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Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network
Get PDFLiquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on experimental data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between experimental data and simulation
Nanocatalytic materials for energy-related small-molecules conversion: Active site design, identification and structure-performance relationship discovery
No full textConspectus
The catalytic conversion of energy-related small-molecules is a critical process in the fields of chemical production, environmental restoration, and energy conversion and storage. Over the years, numerous nanocatalytic materials have been explored in efforts to substantially boost the inherently sluggish catalytic processes. Despite achievements, the lack of fundamental insights into the design and identification of active sites and the structure???performance relationship has been one of the main obstacles to further improvement in catalytic performance. With the development of first-principles density functional theory (DFT) calculations and state-of-art spectroscopic techniques, the pace of research has started to move forward again.
In this Account, we illustrate our recent representative attempts to gain fundamental insights into the rational development of efficient nanocatalytic materials and thus boost the typical electrochemical and mechanochemical conversions of energy-related small-molecules, including for the hydrogen evolution reaction (HER), oxygen reduction reaction (ORR), and ammonia synthesis. DFT calculations and advanced spectroscopic techniques, such as synchrotron radiation-based X-ray absorption spectroscopy (XAS, hard and soft X-ray), were properly adopted for this purpose.
Specifically, to achieve a fast-electrochemical hydrogen evolution process, Ir active sites with balanced hydrogen adsorption/desorption behaviors were first computationally designed via orbital modulation and experimentally identified, and they showed significantly enhanced catalytic activity toward HER in acidic media. For the electrochemical reduction of oxygen, well-designed Zn???N2 active sites and quinone functional groups were introduced into the different carbon matrixes and structurally identified by the XAS technique, utilizing hard and soft X-rays, respectively. Both experimental and DFT studies revealed that Zn???N2 active sites with their unique structure can greatly activate the adsorbed oxygen species, leading to a highly efficient and selective four-electron oxygen reduction pathway, while the quinone functional groups are able to modify the activation mode and alter it into a selective two-electron oxygen reduction pathway for H2O2 production.
In another study, inspired by the dissociation of stable nitrogen molecules on the surface of Fe, dynamic strained Fe active sites were designed for mechanochemical ammonia synthesis. Combined XAS and M??ssbauer spectroscopy revealed the formation of a short-range Fe4N structure by the Fe active sites and dissociated nitrogen during the ball milling process, facilitating robust hydrogenation and ammonia production under mild conditions.
Thanks to the theoretical methods and advanced spectroscopic techniques, fundamental insights into the design and identification of active sites and understanding of the structure???performance relationship can be easily obtained using such tools, which will guide the development of nanocatalytic materials and boost the conversions of energy-related small-molecules for various applications
Rapid seismic damage-state assessment of steel moment frames using machine learning
No full textThe damage state assessment of buildings after an earthquake is an essential and urgent task that typically requires significant manpower and time for the resilience of a city-scale society. This study aims to develop machine learning (ML) models for the rapid seismic damage-state assessment of steel moment frames, which was never tried before to the authors??? knowledge. Eight ML models were examined for this purpose, including K-nearest neighbors, na??ve Bayes, decision tree, random forest (RF), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), light gradient boosting, and category boosting. The combination of 468 steel moment frames from the database in DesignSafe cyberinfrastructure and 240 ground motions yielded a total of 112,320 data points. The steel moment frames have a wide variety of geometric configurations (e.g., number of stories from 1 to 19, number of bays from 1 to 5, bay width from 6.1 to 12.19 m), and applied loads (i.e., three cases of dead load and two cases of live load). Nonlinear time history analyses were conducted using OpenSees to produce a comprehensive dataset for the training and testing of the ML models. A reliable procedure to define the damage states of steel moment frames was suggested based on pushover analysis. Damage states of steel moment frames were categorized following the tag definitions (i.e., green, yellow, and red) in ATC-20. Spectral accelerations at five selected periods (1, 2, 3, 4, and 5 s) for the given ground motions and at the first three natural periods of the steel frames were used as input variables for the ML models. From the results, the RF model is suggested for the prediction of the seismic damage states of steel moment frames. The RF model could accurately predict 98% of the assigned tags in the testing dataset. In contrast, the AdaBoost (88%) and na??ve Bayes (90%) models displayed the lowest performance. Among the four boosting methods considered, the XGBoost model (97%) exhibited the highest performance. Furthermore, Shapley additive explanations (SHAP) method was used to inspect the importance of input variables on the prediction. It was found that the spectral accelerations at 1 and 2 s strongly influence the prediction, likely because the first natural periods of the considered steel frames fall in the range of 1???2 s. Finally, to provide convenient access to engineers, a graphical user interface based on the developed RF model was created. This study places a pioneering step for the application of machine learning to the rapid damage assessment of building structures
Effects of mechanical properties of gelatin methacryloyl hydrogels on encapsulated stem cell spheroids for 3D tissue engineering
No full textCell spheroids are three-dimensional cell aggregates that have been widely employed in tissue engineering. Spheroid encapsulation has been explored as a method to enhance cell-cell interactions. However, the effect of hydrogel mechanical properties on spheroids, specifically soft hydrogels (<1 kPa), has not yet been studied. In this study, we determined the effect of encapsulation of stem cell spheroids by hydrogels crosslinked with different concentrations of gelatin methacryloyl (GelMA) on the functions of the stem cells. To this end, human adipose-derived stem cell (ADSC) spheroids with a defined size were prepared, and spheroid-laden hydrogels with various concentrations (5, 10, 15%) were fabricated. The apoptotic index of cells from spheroids encapsulated in the 15% hydrogel was high. The migration distance was five-fold higher in cells encapsulated in the 5% hydrogel than the 10% hydrogel. After 14 days of culture, cells from spheroids in the 5% hydrogel were observed to have spread and proliferated. Osteogenic factor and pro-angiogenic factor production in the 15% hydrogel was high. Collectively, our results indicate that the functionality of spheroids can be regulated by the mechanical properties of hydrogel, even under 1 kPa. These results indicate that spheroid-laden hydrogels are suitable for use in 3D tissue construction
Toward high-energy Mn-based disordered-rocksalt Li-ion cathodes
Get PDFThe recent development of high-capacity disordered-rocksalt (DRX) cathodes has ushered in new opportunities toward low-cost and high-energy Li-ion batteries. In particular, Mn-based DRX materials in which Mn serves as the primary redox-active transition metal have shown the most promising performance, with capacity and specific energy surpassing those of more established cathode materials. However, there remain critical challenges for these materials to become practical alternatives to conventional cathodes, such as limited cycling kinetics, which require pulverized particle morphology to achieve high capacity or poor capacity retention. Herein, we summarize the current understanding of the operating principles, failure mechanisms, synthesis and processing, microstructure, and performance of the Mn-based DRX materials. From this understanding, we perform a critical analysis of the challenges and opportunities toward high-energy Mn-based DRX for sustainable Li-ion batteries
Solid Electrolyte Interphase Layers by Using Lithiophilic and Electrochemically Active Ionic Additives for Lithium Metal Anodes
No full textThe use of role-assigned ionic additives with different adsorption energies and distinct electron-accepting abilities enables the construction of a multilayer solid electrolyte interphase (SEI) with a sequential structure of lithiophilic, mechanically robust, and ion-permeable layers on Li metal anodes. The uncontrollable Li dendrite formation, which is promoted by localized electric fields on the Li metal anode, is suppressed by the lithiophilic Ag-containing inner SEI and LiF + Li3N-enriched outer SEI with reduced overpotentials upon Li deposition
Self-Healable Organic-Inorganic Hybrid Thermoelectric Materials with Excellent Ionic Thermoelectric Properties
No full textSelf-healable and stretchable thermoelectric (TE) materials provide new possibilities for self-powered flexible wearable devices to self-repair mechanical damage. However, developing high-performance materials with such desirable TE and mechanical properties is a significant challenge. In this work, organic-inorganic ionic TE composites (OITCs) with an unprecedently high ionic TE figure of merit (ZT(i) = 3.74 at 80% relative humidity) and robust properties of simultaneous self-healing and stretching are reported. The OITCs are developed by incorporating inorganic SiO2 nanoparticles (SiO2-nps) in a polyaniline: poly(2-acrylamido-2-methyl-1-propanesulfonic acid): phytic acid (PANI:PAAMPSA:PA) ternary polymer. The incorporated SiO2-nps constructively interact with the hybrid polymer to provide autonomous self-healability and stretchability while augmenting the mobile proton concentration in OITCs, which substantially improves their ionic TE properties (i.e., ionic Seebeck coefficient and ionic conductivity). Moreover, the OITCs remain repeatedly stretchable and self-healable under severe external stresses (50 cycles of 100% strain and 25 cycles of cutting/healing) without degradation of their TE properties. Using the OITCs with multi-walled carbon nanotube electrodes, an ionic TE supercapacitor (ITESC) with a maximum energy density of 19.4 mJ m(-2) is demonstrated upon a temperature difference of 1.8 K
Specializing CGRAs for Light-Weight Convolutional Neural Networks
No full textDNN (Deep Neural Network) processing units, or DPUs, are one of the most energy-efficient platforms for DNN applications. However, designing new DPUs for every DNN model is very costly and time-consuming. In this paper we propose an alternative approach: to specialize coarse-grained reconfigurable architectures (CGRAs), which are already quite capable of delivering high performance and high energy efficiency for compute-intensive kernels. We identify a small set of architectural features on a baseline CGRA to enable high performance mapping of depthwise convolution (DWC) and pointwise convolution (PWC) kernels, which are the most important building block in recent light-weight DNN models. Our experimental results using MobileNets demonstrate that our proposed CGRA enhancement can deliver 8 18?? improvement in area-delay product depending on layer type, over a baseline CGRA with a state-of-the-art CGRA compiler. Moreover, our proposed CGRA architecture can also speed up 3D convolution with similar efficiency as previous work, demonstrating the effectiveness of our architectural features beyond depthwise separable convolution layers
