AIMC Topic: Surface-Active Agents
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Machine Learning Based Quantitative Structure-Dissolution Profile Relationship.
Journal of chemical information and modeling
Jun 5, 2025
Determining accurate drug dissolution processes in the gastrointestinal tract is critical in drug discovery as dissolution profiles provide essential information for estimating the bioavailability of orally administered drugs. While various methods h...
Edible aquatic robots with Marangoni propulsion.
Nature communications
May 7, 2025
Centimeter-scale aquatic robots could be used in environmental monitoring, exploration, and intervention in aquatic environments. However, existing robots rely on artificial polymers and commercial electronic components, which can pollute and disrupt...
Mapping human fingerprint beyond level-3 based on an amphiphilic aggregation-induced emission luminogen and the construction of intelligent platform for personal identification.
Analytica chimica acta
Mar 10, 2025
BACKGROUND: Fluorescence imaging agents have been benefiting tremendously from tailor-made aggregation-induced emission (AIE) luminogens, owing to their high on-off ratio, large signal contrast, low background noise as well as the resistance to photo...
Machine learning strengthened formulation design of pharmaceutical suspensions.
International journal of pharmaceutics
Nov 19, 2024
Many different formulation strategies have been investigated to oppose suboptimal treatment of long-term or chronic conditions, one of which are the nano- and microsuspensions prepared as long-acting injectables to prolong the release of an active ph...
Collaboration of bacterial consortia for biodegradation of high concentration phenol and potential application of machine learning.
Chemico-biological interactions
Jul 17, 2024
Mixed culture of microorganisms is an effective method to remove high concentration of phenol in wastewater. At present, it is still a challenge for microorganisms to remove high-concentration phenol from wastewater. In this study, a phenol-degrading...
Screening structure and predicting toxicity of pesticide adjuvants using molecular dynamics simulation and machine learning for minimizing environmental impacts.
The Science of the total environment
Jun 6, 2024
Surfactants as synergistic agents are necessary to improve the stability and utilization of pesticides, while their use is often accompanied by unexpected release into the environment. However, there are no efficient strategies available for screenin...
Nonionic surfactant Tween 80-facilitated bacterial transport in porous media: A nonmonotonic concentration-dependent performance, mechanism, and machine learning prediction.
Environmental research
Mar 16, 2024
The surfactant-enhanced bioremediation (SEBR) of organic-contaminated soil is a promising soil remediation technology, in which surfactants not only mobilize pollutants, but also alter the mobility of bacteria. However, the bacterial response and und...
What is the impact of intraperitoneal surfactant administration against postoperative intraabdominal adhesion formation? an experimental study.
Turkish journal of medical sciences
Nov 25, 2023
BACKGROUND/AIM: Surfactant is a surface-active substance that, in addition to its detergent effect, also has effects that reduce inflammation and fibrosis. Because of these effects, it was aimed herein to investigate the effect of intraperitoneal sur...
Modeling biosurfactant production from agroindustrial residues by neural networks and polynomial models adjusted by particle swarm optimization.
Environmental science and pollution research international
Aug 23, 2022
Biosurfactants are molecules with wide application in several industrial processes. Their production is damaged due to inefficient bioprocessing and expensive substrates. The latest developments of strategies to improve and economize the biosurfactan...
Prediction of nanoscale thermal transport and adsorption of liquid containing surfactant at solid-liquid interface via deep learning.
Journal of colloid and interface science
Jan 12, 2022
HYPOTHESIS: Recent advances in deep learning (DL) have enabled high level of real-time prediction of thermophysical properties of materials. On the other hand, molecular dynamics (MD) have been long used as a numerical microscope to observe detailed ...
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