The performance of 2D nanomaterials hinges on both the chemical compositions and the morphological structures across different length scales. Among all the three dimensions, thickness is the only one that falls into the nanometer scale and, to some extent, determines the intrinsic properties of 2D nanomaterials. In this study, we report the preparation and precise thickness control of 2D nanosheets assembled from a library of monodispersed amphiphilic giant molecules composed of functional polyhedral oligomeric silsesquioxanes (POSSs) as the side groups. Solution self-assembly of such giant molecules resulted in 2D nanosheets with similar structural configurations, where a bilayer of hydrophobic isobutyl POSS (BPOSS) is sandwiched by two monolayers of hydrophilic POSS bearing carboxylic acid groups (APOSS). The thickness of the obtained nanosheets could be tuned through adjusting the chemical compositions of the pendant POSS cages. Intriguingly, we found that the thickness of the 2D nanosheets was not necessarily proportional to the contour length of the giant molecule nor the total number of POSS cages tethered to the main chain. Indeed, the number ratio of BPOSS to APOSS, rather than the exact number, played a deterministic role in the thickness control. To explain the unusual thickness dependence, we built up a structure model with an in-plane orientation of the giant molecules in the nanosheets, from which a formula was further deduced to semi-quantitatively describe the inverse relationship between the overall thickness and the number ratio of BPOSS to APOSS.
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http://dx.doi.org/10.1039/d1sc00021g | DOI Listing |
Rheumatology (Oxford)
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Research Center for Genome & Medical Sciences, Tokyo Metropolitan Institute of Medical Science, Tokyo, Japan.
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January 2025
Department of Civil and Environmental Engineering, Institute of Science Tokyo, Meguro-ku, Tokyo, 152-8552, Japan. Electronic address:
Anaerobic digestion (AD) offers great potential for pollutant removal and bioenergy recovery. However, it faces challenges when using livestock manure (LSM) as a feedstock given its high content of refractory materials (e.g.
View Article and Find Full Text PDFNat Commun
January 2025
Institute of Polymer Optoelectronic Materials and Devices, State Key Laboratory of Luminescent Materials and Devices, South China University of Technology, Guangzhou, China.
To achieve the commercialization of organic solar cells (OSCs), it is crucial not only to enhance power conversion efficiency (PCE) but also to improve device stability through rational molecular design. Recently emerging giant molecular acceptor (GMA) materials offer various advantages, such as precise chemical structure, high molecular weight (beneficial to film stability under several external stress), and impressive device efficiency, making them a promising candidate. Here, we report a dendritic hexamer acceptor developed through a branch-connecting strategy, which overcomes the molecular weight bottleneck of GMAs and achieves a high production yield over 58%.
View Article and Find Full Text PDFACS Nano
January 2025
Institute of Physics, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland.
Controlling the light emitted by individual molecules is instrumental to a number of advanced nanotechnologies ranging from super-resolution bioimaging and molecular sensing to quantum nanophotonics. Molecular emission can be tailored by modifying the local photonic environment, for example, by precisely placing a single molecule inside a plasmonic nanocavity with the help of DNA origami. Here, using this scalable approach, we show that commercial fluorophores may experience giant Purcell factors and Lamb shifts, reaching values on par with those recently reported in scanning tip experiments.
View Article and Find Full Text PDFNat Commun
January 2025
Chair for Bioinformatics, Institute for Computer Science, Friedrich Schiller University Jena, Jena, Germany.
Small molecule machine learning aims to predict chemical, biochemical, or biological properties from molecular structures, with applications such as toxicity prediction, ligand binding, and pharmacokinetics. A recent trend is developing end-to-end models that avoid explicit domain knowledge. These models assume no coverage bias in training and evaluation data, meaning the data are representative of the true distribution.
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