A Comprehensive Review on Advanced Extraction Techniques for Retrieving Bioactive Components from Natural Sources

ACS Omega, Год журнала: 2024, Номер 9(29), С. 31274 - 31297

Опубликована: Июль 8, 2024

The extraction of bioactive components from natural sources has gained significant attention in recent years due to increasing demand for and functional constituents various industries, including pharmaceuticals, food, cosmetics. This review paper aims provide a comprehensive overview the studies on extracting using different advanced techniques. It highlights need efficient methods preserve these components' integrity bioactivity. Various techniques as supercritical-fluid extraction, microwave-assisted ultrasound-assisted subcritical solvent solid-phase microextraction are explored detail, highlighting their principles, advantages, limitations. further examines impact factors process, selection, time, temperature, ultrasonication-amplitude, etc. Additionally, emerging techniques, such green nanotechnology-based approaches, discussed, emphasizing potential enhance efficiency sustainability process. Furthermore, presents case experimental results research articles, providing insights into applying specific components, phenolics, flavonoids, alkaloids, essential oils. discusses yield, bioactivity, utilization extracted industries. Overall, this is valuable researchers, scientists, industry professionals interested sources. consolidates current knowledge optimization parameters, applications, facilitating advancements field development innovative component

Язык: Английский

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Real-time concentration detection of Al dust using GRU-based Kalman filtering approach

Process Safety and Environmental Protection, Год журнала: 2024, Номер 189, С. 154 - 163

Опубликована: Июнь 14, 2024

Язык: Английский

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Recent Advances in Conventional and Innovative Extraction Techniques for Recovery of High-Added Value Compounds for Food Additives and Nutraceuticals

Food Physics, Год журнала: 2025, Номер unknown, С. 100047 - 100047

Опубликована: Янв. 1, 2025

Язык: Английский

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Novel method for temperature prediction in rotary kiln process through machine learning and CFD

Powder Technology, Год журнала: 2024, Номер 439, С. 119649 - 119649

Опубликована: Март 13, 2024

Язык: Английский

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Prediction of Drug-like Compounds Solubility in Supercritical Carbon Dioxide: A Comparative Study between Classical Density Functional Theory and Machine Learning Approaches

Industrial & Engineering Chemistry Research, Год журнала: 2024, Номер 63(3), С. 1589 - 1603

Опубликована: Янв. 15, 2024

Supercritical carbon dioxide (scCO2) plays an essential role in various technological procedures, making the solubility of drugs scCO2 a crucial aspect drug formulation process. This study focuses on utilizing theoretical approaches to predict drug-like compounds order select optimum parameters for subsequent experimental procedures. Several machine learning models were developed and compared with previously established approach based classical density functional theory (cDFT). The CatBoost model, alvaDesc descriptors, demonstrated reasonably accurate predictions 187 (AARD = 1.8%). Meanwhile, incorporating CDK descriptors melting points as input parameters, exhibited satisfactory accuracy 14.3%) extrapolating new compounds. Comparing results between cDFT-based one revealed, average, higher faster prediction speed former. However, cDFT more physical behavior isotherms models. was particularly evident when ML struggled accurately extrapolate values beyond range supercritical state. Model CatBoost/CDK is freely accessible at http://chem-predictor.isc-ras.ru/individual/scco/.

Язык: Английский

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Supercritical water gasification for hospital wastewater

Journal of Hazardous Materials Advances, Год журнала: 2025, Номер unknown, С. 100651 - 100651

Опубликована: Фев. 1, 2025

Язык: Английский

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Design of Molecularly Imprinted Polymers Using Supercritical Carbon Dioxide Technology

Molecules, Год журнала: 2024, Номер 29(5), С. 926 - 926

Опубликована: Фев. 20, 2024

The design and development of affinity polymeric materials through the use green technology, such as supercritical carbon dioxide (scCO2), is a rapidly evolving field research with vast applications across diverse areas, including analytical chemistry, pharmaceuticals, biomedicine, energy, food, environmental remediation. These are specifically engineered to interact target molecules, demonstrating high selectivity. unique properties scCO2, which present both liquid– gas–like an accessible critical point, offer environmentally–friendly highly efficient technology for synthesis processing polymers. in scCO2 involve several strategies. Commonly, incorporation functional groups or ligands into polymer matrix allows selective interactions compounds. choice monomer type, ligands, conditions key parameters material performance terms In addition, molecular imprinting allied co–polymerization surface modification commonly used these strategies, enhancing materials’ versatility. This review aims provide overview strategies recent advancements using scCO2.

Язык: Английский

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Heat transfer analysis in the engine‐based oil‐based hybrid nanofluid flow between two spinning disks: Probed by artificial neural network

ZAMM ‐ Journal of Applied Mathematics and Mechanics / Zeitschrift für Angewandte Mathematik und Mechanik, Год журнала: 2025, Номер 105(2)

Опубликована: Фев. 1, 2025

Abstract A time‐dependent mixed convective hybrid nanofluid (HNF) ( /Engine oil) flow between two spinning disks is considered. The physical problem modeled and transformed into a non‐dimensional ordianary differential equation system to reduce the complexity. modified Devi Devi's model utilized for properties. cylindrical shape nanoparticles are considered analysis of various pertinent parameters. base fluid as engine oil briefly explain its thermal behavior. One famous optimization algorithms Levenberg–Marquardt used train artificial neural network with data achieved from numerical results analyze states HNF. state variables well nanoparticle shapes displayed through graphs tables. enhancement expansion parameter () causes augment, then drop augment again velocity gradient increasing distance disks. temperature initially enhances rising strength (). concentration nanomaterial associated higher values volume fraction distribution obtained show that smaller will keep at lower temperature. validated in each case by providing validation absolute error graphs.

Язык: Английский

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Open-loop linear modeling method for unstable flow utilizing built-in data-driven feedback controllers

Physical Review Fluids, Год журнала: 2025, Номер 10(3)

Опубликована: Март 31, 2025

Язык: Английский

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Improved Solubility Predictions in scCO₂ Using Thermodynamics-Informed Machine Learning Models

Journal of Chemical Information and Modeling, Год журнала: 2025, Номер unknown

Опубликована: Апрель 15, 2025

Accurate solubility prediction in supercritical carbon dioxide (scCO2) is crucial for optimizing experimental design by eliminating unnecessary and costly trials at an early stage, thereby streamlining the workflow. A comprehensive database containing 31,975 records has been compiled, providing a foundation developing predictive models applicable to diverse class of chemical compounds, with particular focus on drug-like substances. In this study, we propose domain-aware machine learning approach that incorporates thermodynamic properties governing phase transitions predictions scCO2. Predictive were developed using CatBoost algorithm graph-based architecture employing directed message passing identify most effective approach. Furthermore, auxiliary solute, including melting point, critical parameters, enthalpy vaporization, Gibbs free energy solvation, predicted as part work. The findings underscore efficacy incorporating domain-specific features enhance accuracy scCO2 modeling. interpretation applicability domain assessment have confirmed qualitative selection employed descriptors, demonstrating their ability generalize unique compounds fall outside defined domain.

Язык: Английский

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