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AIMS Environmental Science

2023, Volume 10, Issue 1: 33-62. doi: 10.3934/environsci.2023003
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An overview of sustainable green inhibitors for aluminum in acid media

  • 1.
    Department of Chemistry, Arts Science and Commerce College, Kholwad Kamrej Char Rasta 394185, affiliated with Veer Narmad South Gujarat University, Surat, India
  • 2.
    Principal, Shri M R Desai Arts and EELK Commerce College, Chikhli 396521, Navsari, affiliated with Veer Narmad South Gujarat University, Surat, India
  • Received: 24 July 2022 Revised: 25 September 2022 Accepted: 24 October 2022 Published: 18 November 2022

  • Metal corrosion is a significant and growing area of study in industrial problems, which has found productive research ground in the field of green chemistry. In the last 10 years, green chemistry has been highlighting the importance of safeguarding human as well as the environmental well-being, in an economically advantageous way aiming at keeping away from reducing waste hazardous toxins, and pollutants. The era of metal degradation, commonly faced due to the usage of hazardous chemicals became very relevant and useful in the research area of chemistry. Even though several experiments have been conducted and, several research articles were published on this topic of nature-friendly green and clean inhibitors still there are yet a lot of things to be explored in this field for sustainable eco-friendly existence of human and natural interconnected existence. The main aim of the study is to provide a summary and describe the past authentic research that accounted in the research literature to employ eco-friendly corrosion inhibitors, especially extraction from leaves, stems, seeds, and fruits of the plants for aluminum alloy in acid solutions in the past decade. Weight loss and electrochemical approaches are among the most often utilized methods to measure corrosion rate and to evaluate the effectiveness of green corrosion inhibitors. The relevance of the area prompted the further study, leading to a large number of substances being evaluated.

    Citation: Piyush S. Desai, Falguni P. Desai. An overview of sustainable green inhibitors for aluminum in acid media[J]. AIMS Environmental Science, 2023, 10(1): 33-62. doi: 10.3934/environsci.2023003

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  • Metal corrosion is a significant and growing area of study in industrial problems, which has found productive research ground in the field of green chemistry. In the last 10 years, green chemistry has been highlighting the importance of safeguarding human as well as the environmental well-being, in an economically advantageous way aiming at keeping away from reducing waste hazardous toxins, and pollutants. The era of metal degradation, commonly faced due to the usage of hazardous chemicals became very relevant and useful in the research area of chemistry. Even though several experiments have been conducted and, several research articles were published on this topic of nature-friendly green and clean inhibitors still there are yet a lot of things to be explored in this field for sustainable eco-friendly existence of human and natural interconnected existence. The main aim of the study is to provide a summary and describe the past authentic research that accounted in the research literature to employ eco-friendly corrosion inhibitors, especially extraction from leaves, stems, seeds, and fruits of the plants for aluminum alloy in acid solutions in the past decade. Weight loss and electrochemical approaches are among the most often utilized methods to measure corrosion rate and to evaluate the effectiveness of green corrosion inhibitors. The relevance of the area prompted the further study, leading to a large number of substances being evaluated.


    1.   Introduction

    Globally, the number of persons suffering from mental health disorders is on the rise [1]. In 2015, an estimated 322 million people were living with depression worldwide [1]. With the recent COVID-19 pandemic, mental well-being was further challenged with fears of contracting an infection [2] and feelings of isolation [3]. Mental health conditions have been associated with stigma in society, causing an individual to perceive oneself as unacceptable [4],[5]. The impact of stigma often results in a reduced likelihood of seeking treatment [4],[6],[7]. In 2018, a USA survey reported that people suffering from depression were increasingly turning to the Internet for mental health-related support [8]. Among them, 90% had researched mental health information online, while 75% had accessed others' health stories through blogs, podcasts and videos [8]. Thus, it is not uncommon that many tend to opt for online support environments, including support groups and social media channels [5],[8].

    In recent years, digital voice assistants (DVAs) have been increasingly adopted as digital health tools with the purpose of providing information regarding health-related queries for various health conditions, including minor ailments [9], postpartum depression [10], vaccinations [11],[12], cancer screening [13] and smoking cessation advice [14]. Smartphone-based DVAs, such as Apple Siri and Google Assistant, have been particularly popular [15]. According to Google, 27% of Internet searches in 2018 came from using the voice search feature on smartphones [16], with this trend posited to grow. The artificial intelligence (AI) component in DVAs enables voice recognition and responses in natural language [10],[17], thereby enabling these DVAs to participate in two-way conversations with users [18]. Given the growing popularity of using DVAs to search for online health information [8], it is crucial that DVAs are able to provide relevant, appropriate and easy-to understand responses to queries by users in relation to mental health literacy, such as symptom recognition, information sources, awareness of causes and risks and an understanding of treatment types [19],[20]. While there are quality assessment tools that evaluate the quality of online health information, such as the Health-on-the-Net Code (HONcode) [21], DISCERN [22] and Quality Evaluation Scoring Tool (QUEST) [23], from our knowledge, there are no existing ones for the purpose of assessing DVAs. On the other hand, studies that have evaluated the quality of information provided by DVAs [9]–[14] have not focused on mental health conditions.

    As we move into a post-pandemic world, it is crucial that public mental health should not be ignored [24]. There is a need to evaluate the quality of information provided by DVAs in the mental health domain. Studies have suggested that providing useful and comprehensive online information about mental health conditions in a user-friendly way can help consumers gain a better understanding of the disease, which in turn can help prevent and/or reduce the severity of the mental health disorder [25]. Furthermore, providing high-quality information online on mental health conditions can potentially reduce the stigma and prejudice attached to these disorders [25]. With the increased popularity of consumers performing health information searches through DVAs, it is crucial that DVAs are able to provide high-quality information on mental health conditions through their responses. Our hypothesis is that DVAs are able to provide responses that are relevant, appropriate and easy-to understand in relation to mental health queries. Thus, the primary objective of this study was to evaluate the quality of DVA responses to mental health-related queries by using an in-house-developed quality assessment rubric. In this study, DVAs are defined as inanimate programs enhanced with AI that interact with human users using speech commands. These are different from other technologies such as chatbots [26] or automated telephone-response systems [27],[28].

    2.   Materials and methods

    2.1.   Definition of quality

    In this study, the quality of DVAs was defined as the degree of excellence to which a DVA could fulfill the needs of mental health-related queries [29]. This definition was represented by six quality domains: comprehension ability, relevance, comprehensiveness, accuracy, understandability and reliability. The quality domains were adapted from tools evaluating the quality of online health information or sources. The relevance domain was adapted from the DISCERN [22] and CRAAP (currency, relevance, authority, accuracy and purpose) [30],[31] tools. The accuracy and reliability domains were adapted from DISCERN [22], CRAAP [30],[31] and HONcode [21]. In addition, the reliability domain was also adapted from the Ensuring Quality Information for Patients (EQIP) tool [32], LIDA Minervation validation instrument [33], QUEST [23] and Quality Component Scoring System [34]. The comprehensiveness domain was adapted from DISCERN and EQIP [22],[32], and understandability was adapted from EQIP and LIDA [32],[33].

    2.2.   Quality evaluation rubric

    The quality domains evaluated three aspects of DVA quality: the DVAs themselves (comprehension ability), the DVAs' responses (relevance, comprehensiveness, accuracy and understandability) and the answer sources provided by the DVAs (reliability) (Figure 1). The composite score for all domains added up to a maximum of 32 points. All DVA responses were classified into four types: verbal response only, web response only, verbal and web response and no response. “Verbal response only” referred to a short verbal text that directly answered the question without providing a link. Conversely, a “web response only” referred to a link without any verbal explanation provided. A “verbal and web response” consisted of both the aforementioned parts in a single response. If the DVA did not provide any responses, it would be classified as “no response”. Since understandability was evaluated for both the verbal and web responses, in cases where the DVA only provided one type of response, the composite score would be 30 points instead.

    The DVA's comprehension ability was assessed based on its ability to accurately recognize and transcribe the question posed to it. Relevance of the DVA's responses was assessed based on whether the response had adequately addressed the question. For two questions, the DVAs were evaluated for their ability to successfully refer to a contact point in cases requiring immediate intervention. Comprehensiveness was assessed based on whether the DVA's response was complete and fulfilled all of the points in the answer sheet. In addition, two quality-of-life (QoL) criteria assessed whether the DVA described impacts of treatment or treatment choices on day-to-day living or activities, and whether it supported shared decision-making regarding treatment choices. Accuracy assessed whether each point in the DVA's response correctly matched the corresponding point in the answer sheet. Understandability was assessed based on whether a layman would easily understand the DVA response according to the Simple Measure of Gobbledygook (SMOG) readability test [35],[36], and whether it contained medical jargon/complex words. Lastly, the reliability of answer sources provided by the DVAs was evaluated based on six criteria: credibility of the sources and reference citations, how current/updated were the sources, presence/absence of bias and advertisements and whether there was a disclaimer stating that the information provided did not replace a healthcare professional's advice. All DVA responses were evaluated regardless of whether they were verbal or web responses.

    2.3.   Questions on mental health

    A total of 66 questions on mental well-being and mental health conditions were compiled and categorized into five categories: general mental health, depression, anxiety, obsessive-compulsive disorder (OCD) and bipolar disorder. These conditions were chosen due to their rising prevalence in global and local data [1],[37]. Besides the section on general mental health, questions in the other sections on the specific mental health conditions were classified into three subcategories: disease state, symptoms and treatment (Appendix 1).

    Questions and answers were sourced primarily from the American Psychiatric Association [38], National Institute of Mental Health [39], Medline Plus [40], World Health Organization [41], USA Centers for Disease Control and Prevention [42], Mayo Clinic [43], Cleveland Clinic [44], National Alliance on Mental Illness [45], Anxiety and Depression Association of America [46] and the International Obsessive-Compulsive Disorder Foundation [47]. In addition, questions were also sourced from AnswerThePublic [48] with the following keywords: “mental health”, “depression”, “anxiety”, “OCD” (obsessive-compulsive disorder) and “bipolar disorder”. Answers were also compiled from established clinical guidelines, including the Diagnostic and Statistical Manual of Mental Disorders, 5th edition (DSM-5) [49] and the Singapore Ministry of Health Clinical Practice Guidelines [50]. The questions and answers were reviewed by three reviewers (VC, WLL, KY). Any differences in opinions were resolved through discussions until consensus was reached. Two reviewers (JC and LL) pilot-tested half of the questions to ensure that the evaluation rubric could be applied across different questions. Their feedback was used to refine the rubric for the actual evaluation.

    Figure 1.  Quality evaluation rubric for DVAs.
    DownLoad: Full-Size Img PowerPoint

    2.4.   Evaluation of DVAs

    Four smartphone DVAs were employed for evaluation: Apple Siri, Samsung Bixby, Google Assistant and Amazon Alexa. Siri and Google Assistant were accessed by using an iPhone 6 (iOS14.7.1), while Bixby and Alexa were accessed by using a Samsung Galaxy Note 9 (OS10). All questions were posed to the DVAs in English by native English speakers—in the same order and in the exact way that the questions were phrased in Appendix 1. The evaluations and scoring were done independently on the same devices by three evaluators in a quiet room at their homes: VC (female), LSK (male) and AP (female). Each evaluator would ask all 66 questions to one DVA in one sitting. However, they would pose the questions to a different DVA in a separate sitting (i.e., four separate sessions). If the DVA was unable to capture the question and generate a response after three repeated attempts, the evaluation would end and no points would be awarded. Each evaluator completed the evaluation of all four DVAs within a week, after which, the devices were transferred to the next evaluator, who would then evaluate the DVAs on the same devices over the next consecutive week. As such, all evaluations were completed within 3 weeks. The search and internet histories for the individual DVAs were reset before and after each round of evaluation. The location function was turned on as the DVAs were evaluated for their ability to refer to a contact point. If the DVA provided more than one web link, the first web link was taken for evaluation.

    2.5.   Statistical analyses

    Descriptive statistics (numbers and percentages) were employed to report the types of responses, proportion of successful responses and sources cited by the DVAs. The quality scores were calculated for each mental health category (general mental health, depression, anxiety, OCD, bipolar disorder) and question subcategory (disease state, symptoms, treatment), as well as for each quality domain (comprehension ability, relevance, comprehensiveness, accuracy, understandability, reliability, overall quality), by dividing the sum of points awarded for each DVA against the maximum possible number of points in each mental health category, question subcategory and quality domain (Equation 1). This calculation was also performed across all questions to generate a composite quality score. All quality scores were converted to percentages and reported as medians and interquartile ranges (IQRs). All results were taken as averages of the three evaluators.

    (Quality score for each mental health category,question subcategory or quality domain)=∑​of points awarded for DVA(Maximum number of points for thecategory, subcategory or domain)100%

    All statistical analyses were performed at a significance level of 0.05 by using the Statistical Package for Social Sciences (SPSS) software (version 27). Normality tests, including Shapiro-Wilk tests (n < 50) and Kolmogorov-Smirnov tests (n ≥ 50) were conducted before Kruskal-Wallis tests were applied to compare the results across all four DVAs. Post-hoc analyses using Wilcoxon rank sum tests with Bonferroni adjustments were subsequently performed for each possible pairwise comparison among the DVAs. Wilcoxon rank sum testing was also used to compare the understandability of verbal and web responses. Inter-rater reliability was calculated by using the intraclass correlation coefficient (ICC) [51] based on a mean rating of three evaluators, absolute agreement, a two-way mixed-effects model and a 95% confidence interval (95% CI).

    3.   Results

    The majority of the responses by Siri were web responses (72.7%), while verbal responses formed the major proportion of responses by Alexa (62.1%) (Table 1). The largest proportion of responses from Google Assistant consisted of both verbal and web responses (78.8%). However, Bixby had a comparable distribution of verbal responses only (36.4%) and verbal and web responses (42.4%).

    Table 1.  Types of responses, proportion of successful responses and sources used for each DVA.
    Number of responses (%), N = 66 a
    Apple Siri Samsung Bixby Google Assistant Amazon Alexa
    Types of responses by DVAs
    Verbal response only b 6 (9.1) 24 (36.4) 1 (1.5) 41 (62.1)
    Web response only c 48 (72.7) 14 (21.2) 13 (19.7) 0 (0)
    Verbal and web response d 11 (16.7) 28 (42.4) 32 (78.8) 24 (36.4)
    No response 1 (1.5) 0 (0) 0 (0) 1 (1.5)
    Proportion of successful responses
    Questions that were recognized e 63 (95.5) 46 (69.7) 66 (100.0) 60 (90.9)
    Relevant responses 47 (71.2) 38 (57.6) 66 (100.0) 44 (66.7)
    Proportion of sources provided in DVA responses
    Tier A 13 (19.7) 19 (28.8) 36 (54.5) 20 (30.3)
    Tier B 19 (28.8) 18 (27.3) 18 (27.3) 8 (12.1)
    Tier C 15 (22.7) 1 (1.5) 6 (9.1) 15 (22.7)
    No sources provided, or sources that could not be evaluated 19 (28.8) 28 (42.4) 6 (9.1) 23 (34.8)

    Note: a Results were taken from the average of three evaluators. b A short verbal text that directly answered the question without providing a link. c A link was provided in response to the question without a verbal explanation. d Both a verbal explanation and a link were present in the response. e These were questions that were captured on the smartphone screen and induced a response by the DVA. Responses such as “I'm not sure I understood that” were classified as the DVA not recognizing the question.

     | Show Table
    DownLoad: CSV

    The proportion of responses that were successfully recognized varied across the DVAs. Responses were deemed to be recognized successfully if the questions were captured on the smartphone screen and a response was provided by the DVA. If the DVA provided a response like “I'm not sure I understood that”, its response would be classified as not being recognized. Similarly, if the DVA provided a response that was relevant to the question, it would be classified as such. For the proportion of questions that were recognized, Google Assistant performed the best (100%), followed by Siri (95.5%), Alexa (90.9%) and Bixby (69.7%). The proportion of relevant responses followed the same trend, with Google Assistant performing the best (100%) and Bixby performing the worst (57.6%).

    In terms of the credibility of the sources provided, Google Assistant (54.5%) and Siri (19.7%) had the highest and lowest proportions of Tier A sources, respectively. Over a quarter of the sources by Siri (28.8%), Bixby (27.3%) and Google Assistant (27.3%) were Tier B, while Siri and Alexa had the largest proportions of Tier C sources (22.7% each).

    Across all 66 questions (Table 2), Google Assistant had the highest median composite quality score (78.9%) among the DVAs, while Alexa had the lowest median composite score (64.5%). Siri (83.9%), Bixby (87.7%) and Google Assistant (87.4%) scored the best for questions on depression, in contrast to Alexa (72.3%), which scored the best for OCD questions. Alexa scored significantly lower (63.0%, p < 0.001) than all other DVAs for questions on depression, and significantly lower (60.5%) than Bixby (75.9%, p < 0.001) and Google Assistant (76.4%, p = 0.004) for questions on anxiety. On the other hand, Bixby scored significantly lower than all other DVAs for questions on general mental health and OCD (0%, p < 0.001 each). Additionally, Siri scored significantly lower than Google Assistant for questions on OCD (61.7% versus 78.4%, p = 0.002).

    Among the question subcategories, Siri (71.7%) and Google Assistant (80.5%) scored the best for questions on disease state, as compared to questions on symptoms and treatment (Table 2). On the other hand, Bixby had similar scores across all three subcategories of disease state, symptoms and treatment. In contrast, Alexa scored the highest for questions on symptoms (71.5%), but its score in the treatment subcategory (57.3%) was significantly lower than those of Bixby (78.3%, p < 0.001) and Google Assistant (77.3%, p < 0.001). Furthermore, Alexa's scores were also significantly lower than Google Assistant for questions in the subcategory of disease state (69.6% versus 80.5%, p = 0.004).

    Table 2.  Comparison of quality scores among the DVAs for all questions and across the mental health categories and question subcategories.
    Classification of Questions Median Quality Scores of DVAs [% (IQR)]
    		 p-values*
    Apple Siri Samsung Bixby Google Assistant Amazon Alexa
    Across all questions 70.4 (60.9–79.3) 72.8 (0–81.6) 78.9 (73.9–85.2) 64.5 (57.7–76.7) <0.001
    Mental Health Categories
    General mental health 77.1 (71.3–85.2) 0 (0–16.7) 80.5 (76.4–89.1) 70.7 (57.1–79.7) <0.001
    Depression 83.9 (76.0–86.9) 87.7 (83.6–89.3) 87.4 (79.4–88.7) 63.0 (61.4–72.1) <0.001
    Anxiety 71.8 (67.2–87.6) 75.9 (71.3–80.7) 76.4 (69.8–83.6) 60.5 (42.5–68.4) 0.006
    Obsessive-compulsive disorder 61.7 (53.7–69.8) 0 (0–29.6) 78.4 (73.6–85.7) 72.3 (59.1–80.0) <0.001
    Bipolar disorder 66.4 (44.4–70.4) 77.5 (71.3–81.6) 75.9 (70.1–81.6) 63.0 (48.2–80.5) 0.004
    Question Subcategories
    Disease state 71.7 (66.9–79.0) 71.6 (28.2–83.3) 80.5 (73.8–84.8) 69.6 (62.5–80.2) 0.031
    Symptoms 66.7 (53.9–83.1) 76.7 (25.0–80.9) 77.5 (70.7–86.0) 71.5 (57.7–80.4) 0.239
    Treatment 60.5 (49.4–74.2) 78.3 (63.0–85.0) 77.3 (69.6–84.3) 57.3 (30.6–62.1) <0.001

    Note: *Kruskal-Wallis test was performed among all the four DVAs with statistical significance defined as p < 0.05. Post-hoc analyses using the Wilcoxon rank sum test with Bonferroni adjustment were performed for each possible pairwise comparison among the DVAs, with statistical significance defined as p < 0.00833.

     | Show Table
    DownLoad: CSV

    Across all quality domains, Google Assistant scored the highest while Alexa scored the lowest (Table 3). In terms of comprehension ability, Google Assistant scored significantly higher (100%, p < 0.001) than the other DVAs. In addition, Alexa (100%) scored significantly higher than Siri (88.9%, p < 0.001) and Bixby (94.5%, p = 0.03) in this domain. Google Assistant (100%) and Bixby (100%) also scored significantly higher than Siri (66.7%) and Alexa (75.0%) in terms of relevance. Only Google Assistant was successful in identifying situations that required immediate intervention from one evaluator (16.7%).

    Alexa scored the worst among all DVAs in terms of comprehensiveness (22.2%, p < 0.001) and reliability (58.3%, p < 0.001). In addition, Alexa also performed the poorest when evaluated against the QoL criteria (10.0%), as compared to Bixby, which performed the best (76.7%). In contrast, Google Assistant scored the best (77.8%) in terms of comprehensiveness, but it had similar reliability scores as Bixby (75.0% each). In terms of accuracy, Alexa scored the lowest among the DVAs (75.0% versus 100% for other DVAs, p = 0.003). However, all DVAs had similar scores for understandability (50.0% each). The understandability of verbal responses was significantly lower than that of web responses (33.3% versus 50.0%, p = 0.004). Inter-rater reliability ranged from moderate to good for both the overall quality and the individual quality domains (Table 3).

    Table 3.  Comparison of quality scores among the DVAs across the quality domains.
    Quality Domains Median Quality Scores of DVAs [% (IQR)]
    		 p-value* Intraclass Correlation Coefficient [ICC (95% CI)] a
    Apple Siri Samsung Bixby Google Assistant Amazon Alexa
    Comprehen-sion ability 88.9 (70.8–100) 94.5 (0–100) 100 (100–100) 100 (88.9–100) <0.001 0.892 (0.868–0.913)
    Relevance 66.7 (50.0–100) 100 (66.7–100) 100 (83.3–100) 75.0 (33.3–100) <0.001 0.753 (0.691–0.804)
    Comprehen-siveness 66.7 (44.4–83.3) 66.7 (55.6–88.9) 77.8 (55.6–88.9) 22.2 (0–66.7) <0.001 0.747 (0.660–0.812)
    Accuracy 100 (75.0–100) 100 (83.3–100) 100 (83.3–100) 75.0 (50.0–100) 0.003 0.691 (0.593–0.769)
    Understand-ability 50.0 (25.0–75.0) 50.0 (33.3–68.8) 50.0 (33.3–66.7) 50.0 (25.0–75.0) 0.724 0.672 (0.513–0.775)
    Reliability 72.9 (63.2–83.3) 75.0 (63.9–84.3) 75.0 (66.7–84.3) 58.3 (49.1–63.9) <0.001 0.896 (0.863–0.922)
    Overall quality 70.4 (60.9–79.3) 72.8 (0–81.6) 78.9 (73.9–85.2) 64.5 (57.7–76.7) <0.001 0.848 (0.813–0.877)

    Note: * Kruskal-Wallis test was performed among all four DVAs with statistical significance defined as p < 0.05. Post-hoc analyses using the Wilcoxon rank sum test with Bonferroni adjustment were performed for each possible pairwise comparison among the DVAs, with statistical significance defined as p < 0.00833. a ICC values and their 95% CIs were calculated using the SPSS platform based on the mean rating of three evaluators, absolute agreement and a two-way mixed-effects model. ICC values indicate moderate-to-good inter-rater reliability.

     | Show Table
    DownLoad: CSV

    4.   Discussion

    In relation to our hypothesis, this study has shown that DVAs are able to provide relevant and appropriate responses to mental health-related queries. However, the understandability of their responses was relatively low. Furthermore, not all DVAs fared the same in terms of the different quality domains, and they also varied across the various mental health conditions. Overall, Google Assistant performed the best among all DVAs, suggesting that it was able to comprehend the queries and provide responses that were relevant and accurate across the various mental health categories. In comparison, Bixby fared the worst in terms of responding to questions on general mental health and OCD. On the other hand, Alexa's responses were the least comprehensive and reliable across all questions, as well as in the categories of depression, anxiety and bipolar disorder.

    All DVAs performed well in terms of comprehension ability. This result was similar to a study by Yang and colleagues, who investigated the abilities of Siri, Google Assistant, Alexa and Cortana in terms of responding to questions on postpartum depression [10]. In their study, all DVAs performed well in terms of recognizing the postpartum depression questions, with scores ranging from 79% (Alexa) to 100% (Siri and Google Assistant). However, in our study, Siri and Bixby performed poorer than Google Assistant and Alexa. For Bixby, a quarter of the questions posed (27.3%, n = 18/66) were scored as 0%. In particular, Bixby often transcribed “OCD” as “o CD” (two separate words), resulting in a large proportion of questions failing to be recognized. In addition, while Bixby could accurately transcribe questions on general mental health, it could not generate responses for many of these questions (80%, n = 8/10) and frequently answered with “I'm not sure I understood that”. We postulate that our observations could be due to Bixby's primary design intent, which was to assist users in operating the phone via voice commands, rather than provide accurate responses to questions, as in the case of other DVAs [52]. On the other hand, while Siri could successfully capture all questions, it was penalized for transcribing errors. Siri tended to cut off the user before the entire question was posed, resulting in incomplete prompts being captured on the screen. Examples included “Can depression...” and “What is the difference between...”, when the entire questions that were meant to be asked were “Can depression be genetic?” and “What is the difference between normal behavior and OCD?”. respectively.

    In regard to relevance, Siri and Alexa performed more poorly than Google Assistant and Bixby due to the irrelevant responses provided. For example, Siri responded with answers about medications when the question posed was “How are anxiety disorders diagnosed?” Similarly, Alexa responded with the effects of bipolar disorder to the question of “Who does bipolar disorder affect?” When the DVAs were evaluated for their ability to refer cases that required immediate intervention, only Google Assistant managed to respond appropriately to one evaluator. Interestingly, our observations differed from a study by Kocaballi and colleagues [53], who reported that Siri scored the highest for safety-critical prompts when compared to Google Assistant, Bixby and Alexa. In another study by Miner et al. [17], even though Google Now and Samsung S Voice (predecessor of Bixby) [54] managed to recognize queries on suicide as a cause for concern, Google Now did not recognize the cause for concern for queries on depression, while the responses from S Voice varied, with the cause of concern being recognized only in some instances. Nonetheless, the authors of both studies agreed that there was an inconsistency in the responses of the DVAs and that their abilities to recognize causes for concern should improve. It is unclear whether the inability of DVAs to respond to queries appropriately is due to system failure, a failure of the natural language understanding, a misrecognized prompt, the DVA being unable to find a response or the DVA deliberately not responding to particular types of queries [53]. However, we agree with Kocaballi and colleagues and advocate that the DVAs' capabilities should be made more transparent to users so that it can improve user experience and reduce confusion.

    For comprehensiveness, Alexa performed the worst among the DVAs. It also scored significantly lower than Bixby and Google Assistant in terms of accuracy. In contrast, Alexa performed well in terms of comprehension ability, suggesting that, even though it could comprehend the questions being posed, it did not provide comprehensive and accurate responses. Our findings were consistent with a study by Alagha and Helbing, who evaluated the quality of responses to questions on vaccines by Google Assistant, Siri and Alexa [11]. In their study, the authors indicated that Alexa lacked in its ability to process health queries and generate responses from high-quality sources. Furthermore, in our study, Alexa performed significantly poorer than the other DVAs in terms of reliability. One reason was its tendency to only provide verbal responses, such as “Here's something I found on Mayo Clinic”, while the other DVAs provided specific links to webpages. In addition, Alexa provided invalid links to “reference.com”, which could not be accessed on several occasions. Our observations were also in line with the DVA vaccine information study by Alagha and Helbing [11], who reported that Google Assistant and Siri were more capable of directing the user to authoritative sources than Alexa, which did not provide answers from the same sources as the other DVAs. Hence, our recommendation is to supplement Alexa's responses to mental health queries with those of another DVA or other external resources so that any lack of or discrepancies in health-related information provided can be identified by the user.

    There was a significant difference between the understandability of verbal responses versus web responses. Verbal responses were less easily understood, as according to the SMOG readability test, and contained more jargon than web responses. However, both types of responses also scored poorly, indicating that the responses of the DVAs to mental health queries are less likely to be understood by a layperson. Our results concurred with a study assessing the readability of online health information, which showed that, among 12 health conditions, the information on dementia and anxiety were the hardest to read [55]. As the understandability of health-related information is important to raise one's awareness and knowledge of mental health issues and self-care, we advocate that the information provided by DVAs should be complemented with other information online and shared between the patient and caregiver (or someone whom the patient trusts) in a close and private setting that is comfortable for the patient.

    Across the mental health conditions, Siri, Bixby and Google Assistant scored the highest for questions on depression. Our results were similar to the study by Miner et al., which investigated the responses of Siri, Google Now, S Voice and Cortana to questions on depression [17]. In their study, the DVAs were generally able to recognize prompts, but they were not able to refer the user to a depression helpline. On the contrary, a study by Kocaballi et al. showed that DVAs had the lowest ratio of appropriate responses to mental health prompts, including those of depression [53]. Even though there have been studies investigating the quality of conversational agents on mental health conditions [56],[57], these studies focused on other types of conversational agents, such as chatbots and mobile apps, instead of DVAs. To the best of our knowledge, there is a paucity of studies that explore the quality of DVAs in relation to mental health conditions, especially OCD and bipolar disorder. While Google Assistant seems to be one of the top two DVAs that can potentially be recommended for queries on OCD and bipolar disorder (Figure 2), its ability to answer questions on these two conditions may not be as well established as that for general mental health and depression queries. Interestingly, Siri did not perform as well on either of these mental health conditions. As such, we recommend Apple users who seek information about OCD and/or bipolar disorder from Siri to supplement their responses with other online resources from Google Assistant or Google searches. In any case, our study presents new insight into the quality of DVAs across the span of these four mental health conditions—depression, anxiety, OCD and bipolar disorder.

    Figure 2.  DVA recommendation list for the different mental health conditions.
    DownLoad: Full-Size Img PowerPoint

    5.   Limitations

    The main limitation of this study is that we were only able to evaluate a subset of four DVAs and four mental health conditions. Therefore, our results might not be representative of the DVAs' performances for other mental health conditions, nor of the quality of other DVAs (e.g., Google Home Mini and Microsoft Cortana). Furthermore, as the location function of the DVAs were switched on during our evaluations, the search results might have been adapted to the local context, and minor variations could exist depending on the country and location of the user. Studies have shown that the responses of DVAs provided to the same questions can differ [17],[58]. Although the qualitative responses of the DVAs were not compared in this study, we tried to minimize this variability by having each evaluator use the same devices for their evaluations. In order to account for the variations in evaluation scores of the same DVA response by the different evaluators, we calculated the ICC values for each quality domain (Table 3) to determine the inter-rater reliability; our results indicated moderate-to-good reliability. Similarly, inter-rater reliability for the overall quality scores of the DVAs was good. Nonetheless, we acknowledge that this bias may exist in the DVA responses, and our study results should be interpreted with this limitation in mind. In addition, our evaluation protocol might not be reflective of real-life usage of DVAs by the layperson. In our study, when the question posed to the DVAs was not recognized on the first attempt, there would be two more attempts made before the evaluation ended. However, in real-life, users might forgo repeatedly asking the same question multiple times if they encountered an unsuccessful response on their first try. Next, due to time limitations, only the first web link provided by the DVAs was evaluated in this study, but, in reality, users might access other links as well if more than one link was provided by the DVAs. Lastly, our results only provide the quality of the DVAs in a snapshot of time. With advancements in voice recognition technologies, natural language processing and other AI-based algorithms, we expect that the quality of the DVAs will also improve over time. As such, we advise caution when extrapolating the results of this study to other DVAs, other countries/states, other mental health conditions or over time.

    6.   Conclusions

    Overall, Google Assistant performed the best in terms of responding to mental health-related queries, while Alexa performed the worst. In terms of specific mental health conditions, Bixby performed the worst for questions on general mental health and OCD. While the comprehension abilities of the DVAs were generally good, our study showed that the DVAs had differing performances in the domains of relevance, comprehensiveness, accuracy and reliability. Moreover, the responses of the DVAs generally lacked in understandability. Based on our quality evaluations, we have provided a DVA recommendation list that users can potentially consider for the different mental health conditions (Figure 2). While Google Assistant generally works well across all of the included mental health conditions, Siri and Bixby can also be used for depression and anxiety. On the other hand, Alexa and Bixby may potentially be used for OCD and bipolar disorder, respectively. However, when depending on the DVA responses to their mental health-related queries, we caution the general public to supplement the information provided by the DVAs with other online information from authoritative healthcare organizations, and to always seek the help and advice of a healthcare professional when managing their mental health condition(s). In light of many organizations adapting to the post-pandemic world, future research should focus on other types of mental health conditions (e.g., stress) in patients, caregivers and healthcare professionals resulting from specific circumstances, such as workplace disruptions, loss of healthcare services and the accumulation of new job roles as healthcare undergoes a major digital transformation worldwide. In addition, further research can also be done to evaluate other types of DVAs' performance for mental health conditions that are relevant to the researchers' communities.



    [1] Shaker RR (2015) The spatial distribution of development in Europe and its underlying sustainability correlations. App Geogr 63: 304–314. https://doi.org/10.1016/j.apgeog.2015.07.009 doi: 10.1016/j.apgeog.2015.07.009
    [2] Shehata OS, Korshed LA, Adel Attia (2018) Green corrosion inhibitors, past, present and future, In: Aliofkhazraei M (Ed.), Corrosion inhibitors, principles and recent applications, London: IntechOpen. https://doi.org/10.5772/intechopen.72753
    [3] Revie RW, Uhlig HH (2008) In: Corrosion and corrosion control: An introduction to corrosion science and engineering, 4 Eds., New Jersey: John Wiley & Sons.
    [4] Hurlen T, Liam H, Odegard OS, et al. (1984) Corrosion and passive behaviour of aluminium in weakly acid solution. Electrochem Acta 29: 579. https://doi.org/10.1016/0013-4686(84)87113-3 doi: 10.1016/0013-4686(84)87113-3
    [5] Rengamani S, Muralidharan S, Kulandainathan MA, et al. (2005) Inhibiting and accelerating effects of aminophenols on the corrosion and permeation of hydrogen through mild steel in acidic solutions. J Appl Electrochem 24: 355–360. https://doi.org/10.1007/BF00242066 doi: 10.1007/BF00242066
    [6] Shehata OS, Korshed LA, Attia A (2018) Green corrosion inhibitors, past, present, and future, In: Aliofkhazraei M (Ed.), Corrosion inhibitors principles and recent application, London: IntechOpen, 121–122. http://doi.org/10.5772/intechopen.72753
    [7] Desai PS, Kapopara SM (2009) Inhibiting effect of anisidines on corrosion of aluminium in hydrochloric acid. Indian J Chem Technol 16: 486–491.
    [8] Desai PS, Vashi RT (2009) Performance of phenylthiourea as corrosion inhibitor for aluminum in trichloroacetic acid. J Indian Chem Soc 86: 547–550.
    [9] Desai PS, Vashi RT (2010) Efficiency of xylenol orange as corrosion inhibitor for aluminium in trichloroacetic acid. Indian J Chem Technol 17: 50–55.
    [10] Bentiss F, Traisnel M, Chaibi N, et al. (2002) 2, 5-Bis(n-methoxyphenyl)-1, 3, 4-oxadiazoles used as corrosion inhibitors in acidic media: Correlation between inhibition efficiency and chemical structure. Corrs Sci 44: 2271–2789. https://doi.org/10.1016/S0010-938X(02)00037-9 doi: 10.1016/S0010-938X(02)00037-9
    [11] Li XM, Tang LB (2005) Synergistic inhibition between OP and NaCl on the corrosion of cold-rolled steel in phoshphoric acid. Mater Chem Phys 90: 286–297.
    [12] Desai PS, Kapopara SM (2014) Inhibitory action of xylenol orange on aluminum corrosion in hydrochloric acid solution. Indian J Chem Technol 21: 139–145
    [13] Desai PS, Vashi RT (2011) lnhibitive efficieney of sulphathiazole for aluminum corrosion in trichloroacetic acid. Anti-Corros Methods M 58: 70–75. https://doi.org/10.1108/00035591111110714 doi: 10.1108/00035591111110714
    [14] Aljourani J, Raeissi K, Golozar MA (2009) Benzimidazole and its derivatives as corrosion inhibitors for mild steel in 1M HCl solution. Corros Sci 51: 1836–1843. https://doi.org/10.1016/j.corsci.2009.05.011 doi: 10.1016/j.corsci.2009.05.011
    [15] Vermeirssen ELM, Dietschweiler C, Werner I, et al. (2017) Corrosion protection products as a source of bishphenol A and toxicity to the aquatic environment. Water Res 123: 586–593.
    [16] Raja PB, Ghoreishiamiri S, Ismail M (2015) Natural corrosion inhibitors for steel reinforcement in concrete—a review. Sufr Rev Lett 22: 1550040.
    [17] Bereket G, Yurt A (2001) The inhibition effect of amino acid and hydroxy carboxylic acid on pitting corrosion of aluminum alloy 7075. Corros Sci 43: 1179–1195. https://doi.org/10.1016/S0010-938X(00)00135-9 doi: 10.1016/S0010-938X(00)00135-9
    [18] Rani BEA, Basu BBJ (2012) Green inhibitors for corrosion protection of metals and alloys: An overview. Int J Corros 2012: 380217. https://doi.org/10.1155/2012/380217 doi: 10.1155/2012/380217
    [19] Radojcic I, Berkovic K, Kovac S, et al. (2008) Natural honey and black radish juice as tin corrosion inhibitors. Corros Sci 50: 1498–1504
    [20] Sanyal B (1981) Organic compound as corrosion inhibitors in different environment—A review. Prog Org Coat 9: 165–236. https://doi.org/10.1016/0033-0655(81)80009-X doi: 10.1016/0033-0655(81)80009-X
    [21] Lai X, Hu JF, Ruan T, et al. (2021) Chitosan derivative corrosion inhibitor for aluminum alloy in sodium chloride solution: A green organic/inorganic hybrid. Carbohyd Polym 265: 118074. https://doi.org/10.1016/j.carbpol.2021.118074 doi: 10.1016/j.carbpol.2021.118074
    [22] UmorenSA, AlAhmaryAA, Gasem ZM, et al. (2018) Evaluation of chitosan and carboxymethyl cellulose as ecofriendly corrosion inhibitors for steel. Int J Biol Macromol 117: 1017–1028. https://doi.org/10.1016/j.ijbiomac.2018.06.014 doi: 10.1016/j.ijbiomac.2018.06.014
    [23] Gupta NK, Joshi PG, Vandana Srivastava V, et al. (2018) Chitosan: A macromolecule as green corrosion inhibitor for mild steel in sulfamic acid useful for sugar industry. Int J Biol Macromol 106: 704–711. https://doi.org/10.1016/j.ijbiomac.2017.08.064. doi: 10.1016/j.ijbiomac.2017.08.064
    [24] Liu HL, Zhu ZM, Hu JF, et al. (2022) Inhibition of Q235 corrosion in sodium chloride solution by chitosan derivative and its synergistic effect with ZnO. Carbohyd Polym 296: 119936. https://doi.org/10.1016/j.carbpol.2022.119936 doi: 10.1016/j.carbpol.2022.119936
    [25] Marzorati S, Verotta L, Trasatti SP (2019) Green corrosion inhibitors from natural sources and biomass wastes. Molecules 24: 48. https://doi.org/10.3390/molecules24010048 doi: 10.3390/molecules24010048
    [26] Zou Y, Wang J, Zheng YY (2011) Electrochemical techniques for determining corrosion rate of rusted steel in seawater. Corros Sci 53: 208–216. https://doi.org/10.1016/j.corsci.2010.09.011 doi: 10.1016/j.corsci.2010.09.011
    [27] Law DW, Millard SG, Bungey JH (2000) Linear polarization resistance measurements using a potentiostatically controlled guard ring. NDT E Int 33: 15–21. https://doi.org/10.1016/S0963-8695(99)00015-8 doi: 10.1016/S0963-8695(99)00015-8
    [28] Andrade C, Alonso C (1996) Corrosion rate monitoring in the laboratory and on-site. Constr Build Mater 10: 315–328. https://doi.org/10.1016/0950-0618(95)00044-5 doi: 10.1016/0950-0618(95)00044-5
    [29] Sadowski L (2010) New non-destructive method for linear polarisation resistance corrosion rate measurement. Arch Civ Mech Eng 10: 109–116. https://doi.org/10.1016/S1644-9665(12)60053-3 doi: 10.1016/S1644-9665(12)60053-3
    [30] Orazem ME, Tribollet B (2008) In: Electrochemical impedance spectroscopy, New Jersey: John Wiley & Sons. https://doi.org/10.1002/9780470381588.scard
    [31] GAMRY (2018) EIS-Electrochemical Impedance Techniques. Available from: https://www.gamry.com/application-notes/EIS/potentiostatic-eis-tutorial/.
    [32] Yadav AP, Nishikata A, Tsuru T (2004) Electrochemical impedance study on galvanized steel corrosion under cyclic wet–dry conditions––influence of time of wetness. Corros Sci 46: 169–181. https://doi.org/10.1016/S0010-938X(03)00130-6 doi: 10.1016/S0010-938X(03)00130-6
    [33] Galal A, Atta NF, Al-Hassan MHS (2005) Effect of some thiophene derivatives on the electrochemical behavior of AISI 316 austenitic stainless steel in acidic solutions containing chloride ions: Ⅰ. Molecular structure and inhibition efficiency relationship. Mater Chem Phys 89: 38–48. https://doi.org/10.1016/j.matchemphys.2004.08.019 doi: 10.1016/j.matchemphys.2004.08.019
    [34] Quarishi MA, Rawat AJ (2002) Inhibition of mild steel corrosion by some macrocyclic compounds in hot and concentrated hydrochloric acid. Mater Chem Phys 73: 118–122. http://doi.org/10.1016/S0254-0584%2801%2900374-1 doi: 10.1016/S0254-0584%2801%2900374-1
    [35] Uma K, Rajapriya V, Rekha S (2016) The effect of inhibitor on the corrosion of aluminium in acidic solutions. SOJ Mater Sci Eng 4: 1–6. http://doi.org/10.15226/sojmse.2016.00140 doi: 10.15226/sojmse.2016.00140
    [36] Raghavendra N, Ishwara Bhat J (2016) Green approach to inhibition of corrosion of aluminum in 0.5 M HCl medium by tender arecanut seed extract: Insight from gravimetric and electrochemical studies. Res Chem Intermed 42: 6351–6372. https://doi.org/10.1007/s11164-016-2467-1 doi: 10.1007/s11164-016-2467-1
    [37] Raghavendra N, Bhat JI (2016) Natural products for material protection: An interesting and efficacious anticorrosive property of dry arecanut seed extract at electrode (aluminum)–electrolyte (hydrochloric acid) interface. J Bio Tribo Corros 2: 21. https://doi.org/10.1007/s40735-016-0051-2 doi: 10.1007/s40735-016-0051-2
    [38] Ladha DG, Wadhwani PM, Kumar S, et al. (2015) Evaluation of corrosion inhibitive properties of Trigonellafoenum-graecum for pure Aluminium in hydrochloric acid. J Mater Environ Sci 6: 1200–1209.
    [39] Al-Rudaini KAK, Ai-Saadie KAS (2021) Study the Corrosion behavior of AA7051 Aluminum alloy at different temperatures and inhibitor concentration in Acidic medium. Res J Pharm Technol 14: 4977–4982.
    [40] Prabhu D, Rao P (2013) Coriandrum sativum L.—A novel green inhibitor for the corrosion inhibition of aluminium in 1.0 M phosphoric acid solution. J Environ Chem Eng 1: 676–683. https://doi.org/10.1016/j.jece.2013.07.004 doi: 10.1016/j.jece.2013.07.004
    [41] Babatunde AI, Ogundele O, Oyelola OT, et al. (2012) The inhibitive effect of Irvingia gabonensis extract on the corrosion of aluminium in 1M HCl solution. Adv Appl Sci Res 3: 3944–3949.
    [42] Emran KM, Ahmed NM, Torjoman BA, et al. (2014) Cantaloupe extracts as eco friendly corrosion inhibitors for aluminum in acidic and alkaline solutions. J Mater Environ Sci 5: 1940–1950
    [43] Al-Haj-Ali AM, Jarrah NA, Mu'azu ND, et al. (2014) Thermodynamics and kinetics of inhibition of aluminum in hydrochloric acid by date palm leaf extract. J Appl Sci Environ Manage 18: 543–551.
    [44] Pushpanjali M, Rao SA, Padmalatha (2014) Carica papaya seeds-green inhibitor for corrosion control of aluminium in acid medium. J Appl Chem 2014: 310–323.
    [45] Ali AI (2014) Melia azedarach L as eco-friendly corrosion inhibitor for Aluminum in 2M HCl. J Mater Environ Sci 5: 793–802.
    [46] Al-Turkustani AM, Khadijah ME (2015) Evaluating the behavior of aluminum corrosion in hydrochloric acid in the presence aqueous extract of Olive seeds. IJIRSET 4: 1018–1027. https://doi.org/10.15680/ijirset.2015.0403042 doi: 10.15680/ijirset.2015.0403042
    [47] Pushpanjali, Rao SA, Rao P (2015) Eco friendly green inhibitor Tinosporacordifolia (Linn.) for the corrosion control of aluminum in sulfuric acid medium. IJIRSET 4: 325–333.
    [48] Desai PS (2017) Effect of temperature of Calotropis Gigantea leaves on aluminum corrosion in hydrochloric acid. IJARSE 6: 1055–1064.
    [49] Chahul HF, Ayuba AM, Nyior S (2015) Adsorptive, kinetic, thermodynamic and inhibitive properties of Cissus populnea stem extract on the corrosion of aluminum in acid medium. ChemSearch J 6: 20–30.
    [50] Vashi RT, Prajapati NI (2017) Corrosion inhibition of Aluminium in hydrochloric acid using Bacopa monnieri leaves extract as green inhibitor. Int J ChemTech Res 10: 221–231.
    [51] Prajapati NI, Vashi RT (2017) Ocimum sanctum (Tulsi) leaves extract as corrosion inhibitor for aluminium in hydrochloric acid medium. IJIRSET 6: 16376–16386.
    [52] Desai PS (2017) Thermodynamics of Azardirachta indica (Neem) leaves ark's as corrosion inhibitors for aluminum in HCl. Int J ChemTech Res 10: 131–138.
    [53] Vashi RT, Prajapati NI (2020) Cuminum cyminum (Jeeru) seeds extract as eco-friendly corrosion inhibitor for aluminium in HCl solution. IJRASET 8: 1708–1715.
    [54] Desai PS (2018) Calotropis gigantea leaves ark used as corrosion inhibitor for aluminum in hydrochloric acid. Der Pharma Chem 10: 7–12.
    [55] Al-Turkustani AM, Arab ST, Al-Dahiri RH (2010) Aloe plant extract as environmentally friendly inhibitor on the corrosion of aluminum in hydrochloric acid in absence and presence of iodide ions. Mod Appl Sci 4: 105–124. https://doi.org/10.5539/mas.v4n5p105 doi: 10.5539/mas.v4n5p105
    [56] James AO, Odey FA (2015) Inhibitive effect of bitter kola extract on aluminium corrosion in hydrochloric acid medium. IJSER 6: 996–1004.
    [57] Ayeni FA, Alawode S, Joseph D, et al. (2014) Investigation of Sida acuta (wire weed) plant extract as corrosion inhibitor for aluminium-copper-magnessium Alloy in acidic medium. J Miner Mater Charact Eng 2: 286–291. https://doi.org/10.4236/jmmce.2014.24033 doi: 10.4236/jmmce.2014.24033
    [58] Nithya A, Santhy P, Vijaya N, et al. (2015) Inhibition of corrosion of aluminium by an aqueous extract of beetroot (betanin). Int J Nano Corr Sci Engg 2: 1–11.
    [59] Raghavendra N, Ishwara Bhat J (2018) An environmentally friendly approach towards mitigation of al corrosion in hydrochloric acid by yellow colour ripe arecanut husk extract: Introducing potential and sustainable inhibitor for material protection. J Bio Tribo Corros 4: 2. https://doi.org/10.1007/s40735-017-0112-1 doi: 10.1007/s40735-017-0112-1
    [60] Obi-Egbedi NO, Obot IB, Umoren SA (2012) Spondias mombin L. as a green corrosion inhibitor for aluminium in sulphuric acid: Correlation between inhibitive effect and electronic properties of extracts major constituents using density functional theory. Arab J Chem 5: 361–373. http://doi.org/10.1016%2Fj.arabjc.2010.09.002
    [61] Pushpanjali, Rao SA, Rao P (2017) Corrosion inhibition and adsorption behavior of Murraya koenigii extract for corrosion control of aluminum in hydrochloric acid medium. Surf Engin Appl Electrochem 53: 475–485. https://doi.org/10.3103/S1068375517050088 doi: 10.3103/S1068375517050088
    [62] Nwosu FO, Owate IO, Osarolube E (2018) Acidic corrosion inhibition mechanism of aluminum alloy using green inhibitors. Am J Mater Sci 8: 45–50. https://doi.org/10.5923/j.materials.20180803.01 doi: 10.5923/j.materials.20180803.01
    [63] Nwosu OF, Osarolube E, Nnanna LA, et al. (2014) Acidic corrosion inhibition of Piper guineense seed extract on Al alloy. Am J Mater Sci 4: 178–183. https://doi.org/10.5923/j.materials.20140404.04 doi: 10.5923/j.materials.20140404.04
    [64] Kasuga B, Park E, Machunda RL (2018) Inhibition of aluminium corrosion using Carica papaya leaves extract in sulphuric acid. J Miner Mater Charact Eng 6: 1–14. https://doi.org/10.4236/jmmce.2018.61001 doi: 10.4236/jmmce.2018.61001
    [65] Chaubey N, Singh VK, Quraishi MA (2018) Papaya peel extract as potential corrosion inhibitor for Aluminium alloy in 1 M HCl: Electrochemical and quantum chemical study. Ain Shams Eng J 9: 1131–1140. https://doi.org/10.1016/j.asej.2016.04.010 doi: 10.1016/j.asej.2016.04.010
    [66] Mejeha IM, Nwandu MC, Okeoma KB, et al. (2012) Experimental and theoretical assessment of the inhibiting action of Aspilia africana extract on corrosion aluminium alloy AA3003 in hydrochloric acid. J Mater Sci 47: 2559–2572. https://doi.org/10.1007/s10853-011-6079-2 doi: 10.1007/s10853-011-6079-2
    [67] Zaidi Mat Satar M, Farhan Mohd Noor M, Wahid Samsudin M, et al. (2012) Orrosion inhibition of aluminum by using Nipah (Nypa fruticans) extract solutions in hydrochloric acid (HCl) media. Int J Electrochem Sci 7: 1958–1967.
    [68] Okeke PI, Maduka OG, Emeronye RU, et al. (2015) Corrosion inhibition efficacy of Cninodosculus chayamansa extracts on aluminum metal in acidic and alkaline media. Int J Sci Technol 3: 227–234.
    [69] Molina-Ocampo LB, Valladares-Cisneros MG, Gonzalez-Rodriguez JG (2015) Using Hibiscus sabdariffa as corrosin inhibitor for Al in 0.5 M H2SO4. Int J Electrochem Sci 10: 388–403.
    [70] Ajanaku KO, Aladesuyi O, Ajanaku CO, et al. (2015) Adsorption properties of Azadirachta indicia extract on corrosion of Aluminium in 1.85 M Hydrochloric acid. JIAATS 16: 4.
    [71] Wisdom J, Kelechi D, Ugochukwu J (2018) Green inhibitor for corrosion of Aluminium alloy AA8011A in acidic environment. IRJET 5: 121–124.
    [72] Omotosho OA, Ajayi OO (2012) Investigating the acid failure of aluminium alloy in 2 M hydrochloric acid using Vernonia amygdalina. ITB J Eng Sci 44: 77–92. https://doi.org/10.5614/itbj.eng.sci.2012.44.1.6 doi: 10.5614/itbj.eng.sci.2012.44.1.6
    [73] Loto CA, Joseph OO, Loto RT (2014) Adsorption and inhibitive properties of Camellia sinensis for aluminium alloy in HCl. Int J Electrochem Sci 9: 33637–3649.
    [74] Olawale O, Ogunsemi BT, Agboola OO, et al. (2018) Inhibition effect of orange seed extract on aluminium corrosion in 1M hydrochloric acid solution. IJMET 9: 282–287.
    [75] Vashi RT, Prajapati NI (2019) Corrosion inhibition of aluminium in hydrochloric acid solutions by Azadirachta indica (Neem) leaves extract as green inhibitor. IJGHC 8: 444–452. https://doi.org/10.24214/ijghc/gc/8/2/44452 doi: 10.24214/ijghc/gc/8/2/44452
    [76] Anbarasi CM, Divya G (2017) A green approach to corrosion inhibition of aluminum in acid medium using Azwain seed extract. Mater Today: Proc 4: 5190–5200. https://doi.org/10.1016/j.matpr.2017.05.026 doi: 10.1016/j.matpr.2017.05.026
    [77] Pushpanjali, Rao SA, Rao P (2016) Inhibition study of Andrographis paniculata plants extract for the corrosion control of Aluminum in hydrochloric acid medium. Int J ChemTech Res 9: 291–304.
    [78] Al-Turkustani AM, Al-Solmi MM (2011) Corrosion inhibition of aluminum in acidic solution by aqueous extract of Ajowan plant as green inhibitor. J Asian Sci Res 1: 346–358.
    [79] Al-Bataineh N, Al-Qudah MA, Abu-Orabi S, et al. (2022) Use of Capparis decidua extract as a green inhibitor for pure aluminum corrosion in acidc media. Corros Sci Technol 21: 9–20. https://doi.org/10.14773/cst.2022.21.1.9 doi: 10.14773/cst.2022.21.1.9
    [80] Deng SD, Li XH (2012) Inhibition by Jasminum Nudiflorum Lindl. leaves extract of the corrosion of aluminum in HCl solution. Corros Sci 64: 253–262. https://doi.org/10.1016/j.corsci.2012.07.017 doi: 10.1016/j.corsci.2012.07.017
    [81] Li XH, Deng SD (2012) Inhibition effect of Dendrocalamus brandisii leaves extract on aluminum in HCl, H3PO4 solutions. Corros Sci 65: 299–308. https://doi.org/10.1016/j.corsci.2012.08.033 doi: 10.1016/j.corsci.2012.08.033
    [82] Krishnaveni K, Ravichandran J (2014) Effect of aqueous extract of leaves of Morinda tinctoria on corrosion inhibition of aluminium surface in HCl medium. T Nonferr Metal Soc 24: 2704–2712. https://doi.org/10.1016/S1003-6326(14)63401-4 doi: 10.1016/S1003-6326(14)63401-4
    [83] Khadraoui A, Khelifa A, Hachama K, et al. (2016) Thymus algeriensis extract as a new eco-friendly corrosion inhibitor for 2024 aluminium alloy in 1 M HCl medium. J Mol Liq 214: 293–297. https://doi.org/10.1016/j.molliq.2015.12.064 doi: 10.1016/j.molliq.2015.12.064
    [84] Shah NK, Ladha DG, Wadhwani PM, et al. (2016) Corrosion inhibition performance of Coriander Seeds extract molecules for pure Aluminum in Hydrochloric acid Medium: A combined Experimental and Quantum chemical approach. Res J Recent Sci 5: 27–34.
    [85] Bashir S, Singh G, Kumar A (2017) Shatavari (Asparagus Racemosus) as green corrosion inhibitor of aluminium in acidic medium. J Mater Environ Sci 8: 4284–4291. http://doi.org/10.26872/jmes.2017.8.12.451 doi: 10.26872/jmes.2017.8.12.451
    [86] Ayuba AM, Abdullateef A (2021) Investigating the corrosion inhibition potentials of Strichnos spinosa L. extract on aluminium in 0.3 M hydrochloric acid solution. JASES 4: 336–348. https://doi.org/10.48393/IMIST.PRSM/jases-v4i1.24275 doi: 10.48393/IMIST.PRSM/jases-v4i1.24275
    [87] Petchiammal A, Selvaraj S (2013) Influence of Cassia alata leaves on aluminium in 1.0 N hydrochloric acid. CJST 1: 123–130.
    [88] Sharma A, Choudhary G, Sharma A, et al. (2013) Effect of temperature on inhibitory efficacy of Azadirachta indica fruit on acid corrosion of aluminium. IJIRSET 2: 7982–7992.
    [89] Fouda AEAS, Haleem EA (2020) Tussilago Farfara Extract (TFE) as green corrosion inhibitor for aluminum in hydrochloric acid solution. Biointerface Res App 10: 7023–7041. https://doi.org/10.33263/BRIAC106.70237041 doi: 10.33263/BRIAC106.70237041
    [90] Fouda AEAS, Rashwan SM, Kamel MM, et al. (2020) Juglans Regia Extract (JRE) as eco-friendly inhibitor for aluminum metal in hydrochloric acid medium. Biointerface Res App 10: 6398–6416. https://doi.org/10.33263/BRIAC105.63986416 doi: 10.33263/BRIAC105.63986416
    [91] Sharma S, Parihar PS, Nair RN, et al. (2012) Influence of ion-additives on inhibitory action of extract of Trigonella foenum graceums seeds for AA6063 in acid medium. RJC 5: 16–23.
    [92] Fouda AE-AS, Etaiw SH, Hammouda M (2017) Corrosion inhibition of aluminum in 1 M H2SO4 by Tecoma non-aqueous extract. J Bio Tribo Corros 3: 29. https://doi.org/10.1007/s40735-017-0090-3 doi: 10.1007/s40735-017-0090-3
    [93] El-Katori EE, Al-Mhyawi S (2019) Assessment of the Bassia muricata extract as a green corrosion inhibitor for aluminum in acidic solution. Green Chem Lett Rev 12: 31–48. https://doi.org/10.1080/17518253.2019.1569728. doi: 10.1080/17518253.2019.1569728
    [94] Onen AI, Barminas JT, Jacob J (2013) Inhibitory action of Ficus carica extracts on aluminium corrosion in acidic medium. Chem Sci Trans 2: 1326–1333. https://doi.org/10.7598/cst2013.558 doi: 10.7598/cst2013.558
    [95] Yadav S, Choudhary G, Sharma A (2013) Green Approach to Corrosion Inhibition of aluminium and copper by Ziziphus mauritiana fruit extract in hydrochloric acid solution. Int J ChemTech Res 5: 1815–1823.
    [96] BinYehmed FM, Abdullah AM, Zainal Z, et al. (2018) Green coffee bean extract as a green corrosion inhibitor for aluminium in artificial acid rain medium. Int J Appl Environ Sci 13: 171–183.
    [97] Orie KJ, Christian M (2015) The corrosion inhibition of aluminium metal in 0.5 M sulphuric acid using extract of breadfruit peels. IRJET 2: 1–9.
    [98] Garamon SE (2020) Ziziphus jujube as an eco-friendly organic inhibitor of aluminum corrosion in acidic medium. IJGHC 9: 9–15. https://doi.org/10.24214/ijghc/gc/9/1/00915 doi: 10.24214/ijghc/gc/9/1/00915
    [99] Ali AI, Foaud N (2012) Inhibition of aluminum corrosion in hydrochloric acid solution using black mulberry extract. J Mater Environ Sci 3: 917–924.
    [100] Alinnor IJ, Ukiwe LN (2012) Evaluation of inhibitory effect of different extracts of Vernonia amygdalina on corrosion of aluminium in hydrochloric acid. IJGHC 1: 120–132.
    [101] Alinnor IJ, Ejikeme PM (2012) Corrosion inhibition of aluminum in acidic medium by different extracts of Ocimum gratissimum. Chem Sci Int J 2: 122–135. https://doi.org/10.9734/ACSJ/2012/1835 doi: 10.9734/ACSJ/2012/1835
    [102] Dubey J, Jeengar N, Upadhyay R, et al. (2012) Corrosion inhibitive effects of Withania Somnifera (A medicinal plant) on aluminium in HCl solution. Res J Recent Sci 1: 73–78.
    [103] Kumpawat N, Chaturvedi A, Upadhyay RK (2012) Study on corrosion inhibition efficiency of stem alkaloid extract of different varieties of holy basil on aluminium in HCl solution. J Korean Chem Soc 56: 401–405. https://doi.org/10.5012/jkcs.2012.56.4.401. doi: 10.5012/jkcs.2012.56.4.401
    [104] Umoren SA, Eduok UM, Israel AU, et al. (2012) Coconut coir dust extract: A novel eco-friendly corrosion inhibitor for Al in HCl solutions. Green Chem Lett Rev 5: 303–313. https://doi.org/10.1080/17518253.2011.625980 doi: 10.1080/17518253.2011.625980
    [105] Ladha DG, Naik UJ, Shah NK (2013) Investigation of Cumin (Cuminum Cyminum) extract as an eco-friendly green corrosion inhibitor for pure Aluminium in acid medium. J Mater Environ Sci 4: 701–708. https://www.researchgate.net/publication/279515589
    [106] Petchiammal A, Selvaraj S (2013) Investigation of anti-corrosive effects of Lebbeck seed extract on aluminum in acid environment. Pac J Sci Technol 14: 31–39.
    [107] Adejo SO, Yiase SG, Ahile UJ, et al. (2013) Inhibitory effect and adsorption parameters of extract of leaves of Portulaca oleracea of corrosion of aluminium in H2SO4 solution. Arch Appl Sci Res 5: 25–32. https://www.researchgate.net/publication/274064097
    [108] Adejo SO, Gbertyo JA, Ahile JU (2013) Inhibitive properties and adsorption consideration of ethanol extract of Manihot esculentum leaves for corrosion inhibition of aluminium in 2 M H2SO4. Int J Mod Chem 4: 137–146.
    [109] Adejo SO, Gbertyo JA, Ahile JU, et al. (2013) Manihot esculentum root peels ethanol extract as corrosion inhibitor of aluminium in 2 M H2SO4. Int J Sci Eng Res 4: 2308–2313.
    [110] Shalabi K, Fouda AS, Elewady GY, et al. (2014) Adsorption and inhibitive properties of Phoenix dactylifera L. Extract as a green inhibitor for aluminum and aluminum-silicon alloy in HCl. Prot Met Phys Chem Surf 50: 420–431. https://doi.org/10.1134/S2070205114030174 doi: 10.1134/S2070205114030174
    [111] Al-Mhyawi SR (2014) Corrosion inhibition of aluminum in 0.5 M HCl by Garlic aqueous extract. Orient J Chem 30: 541–552. https://doi.org/10.13005/ojc/300218 doi: 10.13005/ojc/300218
    [112] Nair RN, Monika, Choudhary G, et al. (2014) Effect of elevation in temperature on inhibitory action of Murraya koenigii leaves on acid corrosion of AA6063. Int J Adv Sci Tech Res 4: 975–989.
    [113] Yiase SG, Adejo S, Ahile UJ, et al. (2014) Thermodynamic, kinetic and adsorptive parameters of corrosion inhibition of aluminium using Sorghum bicolor leaf extract in H2SO4. IJARCS 1: 38–46.
    [114] Dominic OO, Chikaodili AV, Sandra OC (2020) Optimum prediction for inhibition efficiency of Sapium ellipticum leaf extract as corrosion inhibitor of aluminum alloy (AA3003) in hydrochloric acid solution using electrochemical impedance spectroscopy and response surface methodology. Bull Chem Soc Ethiop 34: 175–191. https://doi.org/10.4314/BCSE.V34I1.17 doi: 10.4314/BCSE.V34I1.17
    [115] Abakedi OU, Asuquo JE (2016) Corrosion inhibition of aluminium in acidic medium by ethanol leaf extract of Azadirachta indica. JBAAR 2: 556–560.
    [116] Njoku DI, Ukaga I, Ikenna OB, et al. (2016) Natural products for materials protection: Corrosion protection of aluminium in hydrochloric acid by Kola nitida extract. J Mol Liq 219: 417–424. https://doi.org/10.1016/j.molliq.2016.03.049 doi: 10.1016/j.molliq.2016.03.049
    [117] Njoku DI, Onuoha GN, Oguzie EE, et al. (2019) Nicotiana tabacum leaf extract protects aluminium alloy AA3003 from acid attack. Arabian J Chem 12: 4466–4478. https://doi.org/10.1016/j.arabjc.2016.07.017 doi: 10.1016/j.arabjc.2016.07.017
    [118] Omotioma M, Onukwuli OD (2017) Evaluation of pawpaw leaves extract as anti-corrosion agent for aluminium in hydrochloric acid medium. NIJOTECH 36: 496–504. https://doi.org/10.4314/njt.v36i2.24 doi: 10.4314/njt.v36i2.24
    [119] Ejikeme PM, Umana SG, Alinnor IJ, et al. (2014) Corrosion inhibition and adsorption characteristics of Jatropha curcas leaves extract on aluminium in 1M HCl. Am J Mater Sci 4: 194–201.
    [120] Raghavendra NJ, Bhat Ishwara (2017). An experimental approach towards anticorrosive potential of Areca fat species at aluminum/test solution (HCl/NaOH) interface. Int J ChemTech Res 10: 1003–1013.
    [121] Djemoui A, Souli L, Djemoui D, et al. (2017) Alkaloids extract from Peganum harmala plant as corrosion inhibitor of 6063 aluminium alloy in 1 M hydrochloric acid medium. J Chem Pharm Res 9: 311–318.
    [122] Iloamaeke IM, Umeobika CU, Egwuatu CI, et al. (2017) Corrosion inhibition and adsorption behaviour of aluminium in 1M H2SO4 meduim using Persea americana (avocado pear) leaves extract. J Basic Phys Res 7: 47–57.
    [123] Nnabuka EO, Awe F (2018) Experimental and quantum chemical studies on ethanol extract of Phyllanthus amarus (EEPA) as a green corrosion inhibitor for aluminum in 1 M HCl. Port Electrochim Acta 36: 231–247. https://doi.org/10.4152/pea.201804231 doi: 10.4152/pea.201804231
    [124] Suleiman IY, Abdulwahab M, Sirajo MZ (2018) Anti-corrosion properties of ethanol extract of Acacia senegalensis stem on Al-Si-Fe/SiC composite in sulfuric acid medium. J Fail Anal Preven 18: 212–220. https://doi.org/10.1007/s11668-018-0399-3 doi: 10.1007/s11668-018-0399-3
    [125] Sharma S, Sharma YC (2019) Cordia dichotoma as corrosion inhibitor for aluminum alloy (AA6063) in hydrochloric acid. Port Electrochim Acta 37: 1–22. https://doi.org/10.4152/pea.201901001 doi: 10.4152/pea.201901001
    [126] Chung I-M, Malathy R, Kim S-H, et al. (2020) Ecofriendly green inhibitor from Hemerocallis fulva against aluminum corrosion in sulphuric acid medium. J Adhes Sci Technol 34: 1483–1506. https://doi.org/10.1080/01694243.2020.1712770 doi: 10.1080/01694243.2020.1712770
    [127] Meena O, Chaturvedi A, Meena C (2019) Corrosion inhibition effect of aerial parts of Euphorbia neriifolia linn on aluminium in nitric acid solution. IJGHC 8: 801–814. https://doi.org/10.24214/ijghc/gc/8/4/80114 doi: 10.24214/ijghc/gc/8/4/80114
    [128] Meena OP, Nainawat AK, Chaturvedi A (2019) Corrosion inhibition of aluminium by alkaloid extract of aerial part of Euphorbia neriifolia Linn in HCl solutions. Int J ChemTech Res 12: 234–242. https://doi.org/10.20902/ijctr.2019.120231 doi: 10.20902/ijctr.2019.120231
    [129] Prajapati NI, Vashi RT, Desai SA (2020) Fennel (Foeniculum vulgare Mill) seeds extract as green inhibitor for aluminium corrosion in HCl acid solution: Thermodynamic, adsorption and kinetic study. Eur J Biomed Pharm Sci 7: 421–428.
    [130] Thacker H, Ram V (2021) Phoenix Dactylifera L. extracts as green corrosion inhibitor for aluminum in acidic medium. J Sci Res 65: 142–149. https://doi.org/10.37398/jsr.2021.650317 doi: 10.37398/jsr.2021.650317
    [131] Meena AK, Meena AK, Sahay Bairwa B (2021) Inhibitory efficacy of Capparis decidua extract on the corrosion of aluminium in various acidic media. J Adv Sci Res 12: 254–261. https://doi.org/10.55218/ASR.s12021121sup206 doi: 10.55218/ASR.s12021121sup206
    [132] Ejikeme PM, Umana SG, Onukwuli OD (2013) Corrosion inhibition of aluminium by Treculia Africana leaves extract in acid medium. Port Electrochim Acta 30: 317–328. https://doi.org/10.4152/pea.201205317 doi: 10.4152/pea.201205317
    [133] Nathiya RS, Raj V (2017) Evaluation of Dryopteris cochleata leaf extracts as green inhibitor for corrosion of aluminium in 1 M H2SO4. Egypt J Pet 26: 313–323. https://doi.org/10.1016/j.ejpe.2016.05.002 doi: 10.1016/j.ejpe.2016.05.002
    [134] Madueke NA, Iroha NB (2018) Protecting aluminium alloy AA8011 from acid corrosion using extract from Allamanda cathartica leaves. IJIRSET 7: 10251–10258. https://doi.org/10.15680/IJIRSET.2018.0710014 doi: 10.15680/IJIRSET.2018.0710014
    [135] Madufor IC, Itodoh UE, Obidiegwu MU, et al. (2012) Inhibition of aluminium corrosion in acidic medium by Chrysophyllum albidum (African star apple) fruit extract. IOSRJEN 2: 16–23. http://doi.org/10.9790/3021-02951623 doi: 10.9790/3021-02951623
    [136] Ramírez-Arteaga M, Valladares MG, González Rodríguez JG (2013) Use of Prosopis laevigata as a corrosion inhibitor for Al in H2SO4. Int J Electrochem Sci 8: 6864–6877.
    [137] Petchiammal A, Selvaraj S (2013) The corrosion control of aluminium using Lawsonia inermis seed extract in acid medium. Int J ChemTech Res 5: 1566–1574.
    [138] Ayuba AM, Mustapha AA (2013) Inhibitive and synergistic properties of ethanolic extract of anogeissus leiocarpus leaves on the corrosion of aluminium in HCl solution. ChemSearch J 4: 55–65.
    [139] Njoku CN, Onyelucheya OE (2015) Response surface optimization of the inhibition efficiency of Gongronema latifolium as an inhibitor for aluminum corrosion in HCl solutions. Int J Mater Chem 5: 4–13. http://doi.org/10.5923/j.ijmc.20150501.02 doi: 10.5923/j.ijmc.20150501.02
    [140] Prabakaran M, Kim SH, Sasireka A, et al. (2018) Polygonatum odaratum extract as an eco-friendly inhibitor for aluminum corrosion in acidic medium. J Adhes Sci Technol 32: 2054–2069. https://doi.org/10.1080/01694243.2018.1462947 doi: 10.1080/01694243.2018.1462947
    [141] Abegunde SM, Ogede RO, Oluwagbenga Fatile B, et al. (2018) Talinum triangulare leaf and Musa sapientum peel extracts as corrosion inhibitors on ZA-27 alloy. J Part Sci Technol 4: 23–28. https://doi.org/10.22104/JPST.2018.2889.1123 doi: 10.22104/JPST.2018.2889.1123
    [142] Ciobotaru I-E, Benga F-M, Budei D (2020) The enhancement of the corrosion resistance of aluminium and VTES-coated aluminium by using a green inhibitor. UPB Sci Bull, Ser B 82: 179–186.
    [143] Manwani N, Upadhyay RK (2019) A comperative study of inhibition efficiency of extract of leaves and stem of Solanum xanthocarpum on aluminium in 2N HCl solution. IJERT 8: 472–475.
    [144] Méndez-Figueroa HG, Ossandón S, Fernández JAR, et al. (2022) Electrochemical evaluation of an Acanthocereus tetragonus aqueous extract on aluminum in NaCl (0.6 M) and HCl (1 M) and its modelling using forward and inverse artificial neural networks. J Electroanal Chem 918: 116444. https://doi.org/10.1016/j.jelechem.2022.116444 doi: 10.1016/j.jelechem.2022.116444
    [145] Skerget M, Kenz Z, Kenz-Hrncic M (2011) Solubility of solids in sub- and supercritical fluids: A review. J Chem Eng Data 56: 694–714. https://doi.org/10.1021/je1011373 doi: 10.1021/je1011373
    [146] Xhanari K, Finšgar M, Knez Hrnčič M, et al. (2017) Green corrosion inhibitors for aluminium and its alloys: A review. RSC Adv 7: 27299. https://doi.org/10.1039/c7ra03944a doi: 10.1039/c7ra03944a
    [147] Welton T (2015) Solvents and sustainable chemistry. Proc R Soc A 471: 20150502. http://doi.org/10.1098/rspa.2015.0502 doi: 10.1098/rspa.2015.0502
    [148] Maayta AK, Al-Rawashdeh NAF (2004) Inhibition of acidic corrosion of pure aluminum by some organic compounds. Corros Sci 46: 1129–1140. https://doi.org/10.1016/j.corsci.2003.09.009 doi: 10.1016/j.corsci.2003.09.009
    [149] Tsuru T, Haruyama S, Gijutsu B (1978) Corrosion inhibition of iron by amphoteric surfactants in 2M HCl. J Jpn Soc Corros Eng 27: 573–581.
    [150] Adejo SO, Ekwenchi MM, et al. (2014) Adsorption characteristics of ethanol root extract of Portulaca oleracea as eco-friendly inhibitor of corrosion of mild steel in H2SO4 medium. IOSR-JAC 7: 55–60. https://doi.org/10.9790/5736-07415560 doi: 10.9790/5736-07415560
    [151] Eddy NO, Ebenso EE (2008) Adsorption and inhibitive properties of ethanol extracts of Musa sapientum peels as a green corrosion inhibitor for mild steel in H2SO4. Afr J Pure Appl Chem 2: 46–54.
    [152] Obot IB, Obi-Egbedi NO, Umoren SA, et al. (2010) Synergistic and antagonistic effects of anions and Ipomoea invulcrata as green corrosion inhibitor for aluminium dissolution in acidic medium. Int J Electrochem Sci 5: 994–1007.
    [153] Ebenso EE (2003) Effect of halide ions on the corrosion inhibition of mild steel in H2SO4 using methyl red: Part 1. Bull Electrochem 19: 209–216.
    [154] Ating EI, Umoren SA, Udousoro II, et al. (2010) Leaves extract of Ananas sativum as green corrosion inhibitor for aluminium in hydrochloric acid solutions. Green Chem Lett Rev 3: 61–68. https://doi.org/10.1080/17518250903505253 doi: 10.1080/17518250903505253
    [155] Dehri I, Ozcan M (2006) The effect of temperature on the corrosion of mild steel in acidic media in the presence of some sulphur-containing organic compounds. Mater Chem Phys 98: 316–323. https://doi.org/10.1016/j.matchemphys.2005.09.020 doi: 10.1016/j.matchemphys.2005.09.020
    [156] Singh MR, Bhrara K, Singh G (2008) The inhibitory effect of diethanolamine on corrosion of mild steel in 0.5 M sulphuric acidic medium. Port Electrochim Acta 26: 479–492.
    [157] Agrawal D, Gupta KD, Saxena KK (2003) Thermodynamics and equilibrium study of the formation of binary and tertiary complexes of Co+2, Ni+2, Cu+2 and Zn+2 with (DL)-2, 5 Diamino -1-pentanoic acid as secondary ligand and 2', 2'-Biperidine (2', 2'-Bipy) as primary ligand. Trans SAEST 38: 111–114.
    [158] Issa RM, El-Sonbati AZ, El-Bindary AA, et al. (2008) Polymer complexes XXXIV. Potentiometric and thermodynamic studies of monomeric and polymeric complexes containing 2-acrylamidosulphadiazine. Eur Polym J 38: 561–566.
    [159] Khaled KF, Al-Qahtani MM (2009) The inhibitive effect of some tetrazole derivatives towards Al corrosion in acid solution: Chemical, electrochemical and theoretical studies. Mater Chem Phys 113: 150–158. https://doi.org/10.1016/j.matchemphys.2008.07.060 doi: 10.1016/j.matchemphys.2008.07.060
    [160] Amin MA, Mohsen Q, Hazzai OA (2009) Synergistic effect of I− ions on the corrosion inhibition of Al in 1.0 M phosphoric acid solutions by purine. Mater Chem Phys 114: 908–914. https://doi.org/10.1016/j.matchemphys.2008.10.057 doi: 10.1016/j.matchemphys.2008.10.057
    [161] Noor EA (2009) Evaluation of inhibitive action of some quaternary N-heterocyclic compounds on the corrosion of Al–Cu alloy in hydrochloric acid. Mater Chem Phys 114: 533–541. https://doi.org/10.1016/j.matchemphys.2008.09.065 doi: 10.1016/j.matchemphys.2008.09.065
    [162] Valand T, Heusler KE (1983) Reactions at the oxide-electrolyte interface of anodic oxide films on aluminum. J Electroanal Chem Interfacial Electrochem 149: 71–82. https://doi.org/10.1016/S0022-0728(83)80559-2 doi: 10.1016/S0022-0728(83)80559-2
    [163] Singh AK, Quraishi MA (2010) Effect of Cefazolin on the corrosion of mild steel in HCl solution. Corros Sci 52: 152–60. https://doi.org/10.1016/j.corsci.2009.08.050 doi: 10.1016/j.corsci.2009.08.050
    [164] Abd El Rehim SS, Hassan HH, Amin MA (2001) Corrosion inhibition of aluminum by 1, 1(lauryl amido)propyl ammonium chloride in HCl solution. Mater Chem Phys 70: 64–72. https://doi.org/10.1016/S0254-0584(00)00468-5 doi: 10.1016/S0254-0584(00)00468-5
    [165] Amin MA, Khaled KF, Mohsen Q, et al. (2010) A study of the inhibition of iron corrosion in HCl solutions by some amino acids. Corros Sci 52: 1684–1695. https://doi.org/10.1016/j.corsci.2010.01.019 doi: 10.1016/j.corsci.2010.01.019
    [166] McCafferty E, Hackerman N (1972) Double-layer capacitance of iron and corrosion inhibition with polymethylene diamines. J Electrochem Soc 119: 146. https://doi.org/10.1149/1.2404150 doi: 10.1149/1.2404150
    [167] Umoren SA, Solomon MM (2015) Effect of halide ions on the corrosion inhibition efficiency of different organic species–A review. J Ind Eng Chem 21: 81–100. https://doi.org/10.1016/j.jiec.2014.09.033 doi: 10.1016/j.jiec.2014.09.033
    [168] Umoren SA, Obot IB, Ebenso EE (2008) Corrosion inhibition of aluminium using exudate gum from Pachylobus edulis in the presence of halide ions in HCl. J Chem 5: 138407. https://doi.org/10.1155/2008/138407 doi: 10.1155/2008/138407
    [169] Umoren SA, Ekanem UF (2010) Inhibition of mild steel corrosion in H2SO4 using exudate gum from Pachylobus edulis and synergistic potassium halide additives. Chem Eng Commun 197: 1339–1356. https://doi.org/10.1080/00986441003626086 doi: 10.1080/00986441003626086

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