AIMC Topic: Cellulose

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Machine learning for the adsorptive removal of ciprofloxacin using sugarcane bagasse as a low-cost biosorbent: comparison of analytic, mechanistic, and neural network modeling.

Environmental science and pollution research international
Contamination with traces of pharmaceutical compounds, such as ciprofloxacin, has prompted interest in their removal via low-cost, efficient biomass-based adsorption. In this study, classical models, a mechanistic model, and a neural network model we...

Machine learning powered CN-coordinated cobalt nanoparticles embedded cellulosic nanofibers to assess meat quality via clenbuterol monitoring.

Biosensors & bioelectronics
The World Anti-Doping Agency (WADA) has prohibited the use of clenbuterol (CLN) because it induces anabolic muscle growth while potentially causing adverse effects such as palpitations, anxiety, and muscle tremors. Thus, it is vital to assess meat qu...

Random forest machine-learning algorithm classifies white- and brown-rot fungi according to the number of the genes encoding Carbohydrate-Active enZyme families.

Applied and environmental microbiology
UNLABELLED: Wood-rotting fungi play an important role in the global carbon cycle because they are the only known organisms that digest wood, the largest carbon stock in nature. In the present study, we used linear discriminant analysis and random for...

Hybrid machine learning model based predictions for properties of poly(2-hydroxyethyl methacrylate)-poly(vinyl alcohol) composite cryogels embedded with bacterial cellulose.

Journal of chromatography. A
Supermacroporous composite cryogels with enhanced adjustable functionality have received extensive interest in bioseparation, tissue engineering, and drug delivery. However, the variations in their components significantly impactfinal properties. Thi...

Understanding cellulose pyrolysis via ab initio deep learning potential field.

Bioresource technology
Comprehensive and dynamic studies of cellulose pyrolysis reaction mechanisms are crucial in designing experiments and processes with enhanced safety, efficiency, and sustainability. The details of the pyrolysis mechanism are not readily available fro...

Plant-inspired multifunctional fluorescent cellulose nanocrystals intelligent nanocomposite hydrogel.

International journal of biological macromolecules
Intelligent hydrogel has great application potentials in flexible sensing and artificial intelligence devices due to its intrinsic characteristics. However, developing an intelligent hydrogel with favorable properties including high strength, superio...

Multivalued Logic for Optical Computing with Photonically Enabled Chiral Bio-organic Structures.

ACS nano
Photonic bio-organic multiphase structures are suggested here for integrated thin-film electronic nets with multilevel logic elements for multilevel computing via a reconfigurable photonic bandgap of chiral biomaterials. Herein, inspired by an artifi...

Artificial neural networks to model the enantioresolution of structurally unrelated neutral and basic compounds with cellulose tris(3,5-dimethylphenylcarbamate) chiral stationary phase and aqueous-acetonitrile mobile phases.

Journal of chromatography. A
Artificial neural networks (ANN; feed-forward mode) are used to quantitatively estimate the enantioresolution (Rs) in cellulose tris(3,5-dimethylphenylcarbamate) of chiral molecules from their structural information. To the best of our knowledge, for...

Portable Food-Freshness Prediction Platform Based on Colorimetric Barcode Combinatorics and Deep Convolutional Neural Networks.

Advanced materials (Deerfield Beach, Fla.)
Artificial scent screening systems (known as electronic noses, E-noses) have been researched extensively. A portable, automatic, and accurate, real-time E-nose requires both robust cross-reactive sensing and fingerprint pattern recognition. Few E-nos...

Application of artificial neural networks for Process Analytical Technology-based dissolution testing.

International journal of pharmaceutics
This work proposes the application of artificial neural networks (ANN) to non-destructively predict the in vitro dissolution of pharmaceutical tablets from Process Analytical Technology (PAT) data. An extended release tablet formulation was studied, ...