AIMC Topic: Adsorption
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Machine learning framework for oxytetracycline removal using nanostructured cupric oxide supported on magnetic chitosan alginate biocomposite.
Scientific reports
Jul 18, 2025
This research proposes a machine learning controlled method for removing the antibiotic oxytetracycline (OTC) from liquids through the use of nanostructured cupric oxide (CuO) nanoparticles. These nanoparticles are attached to magnetic chitosan/algin...
Corona Dynamics of Nanoparticles and Their Functional Design Space in Molecular Sensing.
ACS nano
Jul 6, 2025
As nanomaterials increasingly interact with complex biological environments, understanding and designing their interfacial layers is critical for enabling functional and responsive behaviors. The protein corona, a spontaneously formed biomolecular la...
High-Throughput Analysis of Protein Adsorption to a Large Library of Polymers Using Liquid Extraction Surface Analysis-Tandem Mass Spectrometry (LESA-MS/MS).
Analytical chemistry
Jun 10, 2025
Biomaterials play an important role in medicine from contact lenses to joint replacements. High-throughput screening coupled with machine learning has identified synthetic polymers that prevent bacterial biofilm formation, prevent fungal cell attachm...
Predicting biomolecule adsorption on nanomaterials: a hybrid framework of molecular simulations and machine learning.
Nanoscale
May 2, 2025
The adsorption of biomolecules on the surface of nanomaterials (NMs) is a critical determinant of their behavior, toxicity, and efficacy in biological systems. Experimental testing of these phenomena is often costly or complicated. Computational appr...
Modeling PFAS Sorption in Soils Using Machine Learning.
Environmental science & technology
Apr 11, 2025
In this study, we introduce PFASorptionML, a novel machine learning (ML) tool developed to predict solid-liquid distribution coefficients () for per- and polyfluoroalkyl substances (PFAS) in soils. Leveraging a data set of 1,274 entries for PFAS in ...
Optimizing Cu adsorption prediction in Undaria pinnatifida using machine learning and isotherm models.
Journal of hazardous materials
Apr 7, 2025
Algae are cost-effective bioadsorbents for heavy metal remediation, yet their potential is underutilized due to limitations in traditional adsorption models. This study integrates machine learning (ML) techniques with traditional models to predict th...
Biohybrid microrobots with a Spirulina skeleton and MOF skin for efficient organic pollutant adsorption.
Nanoscale
Mar 24, 2025
Wastewater treatment is a key component in maintaining environmental health and sustainable urban life, and the rapid development of micro/nanotechnology has opened up new avenues for more efficient treatment processes. This work developed a novel bi...
Machine Learning for Quantitative Prediction of Protein Adsorption on Well-Defined Polymer Brush Surfaces with Diverse Chemical Properties.
Langmuir : the ACS journal of surfaces and colloids
Mar 12, 2025
Polymer informatics has attracted increasing attention because machine learning can establish quantitative structure-property relationships in polymer materials. Understanding and controlling protein adsorption on polymer surfaces are crucial for var...
Development of hybrid robust model based on computational modeling and machine learning for analysis of drug sorption onto porous adsorbents.
Scientific reports
Mar 12, 2025
This study investigates the utilization of three regression models, i.e., Kernel Ridge Regression (KRR), nu-Support Vector Regression ([Formula: see text]-SVR), and Polynomial Regression (PR) for the purpose of forecasting the concentration (C) of a ...
Addressing data handling shortcomings in machine learning studies on biochar for heavy metal remediation.
Journal of hazardous materials
Mar 10, 2025
Recent advancements in machine learning (ML) technologies have significantly enhanced their applications in environmental sciences, particularly in the domains of soil and water remediation. This paper reviews recent studies that employ ML to optimiz...
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