Evaluation of analytical greenness metric for an eco-friendly method developed through the integration of green chemistry and quality-by-design for the simultaneous determination of Nebivolol hydrochloride, Telmisartan, Valsartan, and Amlodipine besylate.

In recent years, the field of analytical chemistry has witnessed a notable shift towards the adoption of greener chromatographic methods, aiming to minimize the environmental impact. An effective strategy involves substituting conventional harmful organic solvents with environmentally friendly alternatives, reducing the use of hazardous chemicals that contribute to environmental concerns. However, separating drug substances without the use of buffers and organic solvents presence is a big challenge. To overcome this challenge, a combination of quality-by-design (QbD) and green analytical chemistry (GAC) was employed in this study for method development. A high-performance liquid chromatography (HPLC) method was successfully developed and validated for the simultaneous determination of Nebivolol hydrochloride, Telmisartan, Valsartan, and Amlodipine besylate. The method utilized a mobile phase composed of a mixture of 0.1 % formic acid in water (pH: 2.5) and ethanol. A regular octadecyl silica (ODS) column was employed, and UV detection at 220 nm was utilized. The method exhibited linearity within the concentration range of 25-75 µg/mL for Telmisartan and 150-450 µg/mL for Nebivolol Hydrochloride, Valsartan, and Amlodipine besylate and the correlation coefficient was greater than 0.999 for all the analytes. Limits of detection (LOD) and quantification (LOQ) were determined as 0.01 and 0.04 µg/mL for Telmisartan, 0.06 and 0.20 µg/mL for Nebivolol Hydrochloride, 0.08 and 0.25 µg/mL for Amlodipine besylate, and 0.14 and 0.46 µg/mL for Valsartan, respectively. The developed method underwent thorough validation, encompassing various parameters such as linearity, accuracy, precision, LOD, LOQ, robustness, and ruggedness. The mean recovery values were observed to range between 98.86 % and 99.89 %. The accuracy demonstrated was consistently above 98.98 % for both intra-day and inter-day precisions were with the relative standard deviations less than 2 %. To establish its robustness, a quality-by-design-based experimental design (DoE) approach was implemented. Additionally, the method's environmental friendliness was evaluated using the Analytical Greenness metric (AGREE) an analytical eco scale, both confirming its alignment with sustainable practices and reduced ecological impact. The sustainability of the solvent used in the current study was evaluated by Green Solvents Selecting Tool (GSST) Further, the developed method greenness was evaluated with the green analytical tools such as Analytical method greenness score (AMGS) and using the recently released White Analytical Chemistry (WAC) using RGB assessment tool. By employing this greener approach to chromatography method, this study contributes to the ongoing efforts in analytical chemistry to promote sustainable practices and minimize the environmental footprint of analytical methods.

Lateral flow assays: Progress and evolution of recent trends in point-of-care applications.

Paper based point-of-care (PoC) detection platforms applying lateral flow assays (LFAs) have gained paramount approval in the diagnostic domain as well as in environmental applications owing to their ease of utility, low cost, and rapid signal readout. It has centralized the aspect of self-evaluation exhibiting promising potential in the last global pandemic era of Covid-19 implementing rapid management of public health in remote areas. In this perspective, the present review is focused towards landscaping the current framework of LFAs along with integration of components and characteristics for improving the assay by pushing the detection limits. The review highlights the synergistic aspects of assay designing, sample enrichment strategies, novel nanomaterials-based signal transducers, and high-end analytical techniques that contribute significantly towards sensitivity and specificity enhancement. Various recent studies are discussed supporting the innovations in LFA systems that focus upon the accuracy and reliability of rapid PoC testing. The review also provides a comprehensive overview of all the possible difficulties in commercialization of LFAs subjecting its applicability to pathogen surveillance, water and food testing, disease diagnostics, as well as to agriculture and environmental issues.

Validated green and white RP-HPLC method for the estimation of Zolmitriptan in marketed dosage form.

OBJECTIVE: - To develop and validate a rapid, accurate, economical, effective and greenery RP-HPLC method for the determination of Zolmitriptan in tablet dosage form. MATERIAL AND METHOD: - RP-HPLC method was developed using Luna (C18) (4.6 x 250 mm, 5 µm) column and Sodium phosphate buffer (pH 4.7): Methanol [75: 25, v/v] was used as mobile phase at a flow rate of 1.0 mL/min. The detection was carried out at 227 nm. Further, eco-friendliness, productivity and performance of the optimized analytical method were assessed by green and white tools. RESULTS: - The retention time of Zolmitriptan was found to be 3.25 min with acceptable chromatographic parameters. The optimized RP-HPLC method was more eco-friendly, efficient, throughput and practicable than the reported methods as confirmed by AES, AGREE, GAPI and RGB tools. Further, the proposed analytical method showed all the validation parameters within the acceptance limit of ICH Q2 R1 guidelines. The linear regression analysis indicated a good linear response in the 10 to 120 µg/mL concentration range with R2 of 0.99998. The percentage content and percentage assay of Zolmitriptan in Zomig-5mg tablet was found to be 103.36 ± 0.356 % and 97.86 ± 0.693 %. CONCLUSION: - The developed and validated method has several advantages compared to the reported HPLC methods and is useful in the systematic analysis of Zolmitriptan in its dosage form.

Understudied and underestimated impacts of organic UV filters on terrestrial ecosystems.

Organic UV filters (OUVFs) are vital components in various personal care products (PCPs) and commercial goods, with the annual consumption estimated at 10,000 tons. Consequently, the unavoidable use of OUVFs in PCPs and other unregulated commercial applications could present a considerable risk to human and environmental health. These chemical entities enter terrestrial ecosystems through wastewater discharge, agriculture, atmospheric deposition, and recreational activities. Compared to aqueous ecosystems, the effects of OUVFs on terrestrial environments should be more studied and potentially underestimated. The present review addresses the abovementioned gap by summarizing 189 studies conducted between 2006 and 2024, focusing on the analytical measures, occurrence, and ecotoxicological effects of OUVFs on terrestrial ecosystems. These studies underscore the harmful effects of certain OUVFs on the development, reproduction, and endocrine systems of terrestrial organisms, highlighting the necessity for comprehensive toxicological assessments to understand their impacts on non-target species in terrestrial ecosystems. Besides, by underscoring the ecological effects of OUVFs, this review aims to guide future research and inform regulatory measures to mitigate the risks posed by these widespread contaminants. Meanwhile, interdisciplinary research is essential, integrating environmental science, toxicology, ecology, and chemistry to tackle OUVF challenges in terrestrial ecosystems.

Carboxy-PEG-thiol functionalized gold nanoparticle conjugates for the detection of SARS-CoV-2: detection tools and analytical method development.

Addressing the need for accessible SARS-CoV-2 testing, carboxy-PEG 12-thiol functionalized gold nanoparticles conjugates were developed for rapid point-of-care (POC) detection against SARS-CoV-2 spike protein, pseudo-SARS-CoV-2, and authentic Beta SARS-CoV-2 virus particles. These conjugates leverage gold nanoparticles (AuNPs) as signal transducers, cross-linked to either angiotensin-converting enzyme 2 (ACE2) or SARS-CoV-2 spike protein receptor-binding domain (RBD) antibodies as bioreceptors and showed a distinct color shift from pink to blue. To assess their POC feasibility, the conjugates were integrated into facemasks and breathalyzers, wherein aerosolized SARS-CoV-2 antigens were successfully detected, producing a color change within 10 and 30minutes for the breathalyzer and facemask prototypes, respectively. Furthermore, we explored quantitative analysis using varying concentrations of SARS-CoV-2 spike protein. Both conjugates demonstrated a linear relationship between blue color intensity and virus concentration, with linear ranges of 0.08-0.6ng/mL and 0.04-0.5ng/mL, respectively. Low limits of detection and quantification were also achieved. They exhibited specificity, responding solely to SARS-CoV-2 even in complex matrices containing diverse proteins, including the SARS-CoV-1 spike protein. Precision tests yielded coefficient of variations below 2%, showcasing their remarkable reproducibility. This work presents a promising approach for rapid, sensitive, and specific POC detection of SARS-CoV-2 paving the way for improved pandemic response and management.

Decoding stakeholder priorities of safety culture preferences in the oil and gas industry.

Safety culture is a critical determinant of organisational performance, particularly in high-risk industries especially in oil and gas. Understanding stakeholder preferences is essential for developing effective strategies that enhance safety culture. This study utilised the Analytic Hierarchy Process (AHP) to prioritise stakeholder preferences, identifying key elements of safety culture in Malaysia's oil and gas sector. This study employed a structured methodology to evaluate safety culture within the oil and gas industry, focusing on 18 sub-elements across three key domains: psychological, behavioural, and situational factors. A diverse sample of industry experts was recruited using purposeful and snowball sampling to ensure a comprehensive representation of stakeholder views. The AHP framework was applied to analyse the data, utilizing structured questionnaires and multicriteria decision-making techniques to prioritize the identified safety culture elements. The AHP analysis identified distinct priorities among different professional groups within the oil and gas sector. Safety and Health Practitioners emphasized practical elements such as safety rules and management commitment, while academicians prioritized knowledge and training. Management personnel highlighted the importance of safety ownership and communication, whereas policymakers focused on broader, policy-oriented aspects. The findings suggest that safety culture improvement initiatives should be tailored to address the specific needs and priorities of each professional group. A nuanced understanding of stakeholder preferences is crucial for developing comprehensive strategies that integrate observable behaviours, situational conditions, and psychological factors, ultimately fostering a robust safety culture in the oil and gas industry.

The forecasting of surface displacement for tunnel slopes utilizing the WD-IPSO-GRU model.

To quickly assess slope stability based on field displacement monitoring data, this paper constructs a hybrid optimization model that predicts surface displacement during tunnel excavation in base-overburden slopes. The model combines Wavelet Decomposition (WD) with a Gated Recurrent Unit (GRU), and the GRU's hyperparameters are optimized using an Improved Particle Swarm Optimization algorithm (IPSO). The specific steps are as follows: First, the Wavelet Decomposition (WD) technique is applied to decompose the raw displacement data, extracting features at different time-frequency scales. Next, the Dropout technique is incorporated into the GRU model to prevent overfitting. Additionally, nonlinear inertia weight ω improved cognitive factor c1, and social factor c2 are introduced. The PSO algorithm is improved by integrating crossover and mutation concepts from genetic algorithms. Finally, the IPSO is used to optimize the number of neural units hN, HN, LN and dropout rates D1 and D2 in the GRU network architecture. After constructing the WD-IPSO-GRU model, a comprehensive comparison is made with various swarm intelligence algorithms and state-of-the-art models. The experimental results demonstrate that the WD-IPSO-GRU model significantly improves the prediction accuracy of surface displacement in slopes during tunnel excavation. Compared to directly using raw data for prediction, the introduction of the WD preprocessing technique improved the prediction accuracy at measurement points 01 and 02 by 28% and 45.9%, respectively. Additionally, with the model optimized by IPSO, the prediction accuracy at measurement points 01 and 02 increased by 76% and 56.7%, respectively. The WD-IPSO-GRU model effectively addresses the challenges of extracting features from univariate displacement time-series data and determining the parameters of the GRU network. It improves the prediction accuracy of surface displacement in base-overburden type slopes and demonstrates excellent generalization ability and reliability. The research results validate the potential application of the model in geotechnical engineering and provide strong support for assessing slope stability during tunnel excavation.

Characterization of AAV vectors: A review of analytical techniques and critical quality attributes.

Standardized evaluation of adeno-associated virus (AAV) vector products for biotherapeutic application is essential to ensure the safety and efficacy of gene therapies. This includes analyzing the critical quality attributes of the product. However, many of the current analytical techniques used to assess these attributes have limitations, including low throughput, large sample requirements, poorly understood measurement variability, and lack of comparability between methods. To address these challenges, it is essential to establish higher-order reference methods that can be used for comparability measurements, optimization of current assays, and development of reference materials. Highly precise methods are necessary for measuring the empty/partial/full capsid ratios and the titer of AAV vectors. Additionally, it is important to develop methods for the measurement of less-established critical quality attributes, including post-translational modifications, capsid stoichiometry, and methylation profiles. By doing so, we can gain a better understanding of the influence of these attributes on the quality of the product. Moreover, quantification of impurities, such as host-cell proteins and DNA contaminants, is crucial for obtaining regulatory approval. The development and application of refined methodologies will be essential to thoroughly characterize AAV vectors by informing process development and facilitating the generation of reference materials for assay validation and calibration.

UPLC-MS/MS method for fluconazole determination in plasma and its application in Mexican patients with candidaemia.

Aim: An accurate and fast ultra-high performance liquid chromatography coupled with tandem mass spectrometry analytical method was developed and validated for quantifying fluconazole levels in human plasma according to the US FDA guidelines.Materials & methods: A simple protein precipitation by acetonitrile was employed for the sample preparation. The chromatographic separation was carried out using isocratic elution of water (0.1% formic acid) and acetonitrile on an Acquity ultra-high performance liquid chromatography HSS T3 column. Samples from ten adult patients diagnosed with candidemia who received fluconazole treatment were analyzed.Results & conclusion: The method demonstrated excellent linearity and stability within the 1-50 µg/ml range (r2 >0.999). The intraday and interday precision were determined with coefficient of variation values ranging from 1.4 to 4.38% and 2.8 to 6.6%, respectively. This rapid and selective method has successfully analyzed 27 plasma samples. The straightforward sample preparation in a single step and the reduced analysis time make this method suitable for adult patients with candidemia, leading to improved clinical outcomes.

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An Overview of the Analytical Methods for Products based on Clindamycin and Eco-efficiency Perspectives.

BACKGROUND: Clindamycin (CLIN), an antibiotic sold in the form of capsules, injectable solution, gel, and lotion, is easily soluble in water and ethanol. However, it lacks eco-efficient methods for evaluating pharmaceutical products. OBJECTIVE AND METHOD: The objective of this review is to provide an overview of the analytical methods present both in the literature and in official compendia for evaluating pharmaceutical matrices based on CLIN in the context of Green Analytical Chemistry (GAC). RESULTS: Firstly, microbiological methods for evaluating the potency of CLIN final products were not found, which already shows the need to develop new methods. Among the methods found, which are all physicalchemical, the most used method is HPLC (71%) followed by UV-Vis (14%). Among the targets of the methods, capsules and raw materials were the most studied (33% each). Among the choices of analytical conditions for the methods, acetonitrile is the preferred solvent (27.7%), even though CLIN is easily soluble in ethanol. CONCLUSION: Thus, the gap in eco-friendly and sustainable analytical methods is a reality and an opportunity for analytical development centers to provide means for evaluating the quality of CLIN-based products.

Rice sample preparation method for ICP-MS and CV-AFS analysis: Elemental levels and estimated intakes.

Eight sample digestion procedures were compared to determine 41 elements in rice samples by ICP-MS and CV-AFS. Analytical methods were evaluated using certified rice flour reference material (NIST 1568b) and recovery experiments. The microwave-assisted digestion of 0.5 g rice sample and reagent mixture of 2 mL HNO3, 0.5 mL H2O2, and 0.5 mL deionized water yielded the best recovery for all elements ranging from 90 to 120% at three different levels, bias% within 10%, and precision (coefficient of variation percent, CV% intra- and inter-day) below 15%. The best analytical method was applied to the elemental determination in nine types of rice available on the Italian market. Daily or weekly rice consumption meets the nutritional and safety requirements of EFSA and WHO. The present study allows extensive and detailed knowledge of the content of essential and non-essential/toxic elements in different types of rice produced or packaged in Italy.

Programming Fast DNA Amplifier Circuits with Versatile Toehold Exchange Pathway.

DNA amplifier circuits establish powerful tools to dynamically control molecular assembly for computation, sensing, and biological applications. However, the slow reaction speed remains a major barrier to their practical utility. Here, diverse fast DNA amplifier circuits termed toehold exchange polymerization (TEP) and toehold exchange catalysis (TEC) using toehold exchange-mediated assembly as a fundamental mechanism are built. Both TEP and TEC with a duplex and a hairpin can respond within minutes to diverse nucleic acid inputs with high fidelity. In addition, the circuits can amplify live-cell signals for fluorescence imaging target RNA dynamics and discriminating different cell lines. Compared with existing DNA circuits that involve time scales of hours for transducing small signals, TEP and TEC exhibit much faster dynamics, simpler design, and comparable sensitivity. These features make TEP and TEC promising platforms to develop programmable nucleic acid tools and devices and to create fast sensing and processing systems, amenable to wide practical applications.

Artificial intelligence and the politics of avoidance in global health.

For decades, global health actors have centered technology in their interventions. Today, artificial intelligence (AI) is emerging as the latest technology-based solution in global health. Yet, AI, like other technological interventions, is not a comprehensive solution to the fundamental determinants of global health inequities. This article gathers and critically appraises grey and peer-reviewed literature on AI in global health to explore the question: What is avoided when global health prioritizes technological solutions to problems with deep-seated political, economic, and commercial determinants? Our literature search and selection yielded 34 documents, which we analyzed to develop seven areas where AI both continues and disrupts past legacies of technological interventions in global health, with significant implications for health equity and human rights. By focusing on the power dynamics that underpin AI's expansion in global health, we situate it as the latest in a long line of technological interventions that avoids addressing the fundamental determinants of health inequities, albeit at times differently than its technology-based predecessors. We call this phenomenon the 'politics of avoidance.' We conclude with reflections on how the literature we reviewed engages with and recognizes the politics of avoidance and with suggestions for future research, practice, and advocacy.

Why nothing beats NIRS technology: The green analytical choice for the future sustainable food production.

In this perspective paper we argue for the fact that near infrared (NIR) technology, due to its unique properties, will become an indispensable green sensor technology in the future digitalized and sustainable food production. The future of near infrared spectroscopy (NIRS) in green analytics is bright. Ongoing advancements in NIR technology, coupled with increased accessibility and integration with advanced multivariate data analysis such as machine learning and artificial intelligence will further amplify the impact of NIRS across food, agricultural, environmental, and renewable energy domains. The miniaturization, increased portability, and enhanced affordability of NIR instruments, coupled with its integration into emerging technologies, will empower a diverse range of industries and researchers to address pressing global challenges with unprecedented precision and efficiency. The implementation of NIR technology in process analytical technology will enable the transition to future digitalized and sustainable food production. In a future circular economy, where waste streams, co-products and water are reclaimed and valorized, continuous measurements are necessary and in many cases, there are no sensor alternatives to NIR technology.

External quality assessment performance in ten countries: an IFCC global laboratory quality project.

OBJECTIVES: This study aimed to assess the validity of external quality assessment (EQA) laboratory results across various cultural and environmental contexts and to identify potential improvement areas. METHODS: The International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) Task Force on Global Laboratory Quality (TF-GLQ) conducted a 2-year study (2022 and 2023) in which EQA materials, related software and online training was provided by a commercial vendor to 100 laboratories in ten IFCC member society countries. The results were analysed on a monthly basis by the TF-GLQ, to show the number of submissions per country, tests per lab, acceptability rates, random failures and to get a measure of which analytes performed poorly. RESULTS: The EQA material was dispatched on a quarterly basis. Some countries had problems with customs releasing the material in a timely manner, resulting in laboratories not receiving them on time leading to no submission. We report here the results for the second year of the survey. The number of examinations varied between laboratories, ranging from seven to 84 analytes. Of the ten countries surveyed, six averaged greater than 90 % acceptable results over the whole 12-months cycle, one had unacceptable results for two of the nine months they returned results and the other four were considered to not perform to an acceptable standard. CONCLUSIONS: All 100 participating laboratories indicated satisfaction with the EQA survey and related services, including on-site training, and report handling. However, specimen receiving issues, suggest benefits in dispatching materials for a full 12-month cycle. Significant discrepancies in EQA performance indicate that four countries require long-term assistance, training and guidance. To ensure reliable patient results, promoting EQA in certain countries is essential to achieve the required level of quality.

Inter- and intra-subject similarity in network functional connectivity across a full narrative movie.

Naturalistic paradigms, such as watching movies during functional magnetic resonance imaging, are thought to prompt the emotional and cognitive processes typically elicited in real life situations. Therefore, naturalistic viewing (NV) holds great potential for studying individual differences. Previous studies have primarily focused on using shorter movie clips, geared toward eliciting specific and often isolated emotions, while the potential behind using full narratives depicted in commercial movies as a proxy for real-life experiences has barely been explored. Here, we offer preliminary evidence that a full narrative movie (FNM), that is, a movie covering a complete narrative arc, can capture complex socio-affective dynamics and their links to individual differences. Using the studyforrest dataset, we investigated inter- and intra-subject similarity in network functional connectivity (NFC) of 14 meta-analytically defined networks across a full narrative, audio-visual movie split into eight consecutive movie segments. We characterized the movie segments by valence and arousal portrayed within the sequences, before utilizing a linear mixed model to analyze which factors explain inter- and intra-subject similarity. Our results show that the model best explaining inter-subject similarity comprised network, movie segment, valence and a movie segment by valence interaction. Intra-subject similarity was influenced significantly by the same factors and an additional three-way interaction between movie segment, valence and arousal. Overall, inter- and intra-subject similarity in NFC were sensitive to the ongoing narrative and emotions in the movie. We conclude that FNMs offer complex content and dynamics that might be particularly valuable for studying individual differences. Further characterization of movie features, such as the overarching narratives, that enhance individual differences is needed for advancing the potential of NV research.

Advances in Blood Biomarkers for Alzheimer's Disease: Ultra-Sensitive Detection Technologies and Impact on Clinical Diagnosis.

Alzheimer's disease has escalated into a critical public health concern, marked by its neurodegenerative nature that progressively diminishes cognitive abilities. Recognized as a continuously advancing and presently incurable condition, AD underscores the necessity for early-stage diagnosis and interventions aimed at delaying the decline in mental function. Despite the proven efficacy of cerebrospinal fluid and positron emission tomography in diagnosing AD, their broader utility is constrained by significant costs and the invasive nature of these procedures. Consequently, the innovation of blood biomarkers such as Amyloid-beta, phosphorylated-tau, total-tau et al, distinguished by their high sensitivity, minimal invasiveness, accessibility, and cost-efficiency, emerges as a promising avenue for AD diagnosis. The advent of ultra-sensitive detection methodologies, including single-molecule enzyme-linked immunosorbent assay and immunoprecipitation-mass spectrometry, has revolutionized the detection of AD plasma biomarkers, supplanting previous low-sensitivity techniques. This rapid advancement in detection technology facilitates the more accurate quantification of pathological brain proteins and AD-associated biomarkers in the bloodstream. This manuscript meticulously reviews the landscape of current research on immunological markers for AD, anchored in the National Institute on Aging-Alzheimer's Association AT(N) research framework. It highlights a selection of forefront ultra-sensitive detection technologies now integral to assessing AD blood immunological markers. Additionally, this review examines the crucial pre-analytical processing steps for AD blood samples that significantly impact research outcomes and addresses the practical challenges faced during clinical testing. These discussions are crucial for enhancing our comprehension and refining the diagnostic precision of AD using blood-based biomarkers. The review aims to shed light on potential avenues for innovation and improvement in the techniques employed for detecting and investigating AD, thereby contributing to the broader field of neurodegenerative disease research.

Long non-coding and circular RNAs in osteoporosis: Translation to clinical practice.

Non-coding RNAs (ncRNAs) belong to a class of untranslated nucleic acids involved in regulation of gene expression. ncRNAs are categorized as small (<200 ribonucleotides in length), i.e., microRNAs (miRNAs), and long ncRNAs (lncRNAs) (200 to thousands of ribonucleotides in length) and circular RNAs (circRNAs). In contrast to miRNAs, the roles of lncRNAs in general and circRNAs in bone metabolism specifically are not well understood. As such, a comprehensive understanding of these RNA species in bone turnover could be of great value in the development of new diagnostic tools and therapeutic targets. Unfortunately, measurement of these unique RNAs lacks standardization, a component critical to clinical translation. This review examines the potential role of lncRNA and circRNA as bone biomarkers, the need for validated and standardized measurement and challenges thereof.

Localization and segmentation of atomic columns in supported nanoparticles for fast scanning transmission electron microscopy.

To accurately capture the dynamic behavior of small nanoparticles in scanning transmission electron microscopy, high-quality data and advanced data processing is needed. The fast scan rate required to observe structural dynamics inherently leads to very noisy data where machine learning tools are essential for unbiased analysis. In this study, we develop a workflow based on two U-Net architectures to automatically localize and classify atomic columns at particle-support interfaces. The model is trained on non-physical image simulations, achieves sub-pixel localization precision, high classification accuracy, and generalizes well to experimental data. We test our model on both in situ and ex situ experimental time series recorded at 5 frames per second of small Pt nanoparticles supported on CeO2(111). The processed movies show sub-second dynamics of the nanoparticles and reveal site-specific movement patterns of individual atomic columns.

Extraction, Identification, and Quantification of Polyphenols from the Theobroma cacao L. Fruit: Yield vs. Environmental Friendliness.

The cacao fruit is a rich source of polyphenols, including flavonoids and phenolic acids, which possess significant health benefits. The accurate identification and quantification of these bioactive compounds extracted from different parts of the cacao fruit, such as pods, beans, nibs, and cacao shells, require specific treatment conditions and analytical techniques. This review presents a comprehensive comparison of extraction processes and analytical techniques used to identify and quantify polyphenols from various parts of the cacao fruit. Additionally, it highlights the environmental impact of these methods, exploring the challenges and opportunities in selecting and utilizing extraction, analytical, and impact assessment techniques, while considering polyphenols' yield. The review aims to provide a thorough overview of the current knowledge that can guide future decisions for those seeking to obtain polyphenols from different parts of the cacao fruit.