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Multipurpose packaging system based on intelligent carboxymethyl cellulose film and activated cellulose acetate electrospun nanofibers for seafoods.
Int J Biol Macromol
January 2025
Department of Food Nanotechnology, Research Institute of Food Science and Technology (RIFST), Mashhad, PO Box: 91895-157-356, Iran. Electronic address:
The objective of this research is to develop a natural macromolecules-based smart double-layer film using carboxymethyl cellulose (CMC) film containing pomegranate peel anthocyanins (PPA) and cellulose acetate nanofibers (CANFs) with Artemisia sieberi Besser essential oil-loaded nanostructured lipid carriers (ABNLCs). Based on the performance as a color indicator, and other studied properties, the CMC/PPA 8 % film was selected as the optimized film. The double-layer film was constructed by electrospinning the CANFs containing ABNLCs on the optimized CMC film.
View Article and Find Full Text PDFCompetitive Adsorption of Small Molecule Inhibitors and Trimethylaluminum Precursors on the Cu(111) Surface during Area-Selective Atomic Layer Deposition: A GCMC Study.
Langmuir
January 2025
College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310027, China.
In area-selective atomic layer deposition (AS-ALD), small molecule inhibitors (SMIs) play a critical role in directing surface selectivity, preventing unwanted deposition on non-growth surfaces, and enabling precise thin-film formation essential for semiconductor and advanced manufacturing processes. This study utilizes grand canonical Monte Carlo (GCMC) simulations to investigate the competitive adsorption characteristics of three SMIs─aniline, 3-hexyne, and propanethiol (PT)─alongside trimethylaluminum (TMA) precursors on a Cu(111) surface. Single-component adsorption analyses reveal that aniline attains the highest coverage among the SMIs, attributed to its strong interaction with the Cu surface; however, this coverage decreases by approximately 42% in the presence of TMA, underscoring its susceptibility to competitive adsorption effects.
View Article and Find Full Text PDFAtomically Fine-Tuning Organic-Inorganic Carbon Molecular Sieve Membranes for Hydrogen Production.
ACS Nano
January 2025
Department of Chemical and Biological Engineering, University at Buffalo, The State University of New York, Buffalo, New York 14260, United States.
Polymeric membranes with great processability are attractive for the H/CO separation required for hydrogen production from renewable biomass with carbon capture for utilization and sequestration. However, it remains elusive to engineer polymer architectures to obtain desired sub-3.3 Å ultramicropores to efficiently sieve H from CO.
View Article and Find Full Text PDFSelf-assembly of chromatic patchy particles with tetrahedrally arranged patches.
Soft Matter
January 2025
Department of Theoretical Chemistry, Institute of Chemical Sciences, Faculty of Chemistry, Maria-Curie-Sklodowska University in Lublin, Pl. M Curie-Sklodowskiej 3, 20-031 Lublin, Poland.
The achievement of selectivity in the formation of cubic diamond is challenging due to the emergence of competing phases such as its hexagonal polymorph or clathrates possessing similar free energy. Although both polymorphs exhibit a complete photonic bandgap, cubic diamond exhibits it at lower frequencies than the hexagonal counterpart, positioning it as a promising candidate for photonic applications. Herein, we demonstrate that the 1 : 1 mixture of identical patchy particles cannot selectively form the cubic diamond polymorph due to the frustrations present in the system that are manifested in the primary adsorption layer and propagate as the film grows.
View Article and Find Full Text PDFExplicit relation between thin film chromatography and column chromatography conditions from statistics and machine learning.
Nat Commun
January 2025
AI for Science (AI4S)-Preferred Program, Peking University Shenzhen Graduate School, Shenzhen, 518055, China.
In chemistry, empirical paradigms prevail, especially within the realm of chromatography, where the selection of separation conditions frequently relies on the chemist's experience. However, the underlying rationale for such experiential knowledge has not been established or analysed. This study explicitly elucidates how chemists use thin-layer chromatography (TLC) to determine column chromatography (CC) conditions, employing statistical analysis and machine learning techniques.
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