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Surfactant-Assisted Exfoliation of Tantalum Diboride (TaB2) for Electrochemical CO2 Reduction
No full textRecent years have witnessed a renewed interest in utilizing AlB2-type metal borides for applications traditionally not envisaged for this family of ionic layered materials. This is due to a native synergy between the metal atoms and boron honeycomb planes that imparts them versatile physicochemical properties. This prospect is further augmented by their feasibility to be nanoscaled into quasi-two-dimensional (2D) forms-XBenes, as demonstrated by several recent studies. In this work, we show that such a nanoscale can also be extended to tantalum diboride (TaB2), a member of this family that has largely remained uncharted. We found that TaB2 can be exfoliated into few-layer-thick nanosheets (mean thickness of 4.5 nm) by using surfactant chemistry. The resultant nanosheets were found to retain their structural integrity to a large extent. We utilized the readily accessible Ta-B sites offered by these nanosheets to catalyze the electrochemical reduction of aqueous CO2. Moreover, we found that these TaB2 nanosheets facilitate the production of ethylene as the main carbon product, with faradaic efficiency reaching 75% at −0.85 V vs RHE. We obtain additional insights using DFT studies, which show how the interaction of CO2 with Ta and B atoms results in favorable CO2 adsorption for ethylene production
Evaluating pre-trained large language models using simulated human inputs for python code generation
No full textLarge language models (LLMs) have transformed human-AI interactions, yet research on making their use more accessible is still limited. While some studies address the inclusivity of generated language, less focus has been placed on interaction mechanisms that enhance accessibility, such as speech-to-text and optical character recognition, as well as the errors they may incur. This study investigates these input errors and additionally models interactions from non-native English speakers using data augmentation tools like nlpaug and textattack. We assess the performance of the gpt-4o-mini and o3-mini models by augmenting datasets like HumanEval+ and generating Python code, leveraging the models� strengths. Our results show a statistically significant degradation in performance across various metrics, including pass@k, Pylint score, and code similarity. The text prompts that are summarized or contain speech-to-text artifacts led to performance reductions of up to 40 percentage points in the pass@k metric on the HumanEval+ dataset, with consistent trends across other metrics. These findings highlight the need for developers to evaluate LLM robustness against interaction artifacts before integration with input mechanisms and encourage natural language processing researchers to develop datasets that reflect such artifacts for improved model training
Development of a Matrix Method Based Framework for the Thermo-Mechanical Analysis of RCC Frames
No full textReinforced concrete frames are one of the predominant structural systems which are commonly used in India. Fire resistance is an important design aspect as fire is one of the most severe environmental conditions to which structures may be exposed to during their life time. Typical fire resistance analysis involves evaluation of temperature across the structural member and incorporation of the thermal effects in structural analysis by temperature dependent material models of concrete and steel employing various numerical schemes. The main limitation of these methods is that they evaluate the fire resistance of individual structural components and they lack the competency to quantify the response of whole structure. Thermo-mechanical analysis can be carried out in commercial finite element software like ANSYS but modeling an entire structure is computationally very intensive. To alleviate these difficulties, a matrix analysis methodology has been developed incorporating the effects of thermal strains as well as temperature dependent material models of concrete and steel. The key strength of matrix analysis lies in its computational efficiency compared to ANSYS while producing results of reasonable accuracy. Numerical examples are presented to demonstrate the efficacy of the developed methodology
From lock and key to molecular diplomacy: understanding pollen recognition and discrimination in brassicaceae
No full textKey message: Hybridization barriers in Brassicaceae play a pivotal role in governing reproductive success and maintaining speciation. In this perspective, we highlight recent advances revealing the intricate molecular mechanisms and the interplay among key players governing these barriers. Abstract: Recent studies have shed light on the molecular mechanisms that govern hybridization barriers in Brassicaceae. The interplay between pollen coat proteins, stigmatic receptors, and signaling peptides plays a crucial role in determining the success of pollination. At the core of this system, autocrine stigmatic RALF peptides (sRALF) maintain the stigmatic barrier by activating the FERONIA (FER) and ANJEA (ANJ) receptor complex, triggering the RAC/ROP-RBOHD pathway and subsequent reactive oxygen species (ROS) production. It is now established that incompatible pollen rejection is mediated by two parallel pathways: the FER-RAC/ROP-RBOHD pathway, which generates ROS, and the ARC1-mediated pathway, which degrades compatible factors required for pollen growth. Conversely, compatible pollen overcomes the stigmatic barrier through the action of pollen coat proteins (PCP-B) and paracrine pollen-derived RALF peptides (pRALF), which compete with autocrine sRALF for receptor binding, enabling successful pollen hydration and tube penetration. The "lock-and-key" mechanism involving sRALF and pRALF provides species-specific recognition of compatible pollen. These findings offer valuable insights into the molecular basis of hybridization barriers and open new possibilities for overcoming these barriers in interspecific and intergeneric crosses within Brassicaceae, with potential applications in plant breeding and crop improvement. Future research should focus on elucidating the evolutionary dynamics of these signaling pathways and exploring their manipulation for crop breeding purposes
Residual tensile stress mitigation and surface hardness enhancement in Ti6Al4V alloy via elevated temperature laser surface melting
No full textLaser surface melting (LSM) is a versatile technique used to modify surface properties without affecting the bulk characteristics of materials. This paper investigates the effects of LSM with pre-heating at different temperatures on the induced residual tensile stresses, hardness, and surface topography of Ti6Al4V alloy. A finite element-based numerical model is developed to analyse the cooling rate during the process. Cooling rates decrease, and melt pool dimensions increase in LSM at elevated temperatures. This decreases the residual tensile stresses and increases the microhardness. Experimental results reveal that higher pre-heating temperatures lead to up to 42 % reduction in residual tensile stress and up to 25 % improvement in microhardness, with minimal effect on surface topography. This study highlights the efficacy of elevated temperature LSM to mitigate residual tensile stresses and enhance surface hardness, which is promising for defence, aerospace, and automotive applications
Imprint of a quasi-16-day period in boreal summer through modulation of quasi-2-day wave implying interhemispheric coupling
No full textAn interesting case of quasi-2-day wave (Q2DW) amplitude modulation with a quasi-16-day period is investigated using meteor radar winds and global reanalysis data during the 2019 boreal summer. The modulation is found to originate near the equator at 50 km altitude. Presence of a dominant eastward propagating quasi-16-day wave with zonal wavenumber 2 (Q16DWE2) in the austral winter across the zero-wind line near the equator initiates the modulation, as evident in the westward propagating quasi-2-day wave with zonal wavenumber 3 (Q2DWW3). Notably, while no significant Q16DW wave is detected in the boreal summer middle atmospheric winds, the primary Q2DWW3 mode (with amplitudes reaching ~8 m s−1) play a crucial role in carrying the Q16DW signature from the winter to the summer hemisphere. Additionally, the Q16DW appearance in the summer upper mesosphere and lower thermosphere (90–100 km altitude) that is near the dissipation altitude of the Q2DW corroborates a potential link between these two dynamical entities. Overall, the present study highlights a novel mechanism of interhemispheric coupling through planetary wave modulation, offering new insights into the global dynamics of the lower and middle atmosphere
Atomistic modeling and experimental study of dopant segregation induced morphology transition in ZnO nanoparticles
No full textElemental dopants, commonly introduced during the synthesis of ZnO nanopowders, tend to segregate to surfaces and grain boundaries. However, the atomistic mechanisms underlying dopant segregation and its impact on surface energetics and particle morphology are not yet fully understood. In this study, we combine experimental and computational approaches to investigate Al- and Mg-doped ZnO nanopowders synthesized via the coprecipitation method. Electron microscopy analysis reveals that Al doping transforms the flower-shaped ZnO particles into granular-shaped particles and reduces the particle size, whereas Mg doping does not alter the morphology and results into bigger particles. Atomistic modeling of the surface segregation of dopants indicates that Al preferentially segregates to the surfaces, whereas Mg remains in the bulk. These findings are supported by lattice strain calculations from X-ray diffraction. The preferential segregation of Al to the high energy surfaces results in the homogenization of ZnO surface energies, which is primarily responsible for the observed morphological transformation. This study provides fundamental insights into how Al and Mg dopant segregation influences ZnO nanoparticle's characteristics, offering valuable guidance for designing them for applications in sensing, catalysis, and beyond
Photocatalytic Hydrolysis of Silicon Hydrides for On-Demand Green Hydrogen Production under Natural Sunlight
No full textDeveloping an innovative strategy for efficient, on-demand, and on-site production of green hydrogen from waste and renewable resources could offset the cost of the transport and storage of hydrogen. Here, we establish nanoparticle-assisted photocatalytic silicon hydride (silane) hydrolysis as an effective strategy for the on-demand production of green hydrogen from sunlight without electricity. Furthermore, this process produces silanol and siloxanes as value-added side products with extensive industrial applications. We demonstrated that this process works with all visible and NIR wavelengths with a maximum absolute photon-to-hydrogen conversion efficiency (IPHCE), reaching 0.66% at 808 nm. It also has a broad substrate scope and works with primary to tertiary silanes. Besides green hydrogen production, we also report a photocatalytic pathway for synthesizing polysiloxanes from the hydrolysis of primary and secondary hydrosilanes. Finally, we demonstrated on-demand hydrogen production from methyl phenyl silane under natural sunlight with an appreciable hydrogen production rate of approximately 19.6 L m-2 h-1. These results demonstrate the suitability of photocatalytic silicon hydride hydrolysis as a highly potent strategy for setting up domestic-scale, on-demand hydrogen production units at remote locations
Microlocal inversion of a restricted mixed ray transform for second-order tensor fields in R3
No full textIn this article, we study a restricted mixed ray transform acting on second-order tensor fields in 3-dimensional Euclidean space and prove the invertibility of this integral transform using microlocal techniques. Here, the mixed ray transform is restricted over lines passing through a fixed curve γ in R3 satisfying certain geometric conditions. The main theorem of the article shows that a second-order tensor field can be recovered from its restricted mixed-ray transform up to the kernel of the transform, a smoothing term, and a known singular term