Carbohyd Polym 98: 877–885. https://doi.org/10.1016/j.carbpol.2013.06.067 doi: 10.1016/j.carbpol.2013.06.067
    [95] Zhou Z, Qian C, Yuan W (2021) Self-healing, anti-freezing, adhesive and remoldable hydrogel sensor with ion-liquid metal dual conductivity for biomimetic skin. Compos Sci Technol 203: 108608. https://doi.org/10.1016/j.compscitech.2020.108608 doi: 10.1016/j.compscitech.2020.108608
    [96] Quinzio CM, Ayunta CA, Alancay MM, et al. (2018) Physicochemical and rheological properties of mucilage extracted from Opuntia ficus indica (L. Miller). Comparative study with guar gum and xanthan gum. Food Measure 12: 459–470. https://doi.org/10.1007/s11694-017-9659-2 doi: 10.1007/s11694-017-9659-2
    [97] Martin AA, de Freitas RA, Sassaki GL, et al. (2017) Chemical structure and physical-chemical properties of mucilage from the leaves of Pereskia aculeata. Food Hydrocoll 70: 20–28. https://doi.org/10.1016/j.foodhyd.2017.03.020 doi: 10.1016/j.foodhyd.2017.03.020
    [98] Singh S, Bothara SB (2014) Physico-chemical and structural characterization of mucilage isolated from seeds of Diospyros melonoxylon Roxb. Braz J Pharm Sci 50: 713–726. http://dx.doi.org/10.1590/S1984-82502014000400006 doi: 10.1590/S1984-82502014000400006
    [99] Sriamornsak P (2003) Chemistry of pectin and its pharmaceutical uses: A review. Silpakorn Univ Int J 3: 206–228.
    [100] Glaue ÖŞ, Akcan T, Tavman Ş (2023) Thermal properties of ultrasound-extracted okra mucilage. Appl Sci 13: 6762. https://doi.org/10.3390/app13116762 doi: 10.3390/app13116762
    [101] Santos FSD, de Figueirêdo RMF, Queiroz AJM, et al. (2023) Physical, chemical, and thermal properties of chia and okra mucilages. J Therm Anal Calorim 148: 7463–7475. https://doi.org/10.1007/s10973-023-12179-0 doi: 10.1007/s10973-023-12179-0
    [102] Xin F, Lyu Q (2023) A review on thermal properties of hydrogels for electronic devices applications. Gels 9: 7. https://doi.org/10.3390/gels9010007 doi: 10.3390/gels9010007
    [103] Guo H, Ge J, Li L, et al. (2022) New insights and experimental investigation of high-temperature gel reinforced by nano-SiO2. Gels 8: 362. https://doi.org/10.3390/gels8060362 doi: 10.3390/gels8060362
    [104] Pardeshi S, Damiri F, Zehravi M, et al. (2022) Thermoresponsiveness hydrogel molecule. Encyclopedia https://encyclopedia.pub/entry/25914
    [105] Matsumoto K, Sakikawa N, Miyata T (2018) Thermo-responsive gels that absorb moisture and ooze water. Nat Commun 9: 2315. https://doi.org/10.1038/s41467-018-04810-8 doi: 10.1038/s41467-018-04810-8
    [106] Miranda ND, Renaldi R, Khosla R, et al. (2021) Bibliometric analysis and landscape of actors in passive cooling research. Renew Sust Energ Rev 149: 111406. https://doi.org/10.1016/j.rser.2021.111406 doi: 10.1016/j.rser.2021.111406
    [107] Yang Y, Cui G, Lan CQ (2019) Developments in evaporative cooling and enhanced evaporative cooling–A review. Renew Sust Energ Rev 113: 109230. https://doi.org/10.1016/j.rser.2019.06.037 doi: 10.1016/j.rser.2019.06.037
    [108] Zeyghami M, Goswami DY, Stefanakos E (2018) A review of clear sky radiative cooling developments and applications in renewable power systems and passive building cooling. Sol Energ Mat Sol C 178: 115–128. https://doi.org/10.1016/j.solmat.2018.01.015 doi: 10.1016/j.solmat.2018.01.015
    [109] Zhao B, Wang C, Hu M, et al. (2022) Light and thermal management of the semi-transparent radiative cooling glass for buildings. Energy 238: 121761. https://doi.org/10.1016/j.energy.2021.121761 doi: 10.1016/j.energy.2021.121761
    [110] Cai L, Song AY, Li W, et al. (2018) Spectrally selective nanocomposite textile for outdoor personal cooling. Adv Mater 30: 1–7. https://doi.org/10.1002/adma.201802152 doi: 10.1002/adma.201802152
    [111] Zhao B, Xuan Q, Zhang W, et al. (2023) Low-emissivity interior wall strategy for suppressing overcooling in radiatively cooled buildings in cold environments. Sustain Cities 99: 104912. https://doi.org/10.1016/j.scs.2023.104912 doi: 10.1016/j.scs.2023.104912
    [112] Zhao B, Xu C, Jin C, et al. (2023) Superhydrophobic bilayer coating for passive daytime radiative cooling. Nanophotonics https://doi.org/10.1515/nanoph-2023-0511 doi: 10.1515/nanoph-2023-0511
    [113] Zhao B, Xuan Q, Xu C, et al. (2023) Considerations of passive radiative cooling. Renew Energ 219: 119486. https://doi.org/10.1016/j.renene.2023.119486 doi: 10.1016/j.renene.2023.119486
    [114] Feng C, Yang P, Liu H, et al. (2021) Bilayer porous polymer for efficient passive building cooling. Nano Energy 85: 105971. https://doi.org/10.1016/j.nanoen.2021.105971 doi: 10.1016/j.nanoen.2021.105971
    [115] Lu Z, Strobach E, Chen N, et al. (2020) Passive sub-ambient cooling from a transparent evaporation–insulation bilayer. Joule 4: 2693–2701. https://doi.org/10.1016/j.joule.2020.10.005 doi: 10.1016/j.joule.2020.10.005
    [116] Xu L, Sun D, Tian Y, et al. (2023) Nanocomposite hydrogel for daytime passive cooling enabled by combined effects of radiative and evaporative cooling. Chem Eng J 457: 141231. https://doi.org/10.1016/j.cej.2022.141231 doi: 10.1016/j.cej.2022.141231
    [117] Tu Y, Wang R, Zhang Y, et al. (2018) Progress and expectation of atmospheric water harvesting. Joule 2: 1452–1475. https://doi.org/10.1016/j.joule.2018.07.015 doi: 10.1016/j.joule.2018.07.015
    [118] Pollard J (2023) Hydrogel-coated heat sinks enhance passive CPU cooling efficiency. Available from: https://www.electropages.com.
    [119] Mu X, Shi X, Zhou J, et al. (2023) Self-hygroscopic and smart color-changing hydrogels as coolers for improving energy conversion efficiency of electronics. Nano Energy 10: 108177. https://doi.org/10.1016/j.nanoen.2023.108177 doi: 10.1016/j.nanoen.2023.108177
    [120] Abdo S, Saidani-Scott H, Benedi J, et al. (2020) Hydrogels beads for cooling solar panels: Experimental study. Renew Energ 153: 777–786. https://doi.org/10.1016/j.renene.2020.02.057 doi: 10.1016/j.renene.2020.02.057
    [121] Amith MG, Joshi VV (2022) Cooling enhancement using acacia gum based hydrogel in roof ponds: Model room experimental analysis. Res Square https://doi.org/10.21203/rs.3.rs-1560597/v1 doi: 10.21203/rs.3.rs-1560597/v1
    [122] Future Market Insight. Hydrogel market. Accessed 9 November 2023. Available from: www.futuremarketinsights.com/report/hydrogel-market.
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