15 Search Results

Automation of electrochemical measurements can accelerate the discovery of new electroactive materials. One of the hurdles to automated electrochemical measurement is the pretreatment of electrodes because mechanical polishing is usually conducted manually. Here we investigate the automation of electrochemical measurements using a robotic arm. We demonstrate automated mechanical polishing using a station with a moving polishing pad and evaluate the effect of different polishing patterns. Our automatic method improved the corroded electrodes, and we found the effect of pattern was not significant, which diverges from the current common belief amongst practitioners that a figure eight pattern is best for pretreatment.more » This research is a step toward automating electrochemistry experiments without human intervention.« less

Abstract Electrostatic capacitors play a crucial role as energy storage devices in modern electrical systems. Energy density, the figure of merit for electrostatic capacitors, is primarily determined by the choice of dielectric material. Most industry-grade polymer dielectrics are flexible polyolefins or rigid aromatics, possessing high energy density or high thermal stability, but not both. Here, we employ artificial intelligence (AI), established polymer chemistry, and molecular engineering to discover a suite of dielectrics in the polynorbornene and polyimide families. Many of the discovered dielectrics exhibit high thermal stability and high energy density over a broad temperature range. One such dielectric displaysmore » an energy density of 8.3 J cc ⁻¹ at 200 °C, a value 11 × that of any commercially available polymer dielectric at this temperature. We also evaluate pathways to further enhance the polynorbornene and polyimide families, enabling these capacitors to perform well in demanding applications (e.g., aerospace) while being environmentally sustainable. These findings expand the potential applications of electrostatic capacitors within the 85–200 °C temperature range, at which there is presently no good commercial solution. More broadly, this research demonstrates the impact of AI on chemical structure generation and property prediction, highlighting the potential for materials design advancement beyond electrostatic capacitors.« less

In recent years, self-driving laboratories (SDLs) have emerged as a powerful tool to expedite various areas of chemical research. For optimal functionality, these laboratories must be adaptable, readily modifying configurations to meet researchers' specific needs. Despite these advances, much of chemistry still depends on proprietary equipment from specialized vendors, which can be restrictive and difficult to customize for diverse lab setups. Moreover, ensuring reproducibility requires full disclosure of equipment details. In this work, we introduce an automated system featuring a cost-effective, self-designed potentiostat and a straightforward synthesis platform. We provide complete transparency by disclosing the electronic schematics of the potentiostatmore » and the software used in the system. Our aim is to reduce the barriers to entry for SDLs and promote the principles of open science.« less

Self-driving laboratories (SDLs) promise an accelerated application of the scientific method. Through the automation of experimental workflows, along with autonomous experimental planning, SDLs hold the potential to greatly accelerate research in chemistry and materials discovery. This review provides an in-depth analysis of the state-of-the-art in SDL technology, its applications across various scientific disciplines, and the potential implications for research and industry. This review additionally provides an overview of the enabling technologies for SDLs, including their hardware, software, and integration with laboratory infrastructure. Most importantly, this review explores the diverse range of scientific domains where SDLs have made significant contributions, frommore » drug discovery and materials science to genomics and chemistry. We provide a comprehensive review of existing real-world examples of SDLs, their different levels of automation, and the challenges and limitations associated with each domain.« less

C4F7N [2,3,3,3-tetrafluoro-2-(trifluoromethyl)propanenitrile]/CO2 gas mixtures are being developed as an eco-friendly electrical insulator to replace SF6, the most potent greenhouse industrial gaseous dielectric. However, recent studies have reported complicated and often conflicting decomposition pathways for C4F7N/CO2 gas mixtures, which has raised concerns. In this work, the decomposition characteristics of C4F7N/CO2 gas mixtures were studied comprehensively by both designed computations and experiments. Computations were performed starting from fundamental propositions of C4F7N/CO2 decompositions, which were further experimentally verified by pyrolysis, long-term thermal aging with/without catalytic materials (industrial-grade molecular sieves 4A), and electrical decomposition by spark discharge. The results of both computations and experimentsmore » suggest that in an ideal thermal decomposition, C4F7N is likely to decompose into C2F6 and small fluoronitriles first at high temperatures. The generation of C3F6 and C2N2 from C4F7N thermal decomposition at lower temperatures appears because of the catalytic effect of incompatible materials, for example, the industrial-grade molecular sieves 4A that we tested. The electron impact dissociation of C4F7N plays an important role in C4F7N electrical decomposition, leading to additional formation of distinctive small molecules of CF4 and C2N2 of low concentrations. It was pointed out based on a real arcing test in a load disconnector that the decomposition of C4F7N gas mixtures in real applications will be at a much moderate and manageable rate than what was obtained from the highly accelerated laboratory tests presented in this work. The signatures of decomposition products extracted in this study provide invaluable guidance for developing decomposition-based diagnosis and fixation of decomposition byproducts toward SF6-free power grids.« less

CoCrFeMnNi high-entropy alloy (HEA) was additively manufactured (AM) by laser powder-bed fusion (L-PBF). The AM CoCrFeMnNi has prominent cellular dislocation structures with a small number of Mn-rich oxides. The thermal stability of the AM CoCrFeMnNi was investigated by isochronal annealing treatment at various temperatures from 400 to 1300°C for 1h. Microstructural analysis shows slow dislocation recovery, retarded recrystallization process, and precipitation of additional Cr-Mn based oxides during thermal annealing, resulting in exceptional thermal stability and retained high hardness at elevated temperatures. Further, by thermodynamic calculations, a low stored energy of 1.31 MJ/m³ and a high activation energy of 353 kJ/molmore » for recrystallization were estimated for the AM CoCrFeMnNi. The exceptional thermal stability of the AM CoCrFeMnNi HEA is mechanistically attributed to the low crystallographic misorientations across the dislocation cell walls, sluggish atomic diffusion, and the pinning effects of the oxide nanoprecipitates.« less

Polydimethylsiloxane (PDMS) is one of the most widely used polymeric materials for sealants, adhesives, lubricants, and thermal as well as electrical insulation. At low temperatures, however, PDMS is subject to crystallization that can cause deterioration in mechanical function. A common way to suppress such crystallization is through the incorporation of phenylsiloxane into the backbone of polysiloxane. Nevertheless, the introduction of phenyl components, even in small quantities, could potentially change the properties of the siloxane in a significant way. In this work, a series of mechanical tests and finite element simulations were performed to study the macroscale viscoelasticity of two poly(dimethyl-co-diphenyl)siloxanemore » formulations in order to understand the effects of a few percent diphenyl contents on the viscoelasticity of the polysiloxane material. Here, we utilized the small-angle X-ray scattering to investigate the microscopic structures of the copolymers and broadband dielectric spectroscopy and rheology to probe the chain dynamics at the microscale. The results of these characterizations were used to inform the finite element simulations. We found that the degree of cross-linking does not significantly alter the microstructure but can profoundly affect the viscoelastic response of the copolymer networks. The corresponding hysteretic behavior is interpreted in terms of reptation-like motion and relaxation of the effective free chains in the cured polymer network. The relaxation of the copolymer chains is slowed significantly by even a small increase in the molar ratio of the diphenyl component.« less

Traffic flow forecasting is an essential task of an intelligent transportation system (ITS), closely related to intelligent transportation management and resource scheduling. Dynamic spatial-temporal dependencies in traffic data make traffic flow forecasting to be a challenging task. Most existing research cannot model dynamic spatial and temporal correlations to achieve well-forecasting performance. The multi-head self-attention mechanism is a valuable method to capture dynamic spatial-temporal correlations, and combining it with graph convolutional networks is a promising solution. Therefore, we propose a multi-head self-attention spatiotemporal graph convolutional network (MSASGCN) model. It can effectively capture local correlations and potential global correlations of spatial structures,more » can handle dynamic evolution of the road network, and, in the time dimension, can effectively capture dynamic temporal correlations. Experiments on two real datasets verify the stability of our proposed model, obtaining a better prediction performance than the baseline algorithms. The correlation metrics get significantly reduced compared with traditional time series prediction methods and deep learning methods without using graph neural networks, according to MAE and RMSE results. Compared with advanced traffic flow forecasting methods, our model also has a performance improvement and a more stable prediction performance. We also discuss some problems and challenges in traffic forecasting.« less

Blockchain distributed ledger technology is just starting to be adopted in genomics and healthcare applications. Despite its increased prevalence in biomedical research applications, skepticism regarding the practicality of blockchain technology for real-world problems is still strong and there are few implementations beyond proof-of-concept. We focus on benchmarking blockchain strategies applied to distributed methods for sharing records of gene-drug interactions. We expect this type of sharing will expedite personalized medicine. We generated gene-drug interaction test datasets using the Clinical Pharmacogenetics Implementation Consortium (CPIC) resource. We developed three blockchain-based methods to share patient records on gene-drug interactions: Query Index, Index Everything, andmore » Dual-Scenario Indexing. We achieved a runtime of about 60 s for importing 4,000 gene-drug interaction records from four sites, and about 0.5 s for a data retrieval query. Our results demonstrated that it is feasible to leverage blockchain as a new platform to share data among institutions.« less

Summary We aimed to identify genomic traits of transitions to ectomycorrhizal ecology within the Boletales by comparing the genomes of 21 symbiotrophic species with their saprotrophic brown‐rot relatives. Gene duplication rate is constant along the backbone of Boletales phylogeny with large loss events in several lineages, while gene family expansion sharply increased in the late Miocene, mostly in the Boletaceae. Ectomycorrhizal Boletales have a reduced set of plant cell‐wall‐degrading enzymes (PCWDEs) compared with their brown‐rot relatives. However, the various lineages retain distinct sets of PCWDEs, suggesting that, over their evolutionary history, symbiotic Boletales have become functionally diverse. A smaller PCWDEmore » repertoire was found in Sclerodermatineae. The gene repertoire of several lignocellulose oxidoreductases (e.g. laccases) is similar in brown‐rot and ectomycorrhizal species, suggesting that symbiotic Boletales are capable of mild lignocellulose decomposition. Transposable element (TE) proliferation contributed to the higher evolutionary rate of genes encoding effector‐like small secreted proteins, proteases, and lipases. On the other hand, we showed that the loss of secreted CAZymes was not related to TE activity but to DNA decay. This study provides novel insights on our understanding of the mechanisms influencing the evolutionary diversification of symbiotic boletes.« less