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http://dx.doi.org/10.1039/d4fd90062f | DOI Listing |
Curr Opin Electrochem
April 2025
Department of Chemistry, The University of Texas at Austin, Austin, TX 78712, USA.
Electrochemical correlative microscopy involves the pairing of electrochemical measurements with one or multiple orthogonal microscopic techniques. By integrating electrochemical measurements, especially scanning electrochemical probe microscopies (SEPMs), with correlative optical microscopy, spectroscopy, or electron microscopies, rich information complimentary to the electrochemical measurement can be obtained. This information can reveal detailed structure-property-activity relationships at electrochemical interfaces.
View Article and Find Full Text PDFACS Appl Mater Interfaces
September 2025
Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea.
The amorphous phases of crystalline metal-organic frameworks (MOFs), known as amorphous metal-organic frameworks (AMOFs), offer a vast yet underexplored search space for numerous applications. To efficiently navigate this chemical space, data-driven methodologies are essential for elucidating structure-property relationships. In this study, a novel approach to explore this space is introduced by utilizing chemically accurate reactive force field (ReaxFF) to first create the AMOF database.
View Article and Find Full Text PDFSci Rep
August 2025
IBM Research, Nairobi, Kenya.
Representation learning via pre-trained deep learning models is emerging as an integral method for studying the molecular structure-property relationship, which is then leveraged to predict molecular properties or design new molecules with desired attributes. We propose an unsupervised method to localize and characterize representations of pre-trained models through the lens of non-parametric property-driven subset scanning (PDSS), to improve the interpretability of deep molecular representations. We assess its detection capabilities on diverse molecular benchmarks (ZINC-250K, MOSES, MoleculeNet, FlavorDB, M2OR) across predictive chemical language models (MoLFormer, ChemBERTa) and molecular graph generative models (GraphAF, GCPN).
View Article and Find Full Text PDFJ Am Chem Soc
June 2025
Key Laboratory of Green Chemistry and Technology of Ministry of Education, College of Chemistry, Sichuan University, 29 Wangjiang Road, Chengdu 610064, P. R. China.
Achieving narrowband fluorescence in polycyclic aromatic hydrocarbons (PAHs) is crucial for ultrahigh-definition organic light-emitting diodes (UD-OLEDs), yet the underlying structure-property relationships that dictate emission bandwidth remain insufficiently understood. In this study, we introduce aromaticity localization as a predictive framework for identifying narrowband emitters. Using nucleus-independent chemical shift (NICS) analysis, we uncover a strong correlation between localized aromaticity and reduced vibrational coupling, demonstrating that restricting π-electron delocalization effectively suppresses shoulder peaks, thereby minimizing spectral broadening.
View Article and Find Full Text PDFSmall
June 2025
State Key Laboratory of Functional Crystals and Devices, Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences, Fuzhou, Fujian, 350002, China.
Although 3-hydroxypropane-1-sulfonates are discovered several decades ago, their nonlinear optical (NLO) properties have remained unexplored, mainly because most of them crystallize in centrosymmetric space groups. Herein, by incorporating polarizable rare-earth metal La cations, a novel non-centrosymmetric 3-hydroxypropane-1-sulfonate, namely La[SO(CH)OH] is designed and synthesized, whose crystal structure features 3D framework constructed by highly distorted [LaO] polyhedra and [SO(CH)OH] anions. Experimental results reveal that La[SO(CH)OH] is NLO-active with a moderate second-harmonic generation response of 0.
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