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At least 145 records · Page 8

A phosphorylation-deficient ribosomal protein eS6 is largely functional in Arabidopsis thaliana , rescuing mutant defects from global translation and gene expression to photosynthesis and growth

The eukaryote-specific ribosomal protein of the small subunit eS6 is phosphorylated through the target of rapamycin (TOR) kinase pathway. Although this phosphorylation event responds dynamically to environmental conditions and has been studied for over 50 years, its biochemical and physiological significance remains controversial and poorly understood. Here, we report data from Arabidopsis thaliana, which indicate that plants expressing only a phospho-deficient isoform of eS6 grow essentially normally under laboratory conditions. The eS6z (RPS6A) paralog of eS6 functionally rescued a double mutant in both rps6a and rps6b genes when expressed at approximately twice the wild-type dosage. A mutant isoform of eS6z lacking the major six phosphorylatable serine and threonine residues in its carboxyl-terminal tail also rescued the lethality, rosette growth, and polyribosome loading of the double mutant. This isoform also complemented many mutant phenotypes of rps6 that were newly characterized here, including photosynthetic efficiency, and most of the gene expression defects that were measured by transcriptomics and proteomics. However, compared with plants rescued with a phospho-enabled version of eS6z, the phospho-deficient seedlings retained a mild pointed-leaf phenotype, root growth was reduced, and certain cell cycle-related mRNAs and ribosome biogenesis proteins were misexpressed. The residual defects of the phospho-deficient seedlings could be understood as an incomplete rescue of the rps6 mutant defects. There was little or no evidence for gain-of-function defects. As previously published, the phospho-deficient eS6z also rescued the rps6a and rps6b single mutants; however, phosphorylation of the eS6y (RPS6B) paralog remained lower than predicted, further underscoring that plants can tolerate phospho-deficiency of eS6 well. Our data also yield new insights into how plants cope with mutations in essential, duplicated ribosomal protein isoforms.

60 APPLIED LIFE SCIENCES↗

Investigation of thermal damage in explosive bridgewire detonators via discrete element method simulations

Exploding bridgewire (EBW) detonators are used to rapidly and reliably initiate energetic reactions by exploding a bridgewire via Joule heating. While the mechanisms of EBW detonators have been studied extensively in nominal conditions, comparatively few studies have addressed thermally damaged detonator operability. We present a mesoscale simulation study of thermal damage in a representative EBW detonator, using discrete element method (DEM) simulations that explicitly account for individual particles in the pressed explosive powder. We use a simplified model of melting, where solid spherical particles undergo uniform shrinking, and fluid dynamics are ignored. The subsequent settling of particles results in the formation of a gap between the solid powder and the bridgewire, which we study under different conditions. In particular, particle cohesion has a significant effect on gap formation and settling behavior, where sufficiently high cohesion leads to coalescence of particles into a free-standing pellet. This behavior is qualitatively compared to experimental visualization data, and simulations are shown to capture several key changes in pellet shape. We derive a minimum and maximum limit on gap formation during melting using simple geometric arguments. In the absence of cohesion, results agree with the maximum gap size. With increasing cohesion, the gap size decreases, eventually saturating at the minimum limit. In conclusion, we present results for different combinations of interparticle cohesion and detonator orientations with respect to gravity, demonstrating the complex behavior of these systems and the potential for DEM simulations to capture a range of scenarios.

cohesive powders↗

Morphological–Electrical Property Relation in Cu(In,Ga)(S,Se) 2 Solar Cells: Significance of Crystal Grain Growth and Band Grading by Potassium Treatment

Abstract Solution‐processed Cu(In,Ga)(S,Se) 2 (CIGS) has a great potential for the production of large‐area photovoltaic devices at low cost. However, CIGS solar cells processed from solution exhibit relatively lower performance compared to vacuum‐processed devices because of a lack of proper composition distribution, which is mainly instigated by the limited Se uptake during chalcogenization. In this work, a unique potassium treatment method is utilized to improve the selenium uptake judiciously, enhancing grain sizes and forming a wider bandgap minimum region. Careful engineering of the bandgap grading structure also results in an enlarged space charge region, which is favorable for electron–hole separation and efficient charge carrier collection. Besides, this device processing approach has led to a linearly increasing electron diffusion length and carrier lifetime with increasing the grain size of the CIGS film, which is a critical achievement for enhancing photocurrent yield. Overall, 15% of power conversion efficiency is achieved in solar cells processed from environmentally benign solutions. This approach offers critical insights for precise device design and processing rules for solution‐processed CIGS solar cells.

Kim, Joo‐Hyun↗

Redox‐Functionalized Semiconductor Interfaces for Photoelectrochemical Separations

Redox-mediated electrosorption is a promising platform for selective electrochemical (EC) separations, due to its molecular selectivity, high uptake, and tunability for target ions. However, the electrical energy required is mainly generated by non-renewable energy sources, which limits its sustainability and overall impact to decarbonization. Here, a redox-mediated photoelectrochemical (PEC) separation process using polyvinyl ferrocene functionalized TiO 2 nanorod electrodes is proposed, which integrates direct solar energy as a driver for the selective electrosorption. The photoelectrochemically-driven oxidation and reduction with both homogeneous and heterogeneous ferrocene-systems is investigated to establish the underlying mechanism. The PEC system can separate heavy metal oxyanions at lower voltages or even without electrical energy. At 0.3 V versus SCE, a 124 mg g −1 uptake for Mo is achieved, which is comparable to the performance of EC cells at 0.75 V versus SCE. Thus, PEC systems not only can generate energy for spontaneous redox-separations, but also can reduce electrical energy consumption by 51.4% compared to EC cells for separation processes when coupled with an external electrical energy.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Synthesis of Microscopic 3D Graphene for High‐Performance Supercapacitors with Ultra‐High Areal Capacitance

Abstract Despite graphene being considered an ideal supercapacitor electrode material, its use in commercial devices is limited because few methods exist to produce high‐quality graphene at a large scale and low cost. A simple method is reported to synthesize 3D graphene by graphenization of coal tar pitch with a K 2 CO 3 catalyst. This produces 3D graphenes with high specific surface areas up to 2113 m 2 g −1 and exceptional crystallinity (Raman I D / I G as low as ≈0.15). The material has an outstanding specific capacitance of 182.6 F g −1 at a current density of 1.0 A g −1 . This occurs at a mass loading of 30 mg cm −2 which is 3 times higher than commercial requirements, yielding an ultra‐high areal capacitance of 5.48 F cm −2 . The K 2 CO 3 is recycled and reused over 10 cycles with material quality and electrocapacitive performance of 3D graphene retained and verified after each cycle. The synthesis method and resulting electrocapacitive performance properties create new opportunities for using 3D graphene more broadly in practical supercapacitor devices.

36 MATERIALS SCIENCE↗

Semi-supervised permutation invariant particle-level anomaly detection

The development of analysis methods to distinguish potential beyond the Standard Model phenomena in a model-agnostic way can significantly enhance the discovery reach in collider experiments. However, the typical machine learning (ML) algorithms employed for this task require fixed length and ordered inputs that break the natural permutation invariance in collision events. To address this, a semi-supervised anomaly detection tool is presented that takes a variable number of particle-level inputs and leverages a signal model to encode this information into a permutation invariant, event-level representation via supervised training with a Particle Flow Network (PFN). Data events are then encoded into this representation and given as input to an autoencoder for unsupervised ANomaly deTEction on particLe flOw latent sPacE (ANTELOPE), classifying anomalous events based on a low-level and permutation invariant input modeling. Performance of the ANTELOPE architecture is evaluated on simulated samples of hadronic processes in a high energy collider experiment, showing good capability to distinguish disparate models of new physics.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Numerical Errors in Ice Microphysics Parameterizations and their Effects on Simulated Regional Climate

The major characteristics of ice microphysics in Weather Research and Forecasting (WRF) Double-Moment 6-class (WDM6) bulk-type cloud microphysics originate from the diagnosed ice number concentration, which is a function of the cloud-ice mixing ratio. In this study, we correct numerical errors in ice microphysics processes of the WDM6, in which the cloud-ice shape is assumed as single bullets and examine the impact on regional climate simulations. Here, by rederiving the relationships between cloud microphysics characteristics, including the one linking the cloud-ice mixing ratio and number concentration, we remove numerical errors intrinsic to the description of cloud-ice characteristics in the original WDM6 microphysics scheme. The revised WDM6 is tested using a WRF framework for regional climate simulations over the East Asian region. We find that our correction to the WDM6 improves the model’s performance in capturing the observed distribution of the monsoon rain band. A reduction in cloud ice is significant in the revised WDM6, which strengthens the Western North Pacific High. By conducting the additional sensitivity experiment in which the characteristics of cloud-ice shape are revised as the one for the column type, our study also finds out that the impacts of the existing numerical errors on the simulated monsoon is as large as the ones of the changes in cloud-ice shape.

54 ENVIRONMENTAL SCIENCES↗

Pathway to Decarbonization Through Industrial Energy Efficiency: Micro and Macro Perspectives from Compressed Air Usage

Abstract Energy audits directly provided the industrial sector with reduced energy costs and avoided emissions. Still, they also lead to far-reaching indirect and induced local, regional, and national benefits. This paper aims to present the techno-economic-environmental analysis to achieve decarbonization through implementing industrial energy efficiency at micro and macro levels. An integrated techno-economic-environmental methodology is developed. Case studies of micro-level carbon reduction efforts through industrial energy efficiency technologies are presented. The broader macroeconomic and environmental effects of technology on society are analyzed using data from 206 energy audits of industrial compressed air systems conducted over 13 years. The impacts show that energy-efficient improvements lead to direct cost savings for manufacturers, boost economic activity across sectors, and affect carbon dioxide emissions both short-term and long-term in the region. Given their extensive benefits, energy audits significantly influence policymaking. We devised a methodology to link micro-level energy audit data with macroeconomic and environmental analyses to quantify these cascading benefits. The economic scenario analysis shows that $228 M has been saved from direct industrial energy savings from implementing all compressed air recommendations in the studied periods and the region. In addition, the investment made through manufacturers would create 2,025 jobs and $383 M annually, cascading regional economic impacts. The environmental analysis shows that the regional manufacturers have directly avoided about 2.8 M metric tons of carbon dioxide emissions.

Engineering↗

Change of Electrical and Transport Properties of Nickel Oxide by Carrier Concentration and Temperature through First-Principle Calculations

Abstract Nickel is typically used as one of the main components in electrical contact devices or connectors. Nickel oxide (NiO) is usually formed on the surfaces of electrodes and can negatively impact system performance by introducing electrical contact resistance. The thermal, electrical, and transport properties of NiO, as a Mott insulator or a p-type semiconductor, can be altered by operating and environmental conditions such as temperature and stress/strain by contact. In this study, we investigate the fundamental material properties of NiO through the first-principle calculations. First, we obtain and compare the lattice parameter, magnetic moment, and electronic structure for NiO via the WIEN2K simulations with four different potentials (i.e., GGA, GGA + U, LSDA, and LSDA + U). Then, using the WIEN2K simulation results with LSDA + U potential that produces a highly accurate bandgap for NiO, we calculate the electrical conductivity and electrical part of the thermal conductivity of nickel and NiO as a function of temperature and carrier concentration through the BoltzTraP simulations. Systematic simulation results revealed that the electrical conductivity relative to the relaxation time for NiO increases with the carrier concentration, while it shows a slightly decreasing trend with temperature under a fixed carrier concentration. By contrast, the electrical part of the thermal conductivity shows an increasing trend considering carrier concentration and temperature.

Shah, Nayem Md. Reza↗

Semi-automatic image annotation using 3D LiDAR projections and depth camera data

Efficient image annotation is necessary to utilize deep learning object recognition neural networks in nuclear safeguards, such as for the detection and localization of target objects like nuclear material containers (NMCs). This capability can help automate the inventory accounting of different types of NMCs within nuclear storage facilities. The conventional manual annotation process is labor-intensive and time-consuming, hindering the rapid deployment of deep learning models for NMC identifications. This paper introduces a novel semi-automatic method for annotating 2D images of nuclear material containers (NMCs) by combining 3D light detection and ranging (LiDAR) data with color and depth camera images collected from a handheld scan system. The annotation pipeline involves an operator manually marking new target objects on a LiDAR-generated map, and projecting these 3D locations to images, thereby automatically creating annotations from the projections. The semi-automatic approach significantly reduces manual efforts and the expertise in image annotation that is required to perform the task, allowing deep learning models to be trained on-site within a few hours. The paper compares the performance of models trained on datasets annotated through various methods, including semi-automatic, manual, and commercial annotation services. The evaluation demonstrates that the semi-automatic annotation method achieves comparable or superior results, with a mean average precision (mAP) above 0.9, showcasing its efficiency in training object recognition models. Additionally, the paper explores the application of the proposed method to instance segmentation, achieving promising results in detecting multiple types of NMCs in various formations.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

Role of generic scale invariance in a Mott transition from a U(1) spin-liquid insulator to a Landau Fermi-liquid metal

Highlights: • We look at the role played by generic scale invariance (GSI) in the phase transition between a U(1) spin liquid and a Fermi liquid. • We identify dangerously irrelevant operators in the renormalization group transformation, involved with the GSI. • We highlight novel mean-field dynamics of this Mott quantum criticality, resulting from the GSI. We investigate the role of generic scale invariance in a Mott transition from a U(1) spin-liquid insulator to a Landau Fermi-liquid metal, where there exist massless degrees of freedom in addition to quantum critical fluctuations. Here, the Mott quantum criticality is described by critical charge fluctuations, and additional gapless excitations are U(1) gauge-field fluctuations coupled to a spinon Fermi surface in the spin-liquid state, which turn out to play a central role in the Mott transition. An interesting feature of this problem is that the scaling dimension of effective leading local interactions between critical charge fluctuations differs from that of the coupling constant between U(1) gauge fields and matter-field fluctuations in the presence of a Fermi surface. As a result, there appear dangerously irrelevant operators, which can cause conceptual difficulty in the implementation of renormalization group (RG) transformations. Indeed, we find that the curvature term along the angular direction of the spinon Fermi surface is dangerously irrelevant at this spin-liquid Mott quantum criticality, responsible for divergence of the self-energy correction term in U(1) gauge-field fluctuations. Performing the RG analysis in the one-loop level based on the dimensional regularization method, we reveal that such extremely overdamped dynamics of U(1) gauge-field fluctuations, which originates from the emergent one-dimensional dynamics of spinons, does not cause any renormalization effects to the effective dynamics of both critical charge fluctuations and spinon excitations. However, it turns out that the coupling between U(1) gauge-field fluctuations and both matter-field excitations still persists at this Mott transition, which results in novel mean-field dynamics to explain the nature of the spin-liquid Mott quantum criticality. We discuss physical implications of effective one-dimensional spin dynamics and extremely overdamped gauge dynamics at the Mott quantum criticality.

36 MATERIALS SCIENCE↗

Origins of wear-induced tungsten corrosion defects in semiconductor manufacturing during tungsten chemical mechanical polishing

During chemical mechanical polishing (CMP) process optimization, a unique corrosion defect was identified in the tungsten (W) contact plugs, which were investigated to develop practical solutions to improve CMP mass productivity. The corrosion defect occurred in the absence of external incident energy and was observed to occur only at the W plugs connected to the P + /n-well close to the wafer edge. To examine the root cause of the corrosion, a series of experiments was carried out at each W CMP step, using in-line defect inspection. Additional experiments were conducted using on-line inductively coupled plasma mass spectrometry (ICP-MS) to understand pH- and potential-dependent electrochemical dissolution behaviors of W. Experimentally, W corrosion was confirmed to be exacerbated if the surface oxide of the W plugs is removed by the deionized water (DIW) rinsing step under wafer pressurization, then exposed to DIW and alkaline cleaning solution. Although the DIW rinsing step is commonly performed to clean polishing pads right after the main polishing, a small amount of applied pressure (0.5 psi) without slurry supply can lead to corrosion defects. Finally, this wear-induced corrosion of the W plugs can be prevented by removing the applied pressure during the post DIW rinsing step of W CMP.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Uncertainty quantification and optimization of precipitating hydrometeor parameters for winter precipitation in a cloud microphysics scheme

The precipitating hydrometeor parameters used in cloud microphysics schemes carry inherent uncertainties. The quantification of these uncertainties, together with parameter optimization, can significantly improve precipitation forecasts. This study investigates the effects of 13 parameters in the Weather Research and Forecasting (WRF) Double-Moment 6-class (WDM6) microphysics scheme, which define the hydrometeor characteristics such as fall velocity–diameter and mass–diameter relationships, as well as the shape parameter of the drop size distribution for precipitating particles such as rain, snow, and graupel on simulated winter precipitation. A comparison between the model's pre-defined parameters and observations from the International Collaborative Experiments for the PyeongChang 2018 Olympic and Paralympic winter games (ICE-POP 2018) field campaign reveals that the fall velocity–diameter relationship for rain, the mass–diameter relationships for snow and graupel, and the shape parameters for all precipitating particles in the WDM6 scheme deviate from the median values observed by the two-dimensional video disdrometer (2DVD). To quantify parameter sensitivities, a perturbed parameter ensemble (PPE) of 256 simulations was conducted within parameter ranges constrained by 2DVD observations for three winter precipitation cases. Bayesian optimization was then applied to identify parameter sets that minimized the root mean square error (RMSE) for each case, achieving reductions of up to 30.2 %. These results demonstrate that ensemble-based uncertainty quantification and parameter optimization can help identify key parameters and provide a pathway to improving precipitation simulation performance. In addition, measurement sites can be strategically selected based on regions that show high sensitivity to variations in hydrometeor characteristic parameters.

Bayesian optimization↗

Electronic structure and charge-density wave transition in monolayer $\mathrm{VS}_2$

Vanadium disulfide (VS 2 ) attracts elevated interests for its charge-density wave (CDW) phase transition, ferromagnetism, and catalytic reactivity, but the electronic structure of monolayer has not been well understood yet. Here we report synthesis of epitaxial 1T VS 2 monolayer on bilayer graphene grown by molecular-beam epitaxy (MBE). Angle-resolved photoemission spectroscopy (ARPES) measurements reveal that Fermi surface with six elliptical pockets centered at the M points shows gap opening at low temperature. Temperature-dependence of the gap size suggests existence of CDW phase transition above room temperature. Here, our observations provide important evidence to understand the strongly correlated electron physics and the related surface catalytic properties in two-dimensional transition-metal dichalcogenides (TMDCs).

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗