Energy Map Research Articles

Simulation Study of High-Precision Characterization of MeV Electron Interactions for Advanced Nano-Imaging of Thick Biological Samples and Microchips

The resolution of a mega-electron-volt scanning transmission electron microscope (MeV-STEM) is primarily governed by the properties of the incident electron beam and angular broadening effects that occur within thick biological samples and microchips. A precise understanding and mitigation of these constraints require detailed knowledge of beam emittance, aberrations in the STEM column optics, and energy-dependent elastic and inelastic critical angles of the materials being examined. This simulation study proposes a standardized experimental framework for comprehensively assessing beam intensity, divergence, and size at the sample exit. This framework aims to characterize electron-sample interactions, reconcile discrepancies among analytical models, and validate Monte Carlo (MC) simulations for enhanced predictive accuracy. Our numerical findings demonstrate that precise measurements of these parameters, especially angular broadening, are not only feasible but also essential for optimizing imaging resolution in thick biological samples and microchips. By utilizing an electron source with minimal emittance and tailored beam characteristics, along with amorphous ice and silicon samples as biological proxies and microchip materials, this research seeks to optimize electron beam energy by focusing on parameters to improve the resolution in MeV-STEM/TEM. This optimization is particularly crucial for in situ imaging of thick biological samples and for examining microchip defects with nanometer resolutions. Our ultimate goal is to develop a comprehensive mapping of the minimum electron energy required to achieve a nanoscale resolution, taking into account variations in sample thickness, composition, and imaging mode.

Origin of the intermolecular forces that produce donor–acceptor stacks in π-conjugated charge-transfer complexes

The attraction between π-conjugated planar electron donor and acceptor molecules that form many stable charge-transfer (CT) complexes has been explained by quantum chemical CT interactions, although the fundamental origin remains unclear. Here, we demonstrate the mechanism of CT complex formation by potential energy map analysis for TTF–CA and BTBT–TCNQ, using energy decomposition of intermolecular interaction by symmetry-adapted perturbation theory (SAPT) combined with coupled cluster calculation. We find that the source of attraction between donor and acceptor molecules is ascribed primarily to the dispersion force and also to the electrostatic force. In contrast, the contribution of CT interactions to the attractive forces is minimal. We demonstrate that the highly directional feature of the exchange repulsion force, coupled with the attractive dispersion and electrostatic forces, is crucial in determining the intermolecular arrangements of actual CT crystals. These findings are key for understanding the unique structural and electronic properties of π-conjugated CT complexes.

Development of Promising CDK5 Inhibitors Using Structure‐Based Pharmacophore Modeling, Molecular Docking, and Molecular Dynamics Approach

AbstractCancer is highlighted as one of the deadliest diseases globally, with CDK5 identified as a key enzyme in cancer progression. Despite its potential as a therapeutic target, developing CDK5 inhibitors has been challenging. We used multicomplex‐based pharmacophore modeling on CDK5 complexes, identifying hydrophobic groups, hydrogen bond donors, and acceptors as crucial inhibition features. Validated models were used for the virtual screening of drug‐like natural product databases. Thereafter, the screened candidates were selected to study their binding pattern and binding efficiency in the enzyme. Four molecules were shortlisted and analyzed for electrostatic potential (ESP) energy maps. Molecular dynamic simulations and free energy calculations on the docked complexes revealed stable behavior for all, with three (CNP0299652, CNP0362830, and CNP0009633) showing higher Poisson Boltzmann surface area continuum solvation (MM‐PBSA) binding scores than the reference. These candidates demonstrated drug‐like characteristics, crucial amino acid interactions, favorable electron potentials in ESP plots, stable dynamicigher free energy, highlighting their potential as CDK5 inhibitors.

Auroral 3D structure retrieval from the Juno/UVS data

Context. Jovian auroras, the most powerful in the Solar System, result from the interaction between the magnetosphere and atmosphere of Jupiter. While the horizontal morphology of these phenomena has been widely studied, their vertical structure, determined by the penetration depth of the magnetospheric electron into the auroral regions, remains relatively unexplored. Previous observations, including those from the Hubble Space Telescope (HST), have addressed this question to a limited extent. Aims. In this study we aim to map the vertical structure of Jovian auroral emissions. Methods. Using observations from Juno’s UltraViolet Spectrograph (UVS), we examined the vertical structure of the auroral emissions. Building on a recent study of auroral energy mapping based on UVS observations that mapped the average energy of precipitating electrons in the Jovian auroral regions, we find a relationship between this average energy and the volume emission rate (VER) of H2 for two types of electron energy distributions: monoenergetic and a kappa distribution with κ = 2.5. Results. Using brightness maps, we derived the 3D VER structure of Jovian auroras in both northern and southern regions, across multiple spacecraft perijoves (PJs). By considering the example of PJ11, we find that the average altitude of the VER peak in the polar emission region is approximately ~250 km for the monoenergetic distribution case and ~190 km for kappa distribution case. In the main emission region, we find that the average altitude of the VER peak is approximately ~260 km for the case of monoenergetic distribution and ~197 km for kappa distribution case. For the other PJs, we obtained results that are very similar to those of PJ11. Conclusions. Our findings are, on average, consistent with measurements from the Galileo probe and the HST observations. This study contributes to a better understanding of the complexity of Jovian auroras and highlights the importance of using Juno observations when probing their vertical structure. Considering the variability in the κ parameter in the auroral region, we also studied the impact of this variability on the vertical structure of the auroral emission. This sensitivity study reveals that the influence of the κ parameter on our results was very weak. However, the impact of the κ variability on the VER amplitude shows that there is an influence on the thermal structure and chemical composition of the atmosphere in the auroral regions.

Unmanned aerial vehicle image stitching based on multi‐region segmentation

AbstractUnmanned aerial vehicle (UAV) image stitching is a key technology for aerial remote sensing applications. Most existing image stitching methods based on optimal seamline searching algorithms can eliminate defects such as ghosting and distortion in stitched images, which unfortunately suffer from the problem that the seamline may cross those regions with significant geometric misalignment between different images. Therefore, a novel image stitching method based on multi‐region image segmentation is proposed. The algorithm starts by performing a multi‐scale morphological reconstruction in the overlapping regions between UAV images to obtain superpixel images with precise contours. Then, the fast density peaks clustering based on K‐nearest neighbours is applied to automatically determine the clustering centres and the number of clusters. By constructing a cost function, an energy map is generated in the overlapping regions between UAV images. Finally, the optimal seamline can be determined with a graph‐cut method. Compared to several popular image stitching algorithms in real experiments, the proposed method can essentially prevent the seamline from crossing significant ground objects to ensure the integrity of structural objects while achieving satisfactory accuracy and efficiency during the UAV image stitching process.

Particle size and band gap evaluation of green gold nanoparticles (AuNPs) with potential applications

Abstract The present study was aimed towards green synthesis of gold nanoparticles (AuNPs) using fungal strain (SGSK-7) with assessment of their size using different techniques viz., X-ray diffractormeter, Dynamic light scattering, Atomic force microscopy, FE-Scanning Electron Microscopy and Fourier transform Infrared Spectroscopy. The intense characteristic diffraction peaks at angle 2Ѳ (38.13˚) presented the crystalline nature of the reduced gold solution and calculation of crystalline size (12 nm) using Scherer’s equation further confirmed synthesis of gold nanoparticles while dynamic light scattering analysis indicated the particle size range of gold nanoparticles between 2 nm to 50 nm. Field Emission Scanning Electron Microscopic (FESEM) and Atomic Force Microscopy (AFM) analysis of extracellular gold nanoparticles depicts spherical shape of AuNPs of approximately 40 nm. Fourier transform Infrared (FTIR) Spectroscopy depicted the presence of the gold nanoparticles along with the other biomolecules secreted by fungus which plays vital role in reduction, capping and stabilization of gold nanoparticles. Energy dispersive X-ray, mapping analysis, Zeta potential and UV-Vis spectrophotometery analysis further confirmed the presence and stability of gold nanoparticles, respectively. Zeta potential of gold nanoparticles leads to conclude the neutral nature (-10 mV to 10mV) of the particles. The UV-Visible spectrophotometeric analysis also confirmed the formation of gold nanoparticles with an absorption peak at 547 nm. On the basis of UV-Visible spectra the optical band gap of 2.44 eV is examined. The phylogenetic relatedness of SGSK-7 revealed 99% homology with Aspergillus tamari after sequencing of the ITS region followed by BLAST.

ReZNS: Energy and Performance-Optimal Mapping Mechanism for ZNS SSD

ReZNS: Energy and Performance-Optimal Mapping Mechanism for ZNS SSD

FESTA: A Polygon-Based Approach for Extracting Relevant Structures from Free Energy Surfaces Obtained in Molecular Simulations.

An easy-to-use script named FESTA (Free Energy Surface Trajectory Analysis) is provided to streamline the extraction of equilibrium structures from free energy maps obtained with enhanced sampling simulation techniques and their associated trajectories. This approach efficiently identifies relevant structures by automatically selecting minima on the free energy surface using a connected-component labeling algorithm and extracts them utilizing a Shapely-polygon-based trajectory analysis process. The script is general and portable; it incorporates an automatic periodicity detection system; and multiprocessing is utilized to leverage all available computational resources for enhanced efficiency. The effectiveness of the proposed polygon-based approach is demonstrated through comparison with a naïve and largely inefficient loop-based script and its application across three distinct systems for benchmarking purposes.

Rolling bearing fault diagnosis method based on wavelet packet logarithmic energy map

Aiming at the fault diagnosis problem of rolling bearings, a diagnosis method based on wavelet packet logarithmic energy map is proposed. Firstly, a new wavelet packet node logarithmic energy formula is improved and proposed to overcome the shortcomings of cumbersome parameter determination and strong subjectivity in the traditional wavelet packet energy formula, improve the recognition of high-frequency faults and the distinction of low-frequency fault categories, so as to fully extract the initial time-frequency domain features; secondly, the Gram angle and field idea is used to realize the conversion from one-dimensional features to two-dimensional image features, so as to construct the logarithmic energy map feature based on wavelet packet, which further considers the spatial information between adjacent features, thereby optimizing the initial time-frequency domain features and improving the significance of the obtained features. On this basis, the residual network is used to improve the accuracy of fault diagnosis classification results. Finally, through the simulation verification of the standard rolling bearing data set of Case Western Reserve University, it can be seen that the fault diagnosis model constructed by the proposed method has high diagnostic accuracy and strong generalization ability.

First Principles Unveiling Equilibrium Shapes for Understanding Morphological Growth Mechanisms in Alumina Polymorphs

Understanding the morphological evolution of crystals is crucial for industrial applications and surface engineering. This study employs a multifaceted analytical approach to comprehensively analyze gibbsite, boehmite, η, γ, δ, θ, κ, and α-Al2O3. Density Functional Theory (DFT) surface energy calculations provide essential viewpoints into facet exposure and stability, enriching our comprehension of growth patterns. Using the Bravais–Friedel–Donnay–Harker (BFDH) method, a meticulous investigation into morphological properties reveals equilibrium shapes for each structure. Crucially, our models unveil the inaugural equilibrium shapes for structures previously undocumented in the literature (e.g., η, δ, θ, and κ-Al2O3). The study explores morphological growth process during phase transitions from gibbsite and boehmite to alpha alumina, emphasizing nucleation and dissolution preferences for specific facets. A grain analysis, considering bulk energy, surface area, and volume, designates α-Al2O3 as the most stable phase, offering valuable standpoints on thermodynamics and indicating a direct correlation between facet area and volume. Additionally, a comprehensive 3D surface energy mapping offers intricate visualization of facet interactions, providing valuable viewpoints into the complex relationships between surface energy, facet area, and interplanar distance. Comparisons of Fourier-transform infrared spectroscopy (FTIR) spectra, exclusively within the 200–400 cm-1 range, underscore close agreement between theoretical and experimental spectra, consistent with the expected reduction in specific surface area during polymorphic transformation.

Estimation of energy available in rain droplets from meteorological rainfall data

Abstract Even though the amount of rainfall is well documented in the reports of the India Meteorological Department (IMD), the rainfall energy available in different parts of India is unknown to the best of our knowledge. This paper reports a theoretical framework to convert the meteorological rainfall data to rainfall energy. The amount of rainfall over the south-west Monsoon period of 2022 is first converted to an average rainfall rate. Using the information on rain droplet size distribution for a rainfall of a given rainfall rate, the total energy contained in the rainfall is derived. In this, both the kinetic and surface energies of rain droplets of different sizes are considered. Based on the developed theoretical framework, rainfall map of India is converted to rainfall energy map showing states/union territories with maximum and minimum rainfall energy. Furthermore, for selected states in India, the districts showing maximum and minimum rainfall energy are also highlighted. The framework developed in the present study would help attempts to develop efficient rain energy harvesting systems.

Research on hot workability and deformation mechanism of as-extruded Ti-6554 alloy over a broad temperature range

The favorable mechanical characteristics of Ti-6554 offer potential uses in the aerospace industry. To broaden the potential uses of as-extruded Ti-6554 alloy, it is essential to conduct additional assessments on its hot workability and deformation mechanisms. This study, through a series of hot compression tests conducted over a broad temperature range using a thermal simulation testing machine, established the activation energy map and the hot processing map, while examining the evolution of microstructures and deformation mechanisms. The findings indicate that the activation energy of the Ti-6554 alloy reduces as strain rises. The ideal conditions for hot processing are 850-900 °C and 10-3-10-2s-1. The precipitation of the α phase occurs when deforming in the two-phase region, some maintain burgers orientation relationship (BOR) with the matrix, and some maintain random orientation with the matrix. In addition, the α phase precipitation significantly promotes dynamic recrystallization (DRX), resulting in obvious flow softening. The examination of misorientation angles reveals that DRX mechanisms of the β phase. At a slow strain rate, DDRX gradually dominates as the temperature rises. At constant temperatures, CDRX increasingly takes precedence with the increase of strain rate.

Multimodal, microspectroscopic speciation of legacy phosphorus in two US mid‐Atlantic agricultural soils

AbstractTo understand phosphorus (P) mobility in agricultural soils and its potential environmental risk, it is essential to directly measure solid phase P speciation. Often, bulk P K‐edge X‐ray absorption near edge structure (XANES) spectroscopy followed by linear combination fitting (LCF) is utilized to determine the solid P phases in soil. However, this method may limit results to only a few major phases. Additionally, XANES spectra for different P species may have very similar features, leading to an over‐ or underestimate of their contribution to LCF. Here, an improved P speciation by pairing multimodal microbeam‐X‐ray fluorescence (µ‐XRF) mapping coupled with µ‐XANES (microbeam‐X‐ray absorption near edge structure) analysis to directly speciate major and minor P phases on the micron scale is provided. We combined maps of both tender (P, sulfur, aluminum, and silicon) and hard energy (calcium, iron [Fe], and manganese) elements to evaluate the elemental co‐locations with P. To better account for uncertainty assigning XANES peaks to individual compounds, a more quantitative fingerprinting by “spectral feature analysis” was completed. With this analysis, an R‐factor is reported for the fit. These results were compared to traditional LCF. Pre‐edge fitting results revealed the presence of a two‐component pre‐edge feature for phosphate adsorbed to ferrihydrite. Additionally, phytate co‐precipitated with ferrihydrite (Phytate‐Fe‐Cop) had a pre‐edge feature, indicating direct association with Fe. Lastly, a unique P species associated with manganese oxide was identified in the soil via multimodal mapping and µ‐XANES. These results allow for better prediction of P dissolution and mobility.

Promising antibiofilm formation: Liquid phase pulsed laser ablation synthesis of Graphene Oxide@Platinum core-shell nanoparticles.

The increasing prevalence of multi-drug resistance in pathogenic bacteria has rendered antibiotics ineffective, necessitating the exploration of alternative antibacterial approaches. Consequently, research efforts have shifted towards developing new antibiotics and improving the efficacy of existing ones. In the present study, novel core shell graphene oxide@platinum nanoparticles (GRO@Pt-NPs) and their unchanging form have been synthesized using the two-step pulsed laser ablation in liquid (PLAL) technique. The first step involved using the graphene target to create graphene nanoparticles (GRO-NPs), followed by the ablation of GRO-NPs inside platinum nanoparticles (Pt-NPs). To characterize the nanoparticles, various methods were employed, including UV-VIS, transmission electron microscopy (TEM), energy dispersive X-ray (EDX), mapping tests, and X-ray diffraction (XRD). The anti-bacterial and anti-biofilm properties of the nanoparticles were investigated. TEM data confirm the creation of GRO@Pt-NPs. The average particle size was 11 nm for GRO-NPs, 14 nm for Pt-NPs, and 26 nm for GRO@Pt-NPs. The results demonstrate that the created GRO@Pt-NPs have strong antibacterial properties. This pattern is mostly produced through the accumulation of GRO@Pt-NPs on the bacterial surface of Klebsiella pneumoniae (K. pneumoniae) and Enterococcus faecium (E. faecium). The inhibition zones against K. pneumoniae and E. faecium when GRO-NPs were used alone were found to be 11.80 mm and 11.50 mm, respectively. For Pt-NPs, the inhibition zones of E. faecium and K. pneumoniae were 20.50 mm and 16.50 mm, respectively. The utilization of GRO@Pt-NPs resulted in a significant increase in these values, with inhibitory rates of 25.50 mm for E. faecium and 20.45 mm for K. pneumoniae. The antibacterial results were more potent in the core-shell structure than the GRO-NPs alone or Pt-NPs alone. The current work uses, for the first time, a fast and effective technique to synthesize the GRO@Pt-NPs by PLAL method, and the preparation has high clinical potential for prospective use as an antibacterial agent.

Microcracking evolution and clustering fractal characteristics in coal failure under multi step and cyclic loading

During the deep mining process, coal mass encounter intricate geo-environmental stress, such as periodic weighting loading and repeatedly excavation unloading–reloading cycles, which weakens coal’s mechanical integrity and predisposing it to severe coalburst accidents. To investigate the microcracking damage mechanisms and predictive indicators in coal failure under in-situ stress analogs, the multistage step and cyclic loading experiments are conducted on cubic coal specimens. Acoustic emission (AE) technology is employed to track the spatiotemporal-energy evolution of stress-induced damages and discern the microcracking nature through AF/RA assessments, and the power-law scaling relation of AE activity near the catastrophic failure of coal is investigated. Then the clustering fractal structures of microcracking events in the stressed coal are quantified across temporal, spatial and energetic domains, utilizing correlation integral methodologies and b-value derivations from magnitude-frequency relation. Findings indicate that irrespective of the loading mode (step or cyclic), escalating stress triggers an intensification of irreversible fatigue deformations. AE characteristic parameters manifest a gradual rise, culminating in a precipitous peak coinciding with the critical failure point. This escalation adheres to a power-law correlation between AE occurrence frequency and time to failure, observable in the immediate pre-failure seconds, reflecting a universal attribute of coal fracture. Prior to ultimate failure, a marked increase in shear microcracks is discernible, despite tensile-dominated cracks (constituting about 80 % of total microcracks) prevailing as inferred from the variation of AF/RA values, aligning with an inferred “X” conjugate wedge splitting pattern from AE event density and energy mapping. The microcracking events in the loaded coal exhibit a clustering fractal structure that spans across temporal, spatial, and energetic (or magnitude) domains. Notably, the temporal fractal dimension, spatial fractal dimension, and b-value (i.e., a parameter characterized the energetic fractal dimension) all follow a parallel decrease pattern as the loading stress escalates, with a pronounced diminution becoming especially evident as the specimen approaches its catastrophic failure threshold. This insight offers fresh perspectives for predicting rock/coal dynamic disasters, emphasizing the necessity of concurrently monitoring the shift from diffuse microcracking to localized failure across time, space and energy domains. These research findings contribute to a deeper understanding of microcracking damage evolution and failure mechanism of loaded coal, and provide a foundational basis for early warning of rock failure such as the coalburst disasters.

Exploring hydrophobicity or hydrophilicity of borophene surface via reactive molecular dynamics simulation

Borophene, a novel two-dimensional material unveiled in 1998, has garnered significant interest among researchers due to its distinct mechanical and electrical characteristics. Efforts to experimentally synthesize borophene continue to captivate researchers’ interest in recent years. Given the current lack of experimental studies on the interaction between water and the borophene surface, molecular dynamics simulation offers a valuable approach for predicting the substance’s reactivity with water. Additionally, such simulations can assess the hydrophilicity and hydrophobicity of borophene, providing valuable insights into its properties. In our current research, we utilized reactive molecular dynamics simulation to investigate the wetting behavior of borophene. Our findings reveal that the borophene surface exhibits hydrophobic characteristics, demonstrating anisotropic wettability. Specifically, the water contact angle was calculated to be 149.11° along the zigzag direction and 148.4° along the armchair direction. The contour map of the interaction energy between a water molecule and the borophene surface revealed a notable energy barrier in the zigzag direction. This barrier contributes to the asymmetric spreading of the water droplet on the surface. Density profiles and radial pair distribution function (RDF) diagrams of the water droplet on the borophene surface further corroborated the hydrophobic nature of borophene by indicating a significant distance between the water droplet and the surface. Moreover, analysis of the number of hydrogen bonds demonstrated that borophene efficiently utilizes nearly all its capacity to form hydrogen bonds. Additionally, we compared the wettability of borophene with that of other two-dimensional materials, such as various graphene allotropes and phosphorene, which have been subjects of recent investigation.

Propagation characteristics of show sea seismic wave in different underwater media

The propagation characteristics of sea seismic wave (SSSW) in different underwater media are different. This study mainly focuses on the propagation characteristics of SSSW in different underwater media to improve the detection ability of SSSW mines. The propagation of SSSW in different underwater media, including clay, sandy clay, sandstone, mudstone, hard stone, and dolomite, is simulated using the staggered grid finite difference method. The dispersive energy maps of different underwater media are extracted using the f-k dispersive energy extraction method. Compared with the results of Scholte wave dispersion characteristic equations, the simulation results are consistent with the theoretical results. At the same time, the attenuation law of the SSSW train is obtained. The results provide a theoretical basis for developing SSSW mine fuses.

MagMaR III—Resisting the Pressure, Is the Magnetic Field Overwhelmed in NGC6334I?

We report on Atacama Large Millimeter/submillimeter Array observations of polarized dust emission at 1.2 mm from NGC6334I, a source known for its significant flux outbursts. Between five months, our data show no substantial change in total intensity and a modest 8% variation in linear polarization, suggesting a phase of stability or the conclusion of the outburst. The magnetic field, inferred from this polarized emission, displays a predominantly radial pattern from northwest to southeast with intricate disturbances across major cores, hinting at spiral structures. Energy analysis of CS (J = 5 → 4) emission yields an outflow energy of approximately 3.5 × 1045 erg, aligning with previous interferometric studies. Utilizing the Davis–Chandrasekhar–Fermi method, we determined magnetic field strengths ranging from 1 to 11 mG, averaging at 1.9 mG. This average increases to 4 ± 1 mG when incorporating Zeeman measurements. Comparative analyses using gravitational, thermal, and kinetic energy maps reveal that magnetic energy is significantly weaker, possibly explaining the observed field morphology. We also find that the energy in the outflows and the expanding cometary HII region is also larger than the magnetic energy, suggesting that protostellar feedback may be the dominant driver behind the injection of turbulence in NGC6334I at the scales sampled by our data. The gas in NGC6334I predominantly exhibits supersonic and trans-Alfvenic conditions, transitioning towards a super-Alfvenic regime, underscoring a diminished influence of the magnetic field with increasing gas density. These observations are in agreement with prior polarization studies at 220 GHz, enriching our understanding of the dynamic processes in high-mass star-forming regions.

Polyurethane/Carbon Nanotube-Based ThermoSense Electronic Skin: Perception to Decision Making Aided by Internet of Things Brain.

Human skin has several receptors collaborating with the brain to provide appropriate "decisions" when applying stimuli. Several research articles state that biomimetic electronic skin (e-skin) is reportedly used for sensor-related applications and performs similarly to natural skin. However, research reporting the capability of the e-skin to make decisions and therefore react upon exposure to adverse conditions is still in its nascent stage. Herein, we report the development of an e-skin, ThermoSense, that can thermoregulate by making appropriate decisions. Thermoplastic polyurethane and multiwalled carbon nanotubes were used as the model composite. The heating and sensing capabilities of the optimized e-skin were studied in detail. In the study window, the e-skin demonstrated excellent electrothermal conversion efficiency by generating a temperature of 192 °C, consuming a power of 2.23 W. A finite element modeling (FEM) was adopted to determine the distribution of the filler in the case of the optimized e-skin and thus was used to probe the reason for the heating across the e-skin via mapping of the internal energy across the sample. FEM results and experimental findings are in strong agreement. Additionally, the e-skin demonstrated its capability to act as a thermal sensor with a 0.947% °C-1 sensitivity. To integrate the decision-making capabilities of the e-skin, an Internet of Things (IoT) brain console was made using the e-skin and electronic chips by leveraging More than Moore's concept. The IoT brain was automated with decision-making programming that was controllable via an in-house-developed mobile application. The console worked exclusively under simulated conditions. When there was a shift from the set point temperature, it started to heat. Postusage, the e-skin matrix was recycled, and the recycled e-skin demonstrated a marginal decrement in performance attributes. This study opens new avenues for developing decision-making e-skins for next-generation human-machine interphases.

Hydrogen, Oxygen, and Lead Adsorbates on Al13Co4(100): Accurate Potential Energy Surfaces at Low Computational Cost by Machine Learning and DFT-Based Data.

Intermetallic compounds are promising materials in numerous fields, especially those involving surface interactions, such as catalysis. A key factor to investigate their surface properties lies in adsorption energy maps, typically built using first-principles approaches. However, exploring the adsorption energy landscapes of intermetallic compounds can be cumbersome, usually requiring huge computational resources. In this work, we propose an efficient method to predict adsorption energies, based on a Machine Learning (ML) scheme fed by a few Density Functional Theory (DFT) estimates performed on n sites selected through the Farthest Point Sampling (FPS) process. We detail its application on the Al13Co4(100) quasicrystalline approximant surface for several atomic adsorbates (H, O, and Pb). On this specific example, our approach is shown to outperform both simple interpolation strategies and the recent ML force field MACE [arXiv.2206.07697], especially when the number n is small, i.e., below 36 sites. The ground-truth DFT adsorption energies are much more correlated with the predicted FPS-ML estimates (Pearson R-factor of 0.71, 0.73, and 0.90 for H, O and Pb, respectively, when n = 36) than with interpolation-based or MACE-ML ones (Pearson R-factors of 0.43, 0.39, and 0.56 for H, O, and Pb, in the former case and 0.22, 0.35, and 0.63 in the latter case). The unbiased root-mean-square error (ubRMSE) is lower for FPS-ML than for interpolation-based and MACE-ML predictions (0.15, 0.17, and 0.17 eV, respectively, for hydrogen and 0.17, 0.25, and 0.22 eV for lead), except for oxygen (0.55, 0.47, and 0.46 eV) due to large surface relaxations in this case. We believe that these findings and the corresponding methodology can be extended to a wide range of systems, which will motivate the discovery of novel functional materials.