Non-Contact Atomic Force Microscopy with CO-functionalized metal tips (referred to as HR-AFM) provides access to the internal structure of individual molecules adsorbed on a surface with totally unprecedented resolution. Previous works have shown that deep learning (DL) models can retrieve the chemical and structural information encoded in a 3D stack of constant-height HR-AFM images, leading to molecular identification. In this work, we overcome their limitations by using a well-established description of the molecular structure in terms of topological fingerprints, the 1024-bit Extended Connectivity Chemical Fingerprints of radius 2 (ECFP4), that were developed for substructure and similarity searching.
View Article and Find Full Text PDFThiophosphate-based all-solid-state batteries (ASSBs) are considered the most promising candidate for the next generation of energy storage systems. However, thiophosphate-based ASSBs suffer from fast capacity fading with nickel-rich cathode materials. In many reports, this capacity fading is attributed to an increase of the charge transfer resistance of the composite cathode caused by interface degradation and/or chemo-mechanical failure.
View Article and Find Full Text PDFAtomic layer deposition (ALD) derived ultrathin conformal AlO coating has been identified as an effective strategy for enhancing the electrochemical performance of Ni-rich LiNiCoMnO (NCM; 0 ≤x, y, z < 1) based cathode active materials (CAM) in Li-ion batteries. However, there is still a need to better understand the beneficial effect of ALD derived surface coatings on the performance of NCM based composite cathodes. In this work, we applied and optimized a low-temperature ALD derived AlO coating on a series of Ni-rich NCM-based (NCM622, NCM71.
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