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Starving Free Solvents: Toward Immiscible Binary Liquid Electrolytes for Li-Metal Full Cells
No full textCurrent state-of-the-art Li batteries use single-phase electrolytes; however, these electrolytes often encounter difficulty in simultaneously fulfilling the nonidentical electrochemical requirements of cathodes and anodes. Here, a class of immiscible binary liquid electrolyte (BLE) is designed by starving free solvent molecules. Based on their electrochemical stability window, 1,2-dimethoxyethane (DME) and succinonitrile (SN) are selected as model solvents for Li-metal anodes and LiNi0.8Co0.1Mn0.1 (NCM811) cathodes, respectively. Li bis(fluorosulfonyl)imide (LiFSI), which promotes Li+ solvation (i.e., reduces free solvents), enables the phase separation of the miscible solvent mixture (SN-DME), and an increase in its concentration strengthens the coordination of Li+-FSI- in the solvation sheath, thus yielding (anion-derived) fluorine-rich electrode-electrolyte interphases. The resulting BLE allows 4.4 V Li-metal full cells to exhibit a stable capacity retention under a constrained cell condition (Li (20 mu m, 4.1 mAh cm(-2))||NCM811 (3.8 mAh cm(-2)), N (negative)/P (positive) capacity ratio = 1.08), which exceed those of previously reported binary liquid electrolytes
Multifunctional porous organic polymers as ideal platforms for gas uptake, metal-ions sensing, and cell imaging
No full textPorous organic polymers (POPs) possess versatile advantages, such as light weight, excellent stability, and high porosity. Hence, POPs could be ideal functional platforms. Herein, two multifunctional M-POPs were prepared via the Schiff-base reaction. They possessed good microporosity, remarkable stability, high-density N/O atoms, and excellent luminescence. Interestingly, the M-POPs achieved a good carbon dioxide-capture value of 17.1 wt% at 273 K and 1 bar, and provided excellent H2 capture of 1.0 wt% at 77 K and 1 bar. The M-POPs had many hydroxyl groups and nitrogen on the walls, which aided the formation of metal-coordination sites. The M-POPs had an excellent capacity for the detection of metal ions that was accurate, selective, and sensitive. Furthermore, phospholipid-coated M-POPs could be used to detect metal ions in living cells. These results provide a new design for multifunctional POPs. In this research, we synthesized multifunctional porous organic polymers as an ideal platform for gas uptake, metal ions sensing, and cell image
Development of Machine Learning Model for Neutron Clustering Classification in Monte Carlo Criticality Simulation
No full textThis study presents the ongoing development of a machine learning model as a diagnostic tool to identify particle cluster(s) formation during Monte Carlo criticality simulations. The particle distribution is essentially spatial-temporal features???evolves in space and simulation cycle???and can be directly extracted from Monte Carlo simulation for machine-learning model training without the use of intermediary/silhouette metrics???potentially losing information. The machine learning model uses a combination of convolutional neural network and long short-term memory (Many-to-One variant) to map the spatial-temporal features into higher time-independent features for the classification task. The model was trained with synthetic dataset generated by 3D Gauss distribution to mimic particle cluster(s) formation, and later evaluated with the test set generated from OpenMC simulations. The model achieves an accuracy of 99% for the synthetic test set but only 11% for the OpenMC dataset, revealing the limitations of current features extraction method. Furthermore, the direct use of particle distribution as spatial-temporal features is not viable for a large-scale machine learning model due to the prohibitively large dataset space requirement, so potential spatial-temporal features are discussed for further study
Real flue gas CO2 hydrogenation to formate by an enzymatic reactor using O2- and CO-tolerant hydrogenase and formate dehydrogenase
No full textIt is challenging to capture carbon dioxide (CO2), a major greenhouse gas in the atmosphere, due to its high chemical stability. One potential practical solution to eliminate CO2 is to convert CO2 into formate using hydrogen (H2) (CO2 hydrogenation), which can be accomplished with inexpensive hydrogen from sustainable sources. While industrial flue gas could provide an adequate source of hydrogen, a suitable catalyst is needed that can tolerate other gas components, such as carbon monoxide (CO) and oxygen (O2), potential inhibitors. Our proposed CO2 hydrogenation system uses the hydrogenase derived from Ralstonia eutropha H16 (ReSH) and formate dehydrogenase derived from Methylobacterium extorquens AM1 (MeFDH1). Both enzymes are tolerant to CO and O2, which are typical inhibitors of metalloenzymes found in flue gas. We have successfully demonstrated that combining ReSH- and MeFDH1-immobilized resins can convert H2 and CO2 in real flue gas to formate via a nicotinamide adenine dinucleotide-dependent cascade reaction. We anticipated that this enzyme system would enable the utilization of diverse H2 and CO2 sources, including waste gases, biomass, and gasified plastics
Machine-learning models to predict P-and S-wave velocity profiles for Japan as an example
No full textWave velocity profiles are significant for various fields, including rock engineering, petroleum engineering, and earthquake engineering. However, direct measurements of wave velocities are often constrained by time, cost, and site conditions. If wave velocity measurements are unavailable, they need to be estimated based on other known proxies. This paper proposes machine learning (ML) approaches to predict the compression and shear wave velocities (VP and VS, respectively) in Japan. We utilize borehole databases from two seismograph networks of Japan: Kyoshin Network (K-NET) and Kiban Kyoshin Network (KiK-net). We consider various factors such as depth, N-value, density, slope angle, elevation, geology, soil/rock type, and site coordinates. We use three ML techniques: Gradient Boosting (GB), Random Forest (RF), and Artificial Neural Network (ANN) to develop predictive models for both VP and VS and evaluate the performances of the models based on root mean squared errors and the five-fold cross-validation method. The GB-based model provides the best estimation of VP and VS for both seismograph networks. Among the considered factors, the depth, standard penetration test (SPT) N-value, and density have the strongest influence on the wave velocity estimation for K-NET. For KiK-net, the depth and site longitude have the strongest influence
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No full textRaphe-driven feed-forward inhibition of the hippocampus by glutamate co-transmission
No full textRaphe nuclei are clusters of heterogeneous cell populati ons comprised of serotonergic, dopaminergic, glutamatergic, and GABAergic neurons.Investi gati ons about the modulati on by raphe nuclei have primarily concentrated on their serotonergic slow synapti c transmission since the majorityof the neuronal populati on in the raphe nuclei is serotonergic. However, accumulati ng evidence suggests the emerging role of GABA- or glutamate-mediated fast synapti c transmission by raphe neurons. Especially, glutamate is co-transmitt ed by serotonergic neurons in the diverse brain regionsincluding the hippocampus, amygdala, and VTA. Interesti ngly, glutamatergic transmission mediated by the raphe nuclei tends to modulate inhibitoryneurons rather than excitatory neurons in the amygdala and hippocampus. This evidence suggests that raphe-mediated fast excitatory transmissionmay be mainly converted to inhibitory tone in the target regions. In this study, we focused on the inhibitory eff ect of raphe-mediated fast synapti ctransmission in the major targets of the raphe nuclei. Using optogeneti c approaches, immunohistochemistry, and enhanced confocal imaging, wefound that multi ple brain regions receive disynapti c inhibitory inputs from raphe serotonergic neurons, among which the hippocampus is functi onallythe most impacted region by raphe-mediated feed-forward inhibiti on. In additi on, we discovered that this feed-forward inhibiti on is mediated by theglutamatergic transmission of the raphe neurons. Most notably, raphe-driven feed-forward inhibiti on was able to modulate synapti c transmission atSchaff er collateral-CA1 synapses in the hippocampus. Our fi ndings demonstrate the functi onal signifi cance of raphe-mediated fast synapti ctransmission and provide new insights into the complex synapti c connecti vity of raphe serotonergic neurons
