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Strain induced through fabrication, both by patterning and capping, can be used to change the properties of two-dimensional (2D) materials or other thin films. Here, we explore how capping layers impart strain to monolayer MoS$_{2}$ using direct x-ray diffraction measurements of the lattice. We first observe the impact of naturally-oxidized metal layers ($\sim$1.5 nm Al) and subsequently-deposited Al$_{2}$O$_{3}$ (15 nm to 25 nm thick) on the 2D material, and find that the strain imparted to MoS$_{2}$ is mainly controlled by the interfacial adhesion of the seed layer in addition to the substrate adhesion. Then, using test structures which mimic transistor contacts, we measure enhanced strain from such patterns compared to blanket films. Furthermore, we observe significant tensile strain - up to 2% in monolayer MoS$_{2}$, one of the largest experimental values to date on a rigid substrate - due to highly-stressed blanket metal capping layers. These results provide direct evidence supporting previous reports of strain effects in 2D material devices.

Mobile devices, including smartphones and laptops, generate decentralized and heterogeneous data, presenting significant challenges for traditional centralized machine learning models due to substantial communication costs and privacy risks. Federated Learning (FL) offers a promising alternative by enabling collaborative training of a global model across decentralized devices without data sharing. However, FL faces challenges due to statistical heterogeneity among clients, where non-independent and identically distributed (non-IID) data impedes model convergence and performance. This paper focuses on data-dependent heterogeneity in FL and proposes a novel approach leveraging mean latent representations extracted from locally trained models. The proposed method normalizes client contributions based on these representations, allowing the central server to estimate and adjust for heterogeneity during aggregation. This normalization enhances the global model's generalization and mitigates the limitations of conventional federated averaging methods. The main contributions include introducing a normalization scheme using mean latent representations to handle statistical heterogeneity in FL, demonstrating the seamless integration with existing FL algorithms to improve performance in non-IID settings, and validating the approach through extensive experiments on diverse datasets. Results show significant improvements in model accuracy and consistency across skewed distributions. Our experiments with six FL schemes: FedAvg, FedProx, FedBABU, FedNova, SCAFFOLD, and SGDM highlight the robustness of our approach. This research advances FL by providing a practical and computationally efficient solution for statistical heterogeneity, contributing to the development of more reliable and generalized machine learning models., Comment: Accepted at IEEE INDICON 2024

We consider the framework of distributed aggregation of Distributed Energy Resources (DERs) in power networks to provide ancillary services to the power grid. Existing aggregation schemes work under the assumption of trust and honest behavior of the DERs and can suffer when that is not the case. In this article, we develop a distributed detection scheme that allows the DERs to detect and isolate the maliciously behaving DERs. We propose a model for the maliciously behaving DERs and show that the proposed distributed scheme leads to the detection of the malicious DERs. Further, augmented with the distributed power apportioning algorithm the proposed scheme provides a framework for resilient distributed power apportioning for ancillary service dispatch in power networks. A controller-hardware-in-the-loop (CHIL) experimental setup is developed to evaluate the performance of the proposed resilient distributed power apportioning scheme on an 8-commercial building distribution network (Central Core) connected to a 55 bus distribution network (External Power Network) based on the University of Minnesota Campus. A diversity of DERs and loads are included in the network to generalize the applicability of the framework. The experimental results corroborate the efficacy of the proposed resilient distributed power apportioning for ancillary service dispatch in power networks., Comment: 7 pages, 3 figures

The electrical resistivity of conventional metals, such as copper, is known to increase in thinner films due to electron-surface scattering, limiting the performance of metals in nanoscale electronics. Here, we uncover a reduction of resistivity with decreasing film thickness in NbP semimetal, deposited at relatively low temperatures of 400 \deg C. In sub-5 nm thin films, we find a significantly lower room temperature resistivity (~34 microOhm*cm for 1.5 nm thin NbP) than in the bulk form, and lower than conventional metals at similar thickness. Remarkably, the NbP films are not crystalline, but display local nanocrystalline, short-range order within an amorphous matrix. Our analysis suggests that the lower resistivity is due to conduction via surface channels, together with high surface carrier density and sufficiently good mobility as the film thickness is reduced. These results and the fundamental insights obtained here could enable ultrathin, low-resistivity wires for nanoelectronics, beyond the limitations of conventional metals.

We have studied transverse momentum ($p_{\rm{T}}$) spectra of charged hadrons in various pseudo-rapidity ranges for p-Pb collisions at $\sqrt{s_{\rm{NN}}}$ = 5.02 TeV. The medium effects such as collective flow and energy loss resulting from heavy-ion collisions have also been investigated using modified Tsallis distribution function over a wide range of $p_{\rm{T}}$ that indicates the transverse collective flow at low and intermediate $p_{\rm{T}}$ range and in-medium energy loss in high $p_{\rm{T}}$ range., Comment: arXiv admin note: substantial text overlap with arXiv:2405.12231

The trends and reactions of the general public towards global events can be analyzed using data from social platforms, including Twitter. The number of tweets has been reported to help detect variations in communication traffic within subsets like countries, age groups and industries. Similarly, publicly accessible data and (in particular) data from social media about agricultural issues provide a great opportunity for obtaining instantaneous snapshots of farmer opinions and a method to track changes in opinion through temporal analysis. In this paper we hypothesize that the presence of keywords like precision agriculture, digital agriculture, Internet of Things (IoT), BigData, remote sensing, GPS, etc., in tweets could serve as an indicator of discussions centered around interest in modern farming practices. We extracted relevant tweets using keywords such as IoT, BigData and Geographical Information System (GIS), and then analyzed their geographical origin and frequency of their mention. We analyzed the Twitter data for the period of 1st -11th January 2018 to understand these trends and the factors affecting them. These factors, such as special events, projects, biogeography, etc., were further analyzed using tweet sources and trending hashtags from the database. The regions with the highest interest in the keywords were United States, Egypt, Brazil, Japan and China. A comparison of frequency of keywords revealed IoT as the most tweeted word (77.6%) in the downloaded data. The most used language was English followed by Spanish, Japanese and French. Periodical tweets on IoT from an account handled by IoT project on Twitter and Seminars on IoT in January in Santa Catarina (Brazil) were found to be the underlying factors for the observed trends., Comment: 7 pages, conference accepted

Strain engineering has played a key role in modern silicon electronics, having been introduced as a mobility booster in the 1990s and commercialized in the early 2000s. Achieving similar advances with two-dimensional (2D) semiconductors in a CMOS (complementary metal oxide semiconductor) compatible manner would radically improve the industrial viability of 2D transistors. Here, we show silicon nitride capping layers can impart strain to monolayer MoS2 transistors on conventional silicon substrates, enhancing their electrical performance with a low thermal budget (350 {\deg}C), CMOS-compatible approach. Strained back-gated and dual-gated MoS2 transistors demonstrate median increases up to 60% and 45% in on-state current, respectively. The greatest improvements are found when both transistor channels and contacts are reduced to ~200 nm, reaching saturation currents of 488 uA/um, higher than any previous reports at such short contact pitch. Simulations reveal that most benefits arise from tensile strain lowering the contact Schottky barriers, and that further reducing device dimensions (including contacts) will continue to offer increased strain and performance improvements.

n this study, we utilize a rigorous ab initio-based finite momentum Bethe-Salpeter equation to investigate the photoluminescence emission in two-dimensional hexagonal tungsten carbide (h-WC). This thermodynamically stable monolayer exhibits an indirect optical gap, resulting in phonon-assisted emission. We observe that light absorption is a direct process centered around the direct quasiparticle gap, while light emission is indirect and requires modes between $\Gamma$-$M$ in the phonon dispersion. The emission lines feature prominent phonon replicas at cryogenic temperatures, particularly near-infrared wavelengths (1.09 and 1.17 eV), and we observe exciton thermalization with the crystal beyond 25 K. Additionally, non-radiative recombination is a remarkably fast process, occurring at order of a few femtoseconds (4.8 fs at 0 K and 2.8 fs at 300 K) compared to radiative recombination (2.3 ps at 0 K and 214 ns at 300 K). These optical characteristics of 2D h-WC may facilitate the promise of photon-emitter devices for near-infrared signal communication.

The transverse momentum ($p_T$) spectra of charged hadrons in $p+p$, $p+Pb$ and $Pb+Pb$ collisions at $\sqrt {s_{NN}} = 5.02$ TeV are presented here within the rapidity range of $-2.5

We use a continuum, two-fluid approach to study a mixture of two active nematic fluids. Even in the absence of thermodynamically-driven ordering, for mixtures of different activities we observe turbulent microphase separation, where domains form and disintegrate chaotically in an active turbulent background. This is a weak effect if there is no elastic nematic alignment between the two fluid components, but is greatly enhanced in the presence of an elastic alignment or substrate friction. We interpret the results in terms of relative flows between the two species which result from active anchoring at concentration gradients. Our results may have relevance in interpreting epithelial cell sorting and the dynamics of multi-species bacterial colonies.

Learning of deep neural networks (DNN) using Resistive Processing Unit (RPU) architecture is energy-efficient as it utilizes dedicated neuromorphic hardware and stochastic computation of weight updates for in-memory computing. Charge Trap Flash (CTF) devices can implement RPU-based weight updates in DNNs. However, prior work has shown that the weight updates (V_T) in CTF-based RPU are impacted by the non-ideal program time of CTF. The non-ideal program time is affected by two factors of CTF. Firstly, the effects of the number of input pulses (N) or pulse width (pw), and secondly, the gap between successive update pulses (t_gap) used for the stochastic computation of weight updates. Therefore, the impact of this non-ideal program time must be studied for neural network training simulations. In this study, Firstly, we propose a pulse-train design compensation technique to reduce the total error caused by non-ideal program time of CTF and stochastic variance of a network. Secondly, we simulate RPU-based DNN with non-ideal program time of CTF on MNIST and Fashion-MNIST datasets. We find that for larger N (~1000), learning performance approaches the ideal (software-level) training level and, therefore, is not much impacted by the choice of t_gap used to implement RPU-based weight updates. However, for lower N (<500), learning performance depends on T_gap of the pulses. Finally, we also performed an ablation study to isolate the causal factor of the improved learning performance. We conclude that the lower noise level in the weight updates is the most likely significant factor to improve the learning performance of DNN. Thus, our study attempts to compensate for the error caused by non-ideal program time and standardize the pulse length (N) and pulse gap (t_gap) specifications for CTF-based RPUs for accurate system-level on-chip training.

Semiconducting transition metal dichalcogenides (TMDs) are promising for high-specific-power photovoltaics due to desirable band gaps, high absorption coefficients, and ideally dangling-bond-free surfaces. Despite their potential, the majority of TMD solar cells are fabricated in a non-scalable fashion using exfoliated materials due to the absence of high-quality, large-area, multilayer TMDs. Here, we present the scalable, thickness-tunable synthesis of multilayer tungsten diselenide (WSe$_{2}$) films by selenizing pre-patterned tungsten with either solid source selenium or H$_{2}$Se precursors, which leads to smooth, wafer-scale WSe$_{2}$ films with a layered van der Waals structure. The films have charge carrier lifetimes up to 144 ns, over 14x higher than large-area TMD films previously demonstrated. Such high carrier lifetimes correspond to power conversion efficiency of ~22% and specific power of ~64 W g$^{-1}$ in a packaged solar cell, or ~3 W g$^{-1}$ in a fully-packaged solar module. This paves the way for the mass-production of high-efficiency multilayer WSe$_{2}$ solar cells at low cost.

We have performed hardness measurement experiments under different loads and loading times by performing micro-indentation marks in the present work. Chalcogenide glasses (ChGs) comprising Se$_{78}$Te$_{20}$Sn$_2$ and Se$_{78-x}$Te$_{20}$Sn$_2$Zn$_x$ (where $x = 0, 2, 4, 6$) alloys are the subject of micro-indentation tests in this work. We have utilized both micro-indentation and optical microscopic methods to determine Vickers hardness. Thermal glass transition phenomena have been identified through DSC techniques. The modulus of elasticity (E), an essential mechanical property, has been evaluated using established empirical equations. Further, we have studied other mechanical parameters [e.g., minimal micro-void formation energy (Eh), glass's fragility index (m), micro-void volume (Vh), etc.] and the covalent character of the glassy system. Additionally, various physical parameters, including density, molar volume, and compactness, have also been determined.

In the innermost regions of Active Galactic Nuclei (AGN), matter is understood to be flowing onto the Supermassive black hole (SMBH), which forms an accretion disk. This disk is responsible for the optical/UV continuum emission observed in the spectra of AGN. Reverberation Mapping of the accretion disk using multiple bands can yield the structure of the disk. The emission is expected to be of the black body type peaking at different wavelengths. Hence, depending on the temperature of the disk, continuous, simultaneous monitoring in multiple wavelength ranges to cover hotter inner regions and cooler outer regions can yield the structure and temperature profile of the accretion disk itself. In this study, we present initial results from our accretion disk reverberation mapping campaign targeting AGN with Super High Eddington Accreting Black Holes (SEAMBH). Our analysis on one of the sources- IRAS 04416+1215; based on the broadband observations using the Growth India telescope (GIT), reveals that the size of the accretion disk for this source, calculated by cross-correlating the continuum light curves is larger than expected from the theoretical model. We fit the light curves directly using the thin disk model available in {\sc javelin} and find that the disk sizes are approximately 4 times larger than expected from the Shakura Sunyaev (SS) disk model. Further studies are needed to understand better the structure and physics of AGN accretion disks and their role in the evolution of galaxies., Comment: 11 pages,4 Figures. Accepted for publication in the Bulletin de la Soci\'et\'e Royale des Sciences de Li\`ege as proceeding of the 3rd Belgo-Indian Network for Astronomy and Astrophysics (BINA) workshop, held in Bhimtal, Uttarakhand (India) on 22-24 March 2023

We use linear stability analysis and hybrid lattice Boltzmann simulations to study the dynamical behaviour of an active nematic confined in a channel made of viscoelastic material. We find that the quiescent, ordered active nematic is unstable above a critical activity. The transition is to a steady flow state for high elasticity of the channel surroundings. However, below a threshold elastic modulus, the system produces spontaneous oscillations with periodic flow reversals. We provide a phase diagram that highlights the region where time-periodic oscillations are observed and explain how they are produced by the interplay of activity and viscoelasticity. Our results suggest new experiments to study the role of viscoelastic confinement in the spatio-temporal organization and control of active matter., Comment: Main text: 5 pages + 4 figs., Supp. Mat.: 4 pages

Transition metal dichalcogenides (TMDs) show great promise as absorber materials in high-specific-power (i.e. high-power-per-weight) solar cells, due to their high optical absorption, desirable band gaps, and self-passivated surfaces. However, the ultimate performance limits of TMD solar cells remain unknown today. Here, we establish the efficiency limits of multilayer MoS2, MoSe2, WS2, and WSe2 solar cells under AM 1.5 G illumination as a function of TMD film thickness and material quality. We use an extended version of the detailed balance method which includes Auger and defect-assisted Shockley-Reed-Hall recombination mechanisms in addition to radiative losses, calculated from measured optical absorption spectra. We demonstrate that single-junction solar cells with TMD films as thin as 50 nm could in practice achieve up to 25% power conversion efficiency with the currently available material quality, making them an excellent choice for high-specific-power photovoltaics., Comment: 24 pages

Training deep neural networks (DNNs) is computationally intensive but arrays of non-volatile memories like Charge Trap Flash (CTF) can accelerate DNN operations using in-memory computing. Specifically, the Resistive Processing Unit (RPU) architecture uses the voltage-threshold program by stochastic encoded pulse trains and analog memory features to accelerate vector-vector outer product and weight update for the gradient descent algorithms. Although CTF, offering high precision, has been regarded as an excellent choice for implementing RPU, the accumulation of charge due to the applied stochastic pulse trains is ultimately of critical significance in determining the final weight update. In this paper, we report the non-ideal program-time conservation in CTF through pulsing input measurements. We experimentally measure the effect of pulse width and pulse gap, keeping the total ON-time of the input pulse train constant, and report three non-idealities: (1) Cumulative V_T shift reduces when total ON-time is fragmented into a larger number of shorter pulses, (2) Cumulative V_T shift drops abruptly for pulse widths < 2 {\mu}s, (3) Cumulative V_T shift depends on the gap between consecutive pulses and the V_T shift reduction gets recovered for smaller gaps. We present an explanation based on a transient tunneling field enhancement due to blocking oxide trap-charge dynamics to explain these non-idealities. Identifying and modeling the responsible mechanisms and predicting their system-level effects during learning is critical. This non-ideal accumulation is expected to affect algorithms and architectures relying on devices for implementing mathematically equivalent functions for in-memory computing-based acceleration.

Energy-efficient real-time synapses and neurons are essential to enable large-scale neuromorphic computing. In this paper, we propose and demonstrate the Schottky-Barrier MOSFET-based ultra-low power voltage-controlled current source to enable real-time neurons for neuromorphic computing. Schottky-Barrier MOSFET is fabricated on a Silicon-on-insulator platform with polycrystalline Silicon as the channel and Nickel/Platinum as the source/drain. The Poly-Si and Nickel make the back-to-back Schottky junction enabling ultra-low ON current required for energy-efficient neurons.