Environmental Science Water Research & Technology, Journal Year: 2024, Volume and Issue: 10(5), P. 1009 - 1018
Published: Jan. 1, 2024
Managing drinking water-associated pathogens that can cause infections in immunocompromised individuals is a persistent challenge, particularly for healthcare facilities where occupant exposures carry substantial health risk.
Language: Английский
Advanced Materials, Journal Year: 2024, Volume and Issue: 36(18)
Published: Jan. 19, 2024
Abstract Machine learning holds significant research potential in the field of nanotechnology, enabling nanomaterial structure and property predictions, facilitating materials design discovery, reducing need for time‐consuming labor‐intensive experiments simulations. In contrast to their achiral counterparts, application machine chiral nanomaterials is still its infancy, with a limited number publications date. This despite great advance development new sustainable high values optical activity, circularly polarized luminescence, enantioselectivity, as well analysis structural chirality by electron microscopy. this review, an methods used studying provided, subsequently offering guidance on adapting extending work nanomaterials. An overview within framework synthesis–structure–property–application relationships presented insights how leverage study these highly complex are provided. Some key recent reviewed discussed Finally, review captures achievements, ongoing challenges, prospective outlook very important field.
Language: Английский
Citations
25
Critical Reviews in Oncology/Hematology, Journal Year: 2025, Volume and Issue: unknown, P. 104701 - 104701
Published: March 1, 2025
Language: Английский
Citations
3
Advanced Materials, Journal Year: 2025, Volume and Issue: unknown
Published: Feb. 9, 2025
Machine learning is increasingly being applied in polymer chemistry to link chemical structures macroscopic properties of polymers and identify patterns the that help improve specific properties. To facilitate this, a dataset needs be translated into machine readable descriptors. However, limited inadequately curated datasets, broad molecular weight distributions, irregular configurations pose significant challenges. Most off shelf mathematical models often need refinement for applications. Addressing these challenges demand close collaboration between chemists mathematicians as must formulate research questions terms while are required refine This review unites both disciplines address curation hurdles highlight advances synthesis modeling enhance data availability. It then surveys ML approaches used predict solid-state properties, solution behavior, composite performance, emerging applications such drug delivery polymer-biology interface. A perspective field concluded importance FAIR (findability, accessibility, interoperability, reusability) integration theory discussed, thoughts on machine-human interface shared.
Language: Английский
Citations
2
Macromolecules, Journal Year: 2024, Volume and Issue: unknown
Published: Aug. 9, 2024
Language: Английский
Citations
7
Environmental Science Nano, Journal Year: 2024, Volume and Issue: 11(7), P. 2885 - 2893
Published: Jan. 1, 2024
There are numerous opportunities for nanomaterials and nanotechnology to support circular economy adoption. In this perspective, we present the important role engineered can play in advancing circularity of bulk composite materials.
Language: Английский
Citations
4
MRS Communications, Journal Year: 2024, Volume and Issue: 14(5), P. 752 - 770
Published: July 1, 2024
Abstract Artificial intelligence and machine learning (ML) continue to see increasing interest in science engineering every year. Polymer is no different, though implementation of data-driven algorithms this subfield has unique challenges barring widespread application these techniques the study polymer systems. In Prospective, we discuss several critical ML science, including structure representation, high-throughput limitations, limited data availability. Promising studies targeting resolution issues are explored, contemporary research demonstrating potential despite existing obstacles discussed. Finally, present an outlook for moving forward. Graphical
Language: Английский
Citations
4
Advanced Materials, Journal Year: 2025, Volume and Issue: unknown
Published: March 10, 2025
The traditional trial-and-error approach, although effective, is inefficient for optimizing rubber composites. latest developments in machine learning (ML)-assisted methodologies are also not suitable predicting and composite properties. This due to the dependency of properties on processing conditions, which prevents alignment data collected from different sources. In this work, a novel workflow called ML-enhanced approach proposed. integrates orthogonal experimental design with symbolic regression (SR) effectively extract empirical principles. combination enables optimization process retain characteristics while significantly improving efficiency capability. Using composites as model system, extracts principles encapsulated by high-frequency terms SR-derived mathematical formulas, offering clear guidance material property optimization. An online platform has been developed that allows no-code usage proposed methodology, designed seamlessly integrate into existing process.
Language: Английский
Citations
0
Advanced Nanocomposites, Journal Year: 2025, Volume and Issue: unknown
Published: March 1, 2025
Language: Английский
Citations
0
Polymer Chemistry, Journal Year: 2025, Volume and Issue: unknown
Published: Jan. 1, 2025
AI-driven polymer science: a structured perspective on integrating machine learning for data analysis, property prediction, and automated research workflows.
Language: Английский
Citations
0
Composites Part A Applied Science and Manufacturing, Journal Year: 2025, Volume and Issue: unknown, P. 109027 - 109027
Published: May 1, 2025
Language: Английский
Citations
0