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Glass-ceramics and molybdenum doping synergistic approach for Nasicon-type solid-state electrolytes
No full textAdvancing energy density, enabling lithium metal anodes, and ensuring unparalleled safety and operational reliability in lithium batteries hinge on advancing inorganic solid-state electrolytes. To overcome current im-pediments, we present an innovative approach that integrates glass-ceramics with a pioneering new Nasicon strategy involving molybdenum doping. In the conducted study, a series of 14Li2O-9Al2O3-38TiO2-(39-x)P2O5- xMoO3 glasses, denoted as LATPMox, along with their corresponding glass-ceramics (LATPMox-GC), have exhibited a promising characteristic as solid electrolytes. X-ray diffraction (XRD) analysis confirms the formation of the novel Mo-doped Nasicon phases in the glass-ceramics, as validated by Rietveld refinement. Examination of the crystallization kinetic behavior of the glasses reveals a three-dimensional nucleation process with spherical particle growth, featuring an activation energy of 165 kJ mol-1. Transmission Electron Microscopy TEM char-acterization aligns crystallization behavior with crystallite and distribution within the glass matrix, resulting in a compact and dense microstructure. The structural properties of the resultant phases are examined through FT-IR, Raman spectroscopy, and TEM-SEAD analysis. Vickers indentation tests were employed to assess the microscopic fracture toughness, and both the glass and glass-ceramics materials demonstrated favorable mechanical per-formance. Optical characterization using UV–visible absorption highlights the reduction of Mo6+ to Mo5+, likely occupying tetrahedral sites within the crystalline lattice. Impedance spectroscopy measurement showcases the effective promotion of ionic conductivity following Mo doping, reaching a total conductivity value of 5.50 × 10-5 Ω-1 cm-1 along with a high lithium transference number of 0.99 at room temperature for LATPMo2.6-GC glass-ceramic. This value is larger than that of many other glass-ceramics as well as that of the well-known lithium phosphorous oxy-nitride LiPON solid electrolyte whose ionic conductivity at RT is around 2 × 10-6 Ω-1 cm-1
Exogenous silicon facilitates safe crop production in cadmium-contaminated soils: A comprehensive meta-analysis
No full textCombined effects of flood, drought and land use dominate water quality and nutrient exports in Jialing River basin, SW China
No full textThe affinity towards the hydrophobic region of biomimicking bacterial membranes drives the antimicrobial activity of EFV12 peptide from Lactobacillus gasseri gut microbiota
No full textThe gut microbiota consists of a large variety of microorganisms, which interact with the immune system and exert essential roles for the human body health. Many of these microorganisms are also capable of producing various bioactive molecules, such as selective antimicrobial peptides, thus promoting the proliferation of only certain bacterial strains. These result in the shaping of the composition of the local microbiome and the co-evolution with a complex microbiome. Recently, a small peptide, named EFV12 and deriving from the bacterium Lactobacillus gasseri SF1109 regularly placed in the human intestine, showed a significant antimicrobial activity. Here we discuss a biophysical study on the structural changes induced by the peptide on lipid bilayers mimicking bacterial membranes with the aim of shedding light on the molecular features driving the biocidal activity against Gram(+) and Gram(−) strains. Supported Lipid Bilayers and liposomes composed of 1,2-oleoyl-sn-glycero-3-phosphocholine and 1,2-oleoyl-sn-glycero-3-rac-phosphoglycerol, both in the absence and presence of cardiolipin and lipopolysaccharides (LPSs), were selected to investigate the peptide-lipid interactions through a combination of specular Neutron Reflectometry, Dynamic Light Scattering, Small-Angle X-ray Scattering and Circular Dichroism measurements. The obtained results indicated association of EFV12 peptide with the hydrophobic region of lipid bilayers, which caused their destabilization, and is thus driving the antimicrobial activity against bacterial cells
Tuning Silica Surface Properties for Enhanced Performance in Si–UHMWPE Battery Separators
No full textGraded Cathode Design for Enhanced Performance of Sulfide-Based Solid-State Batteries
No full textSolid-state batteries present a promising technology to overcome the energy density limitations of lithium-ion batteries. However, achieving a high areal loading in cathodes without introducing significant transport limitations remains a key challenge, particularly in thick electrodes. In this work, we study the impact of a three-layer graded cathode design on the performance of a (NCM83) (LPSCl) composite cathode using a combination of experiments and microstructure-resolved simulations. An increased LPSCl content at the separator and higher NCM83 content toward the current collector improve effective charge transport, resulting in better rate performance and reduced overpotentials at high current densities. This comprehensive experimental and theoretical study demonstrates that the optimization of cathode design has the potential to significantly enhance the performance of solid-state batteries
Electron delocalization engineering via hierarchical modulation in single-atom catalysts for highly efficient electrochemical CO2 reduction
No full textResource-adaptive successive doubling for hyperparameter optimization with large datasets on high-performance computing systems
No full textThe accuracy of Machine Learning (ML) models is highly dependent on the hyperparameters that have to be chosen by the user before the training. However, finding the optimal set of hyperparameters is a complex process, as many different parameter combinations need to be evaluated, and obtaining the accuracy of each combination usually requires a full training run. It is therefore of great interest to reduce the computational runtime of this process. On High-Performance Computing (HPC) systems, several configurations can be evaluated in parallel to speed up this Hyperparameter Optimization (HPO). State-of-the-art HPO methods follow a bandit-based approach and build on top of successive halving, where the final performance of a combination is estimated based on a lower than fully trained fidelity performance metric and more promising combinations are assigned more resources over time. Frequently, the number of epochs is treated as a resource, letting more promising combinations train longer. Another option is to use the number of workers as a resource and directly allocate more workers to more promising configurations via data-parallel training. This article proposes a novel Resource-Adaptive Successive Doubling Algorithm (RASDA), which combines a resource- adaptive successive doubling scheme with the plain Asynchronous Successive Halving Algorithm (ASHA). Scalability of this approach is shown on up to 1,024 Graphics Processing Units (GPUs) on modern HPC systems. It is applied to different types of Neural Networks (NNs) and trained on large datasets from the Computer Vision (CV), Computational Fluid Dynamics (CFD), and Additive Manufacturing (AM) domains, where performing more than one full training run is usually infeasible. Empirical results show that RASDA outperforms ASHA by a factor of up to 1.9 with respect to the runtime. At the same time, the solution quality of final ASHA models is maintained or even surpassed by the implicit batch size scheduling of RASDA. With RASDA, systematic HPO is applied to a terabyte-scale scientific dataset for the first time in the literature, enabling efficient optimization of complex models on massive scientific data
An Automated Workflow to Discover the Structure–Stability Relations for Radiation Hard Molecular Semiconductors
No full textEmerging photovoltaics for outer space applications are one of the many examples where radiation hard molecular semiconductors are essential. However, due to a lack of general design principles, their resilience against extra-terrestrial high-energy radiation can currently not be predicted. In this work, the discovery of radiation hard materials is accelerated by combining the strengths of high-throughput, lab automation and machine learning. This way, a large material library of more than 130 organic hole transport materials is automatically processed, degraded, and measured. The materials are degraded under ultraviolet-C (UVC) light in a nitrogen atmosphere, serving as the conditions for electromagnetic radiation hardness tests. A value closely related to the differential quantum yield for photodegradation is extracted from the evolution of the UV–visible (UV–vis) spectra over time and used as a stability target. Following this procedure, a stability ranking spanning over 3 orders of magnitude was obtained. Combining Gaussian Process Regression based on predictors from structural fingerprints and manual filtering of the materials by features, structure–stability relations for UVC stable materials could be found: Fused aromatic ring clusters are beneficial, whereas thiophene, methoxy and vinylene groups are detrimental. Comparing the UV–vis spectra of the degraded material in film and solution, bond cleavage could be made out as the leading degradation mechanism. Even though UVC light can in principle break most organic bonds, the stable materials are able to distribute and dissipate the energy well enough so that the chemical structures remain stable. The established predictive model quantifies the effect of specific molecular features on UVC stability, allowing chemists to consider UVC stability in their molecular design strategy. In the future, a larger data set will allow to inversely design molecular semiconductors which show high performance and radiation hardness at the same time
Diastereomeric Fullerene Composite Engineering for Enhanced Perovskite Solar Cells
No full textAchieving high performance and long-term stability in perovskite solar cells (PSCs) typically requires the use of surface passivation layers to suppress the interfacial defects. However, these additional passivation agents often introduce chemical and structural instabilities, limiting the device lifetime. Here, we present a molecular engineering strategy utilizing a chiral series of C60-Furan-Sugar (CFS) fullerene derivatives blended with [6,6]-phenyl-C61-butyric acid methyl ester (PCBM) to modify the electron transport layer (ETL). The incorporation of CFSs significantly enhances the electron mobility and dielectric constant of the ETL, while their intrinsic passivation functionality effectively passivates perovskite surface defects. As a result, PSCs employing PCBM:CFS-RS blends achieve a power conversion efficiency (PCE) of 25.81% without the use of additional passivation layers and retain 95% of their initial performance after 1000 h of aging. Notably, CFS-RS is a chiral molecule bearing a side chain with R/S configurational isomers, which facilitates interfacial compatibility and contributes to the enhanced device performance. This work demonstrates that tuning the orientation of polar substituents in fullerene side chains can effectively influence the optoelectronic properties of the blended films, thereby simultaneously enhancing both efficiency and stability in PSCs