AIMC Topic: Solutions

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Synthesis and characterization of Fe(III)-doped beta-cyclodextrin-grafted chitosan cryogel beads for adsorption of diclofenac in aqueous solutions: Adsorption experiments and deep-learning modeling.

International journal of biological macromolecules
Diclofenac (DCF) is frequently detected in aquatic environments, emphasizing the critical need for its efficient removal globally. Here, we present the synthesis of Fe(III)-doped β-CD-grafted chitosan (Fe/β-CD@CS) cryogel beads designed for adsorbing...

Computational prediction of complex cationic rearrangement outcomes.

Nature
Recent years have seen revived interest in computer-assisted organic synthesis. The use of reaction- and neural-network algorithms that can plan multistep synthetic pathways have revolutionized this field, including examples leading to advanced natur...

Explainable Supervised Machine Learning Model To Predict Solvation Gibbs Energy.

Journal of chemical information and modeling
Many challenges persist in developing accurate computational models for predicting solvation free energy (Δ). Despite recent developments in Machine Learning (ML) methodologies that outperformed traditional quantum mechanical models, several issues r...

Factor analysis of error in oxidation potential calculation: A machine learning study.

Journal of computational chemistry
The conductor-like polarizable continuum model (C-PCM), which is a low-cost solvation model, cannot treat characteristic interactions between the solvent and substructure(s) of the solute. Moreover, the error in a charged system is significant. Using...

Rapid Temperature-Dependent Rheological Measurements of Non-Newtonian Solutions Using a Machine-Learning Aided Microfluidic Rheometer.

Analytical chemistry
Biofluids such as synovial fluid, blood plasma, and saliva contain several proteins which impart non-Newtonian properties to the biofluids. The concentration of such protein macromolecules in biofluids is regarded as an important biomarker for the di...

Group Contribution and Machine Learning Approaches to Predict Abraham Solute Parameters, Solvation Free Energy, and Solvation Enthalpy.

Journal of chemical information and modeling
We present a group contribution method (SoluteGC) and a machine learning model (SoluteML) to predict the Abraham solute parameters, as well as a machine learning model (DirectML) to predict solvation free energy and enthalpy at 298 K. The proposed gr...

Modeling of Cu(II) Adsorption from an Aqueous Solution Using an Artificial Neural Network (ANN).

Molecules (Basel, Switzerland)
This research optimized the adsorption performance of rice husk char (RHC4) for copper (Cu(II)) from an aqueous solution. Various physicochemical analyses such as Fourier transform infrared spectroscopy (FTIR), field-emission scanning electron micros...

Stability assessment of extracts obtained from Arbutus unedo L. fruits in powder and solution systems using machine-learning methodologies.

Food chemistry
Arbutus unedo L. (strawberry tree) has showed considerable content in phenolic compounds, especially flavan-3-ols (catechin, gallocatechin, among others). The interest of flavan-3-ols has increased due their bioactive actions, namely antioxidant and ...

Ionic Liquid-Based Ultrasonic-Assisted Extraction Coupled with HPLC and Artificial Neural Network Analysis for .

Molecules (Basel, Switzerland)
is widely used in traditional Chinese medicine (TCM). Ganoderic acid A and D are the main bioactive components with anticancer effects in . To obtain the maximum content of two compounds from , a novel extraction method, an ionic liquid-based ultras...

Artificial neural network model to predict transport parameters of reactive solutes from basic soil properties.

Environmental pollution (Barking, Essex : 1987)
Measurement of solute-transport parameters through soils for a wide range of solute- and soil-types is time-consuming, laborious, expensive and practically impossible. So, indirect methods for estimating the transport parameters by pedo-transfer func...