arXiv subjects

Tarique Anwar

Publications and source records attributed to Tarique Anwar.

10 recordsLinked to original sources

arXiv2026

Revealing Wavelength- and Size-Dependent CO2 Reduction Selectivity via Operando Scanning Photo-Electrochemical Microscopy

Controlling product selectivity in plasmonic catalysis, particularly in CO2 reduction (CO2R), remains a central unsolved challenge with direct implications for light-driven fuel and chemical synthesis. Here, we deploy quantitative operando scanning photoelectrochemical microscopy (photo-SECM) to provide a direct demonstration that tuning photon energy switches CO2R selectivity through an electronically driven pathway. On plasmonic Au/p-GaN photocathodes, interband excitation (460-560 nm) drives selective CO production while intraband excitation (640-800 nm) favors H2 evolution. By maintaining constant absorbed power across wavelengths and confirming linear power dependence, we isolate the role of hot-carrier energy from photonic and photothermal contributions. Density functional theory calculations reveal that higher-energy interband excitation progressively increases the overlap between hot-electron-accessible states and the CO-producing intermediate, selectively promoting CO over formate, in excellent agreement with experiment. We further show that selectivity is geometrically gated by hot-carrier transport: sub-100 nm nanostructures sustain CO2R activity, while ~300 nm nanodisks suffer transport losses that suppress it, consistent with ab initio hot-carrier transport calculations. Together, these results establish photon energy, carrier transport, and nanostructure geometry as coupled design parameters for plasmonic CO2R selectivity, resolve a longstanding debate on the origin of plasmon-driven selectivity effects, and position photo-SECM as a broadly applicable operando platform for photo(electro)catalysis.

physics.chem-ph↗

arXiv2025

Revealing Light-Driven Dynamics at Nanostructured Solid-Liquid Interfaces with In-Situ SHG

Light and heat drive interfacial chemistry at solid-liquid interfaces, underpinning processes central to sustainable energy conversion, including photoelectrochemical and hydrovoltaic systems. Yet, non-invasive probing of light-induced interfacial dynamics remains challenging due to the weak and spatially complex nature of optical signals. Here, we introduce a nanophotonic platform that enhances second harmonic generation (SHG) from nanostructured interfaces by over two orders of magnitude, enabling real-time, all-optical access to interfacial processes. We develop a rigorous overlap-integral formalism that provides a general quantitative framework for SHG in nanostructured geometries. By accounting for spatially inhomogeneous electromagnetic fields, this approach links the nonlinear response to geometry-dependent near-field and reveals new degrees of freedom, namely independent control of attenuation and phase, which are absent in planar systems. This enables deterministic tuning of surface and electric-field-induced contributions through nanophotonic design. Using in situ SHG at silicon-oxide-electrolyte interfaces, we resolve subtle spectral shifts of ~1.3 nm with electrolyte concentration, indicating coupling between electrical double layer potential and semiconductor polarizability. Under controlled optical excitation, we observe reversible, intensity-dependent modulation of interfacial susceptibility, with a decrease at low intensities consistent with photocharging and an increase at higher intensities due to photothermal effects. These results establish nanophotonic-enhanced SHG as a quantitative and tunable probe of interfacial phenomena, providing a unified framework linking optical response, electrostatics, and geometry, and opening new avenues for controlling interfacial charge and potential with light for applications in energy conversion, catalysis, and nanophotonic devices.

physics.chem-ph↗

arXiv2025

AttentionDep: Domain-Aware Attention for Explainable Depression Severity Assessment

In today's interconnected society, social media platforms provide a window into individuals' thoughts, emotions, and mental states. This paper explores the use of platforms like Facebook, X (formerly Twitter), and Reddit for depression severity detection. We propose AttentionDep, a domain-aware attention model that drives explainable depression severity estimation by fusing contextual and domain knowledge. Posts are encoded hierarchically using unigrams and bigrams, with attention mechanisms highlighting clinically relevant tokens. Domain knowledge from a curated mental health knowledge graph is incorporated through a cross-attention mechanism, enriching the contextual features. Finally, depression severity is predicted using an ordinal regression framework that respects the clinical-relevance and natural ordering of severity levels. Our experiments demonstrate that AttentionDep outperforms state-of-the-art baselines by over 5% in graded F1 score across datasets, while providing interpretable insights into its predictions. This work advances the development of trustworthy and transparent AI systems for mental health assessment from social media.

cs.AI↗

arXiv2025

DepressionX: Knowledge Infused Residual Attention for Explainable Depression Severity Assessment

In today's interconnected society, social media platforms have become an important part of our lives, where individuals virtually express their thoughts, emotions, and moods. These expressions offer valuable insights into their mental health. This paper explores the use of platforms like Facebook, $\mathbb{X}$ (formerly Twitter), and Reddit for mental health assessments. We propose a domain knowledge-infused residual attention model called DepressionX for explainable depression severity detection. Existing deep learning models on this problem have shown considerable performance, but they often lack transparency in their decision-making processes. In healthcare, where decisions are critical, the need for explainability is crucial. In our model, we address the critical gap by focusing on the explainability of depression severity detection while aiming for a high performance accuracy. In addition to being explainable, our model consistently outperforms the state-of-the-art models by over 7% in terms of $\text{F}_1$ score on balanced as well as imbalanced datasets. Our ultimate goal is to establish a foundation for trustworthy and comprehensible analysis of mental disorders via social media.

cs.LG↗

arXiv2024

Enhancing Hydrovoltaic Power Generation through Coupled Heat and Light-Driven Surface Charge Dynamics

Harnessing natural evaporation offers a sustainable and untapped pathway for next-generation energy technologies. Here, we present a unified physical and experimental framework for evaporation-driven hydrovoltaic (EDHV) systems that decouples and systematically controls the key interfacial processes underlying electricity generation from ambient heat and sunlight. By introducing an intermediate ion-conducting layer, we spatially and functionally separate the evaporative top interface from the silicon-dielectric nanopillar array at the bottom, enabling independent modulation of evaporation, ion transport, and interfacial chemical equilibrium. This decoupling strategy enhances device performance, facilitating the study of thermal and photo-induced charge generation, and improving ion migration and electricity generation. We develop a predictive equivalent electrical circuit model that captures the coupling between these processes through a transfer capacitance term, which we derive analytically as a function of geometric and material parameters. Our study reveals that capacitive photocharging and thermally modulated surface equilibria, rather than Faradaic or photothermal effects, are the dominant drivers of energy conversion when interfacial environments are adequately engineered. The device achieves a state-of-the-art open-circuit voltage of 1 V and a peak power density of 0.25 W/m2 at a 0.1 M salt concentration. Strategic variation of doping reveals that increasing silicon doping enhances voltage by 28% and power by 1.6 times, while switching the dielectric shell from TiO2 to Al2O3 boosts voltage (power) by up to 1.9 times (3.6 times). These findings offer insights for enhancing EDHV devices and suggest strategies that consider environmental conditions, water salinity, and material engineering to better harness waste heat and sunlight.

physics.chem-ph↗

arXiv2024

Explainable AI for Mental Disorder Detection via Social Media: A survey and outlook

Mental health constitutes a complex and pervasive global challenge, affecting millions of lives and often leading to severe consequences. In this paper, we conduct a thorough survey to explore the intersection of data science, artificial intelligence, and mental healthcare, focusing on the recent developments of mental disorder detection through online social media (OSM). A significant portion of the population actively engages in OSM platforms, creating a vast repository of personal data that holds immense potential for mental health analytics. The paper navigates through traditional diagnostic methods, state-of-the-art data- and AI-driven research studies, and the emergence of explainable AI (XAI) models for mental healthcare. We review state-of-the-art machine learning methods, particularly those based on modern deep learning, while emphasising the need for explainability in healthcare AI models. The experimental design section provides insights into prevalent practices, including available datasets and evaluation approaches. We also identify key issues and challenges in the field and propose promising future research directions. As mental health decisions demand transparency, interpretability, and ethical considerations, this paper contributes to the ongoing discourse on advancing XAI in mental healthcare through social media. The comprehensive overview presented here aims to guide researchers, practitioners, and policymakers in developing the area of mental disorder detection.

cs.LG↗

arXiv2023

Salinity-Dependent Interfacial Phenomena Towards Hydrovoltaic Device Optimization

Evaporation-driven fluid flow in porous or nanostructured materials has recently opened a new paradigm for renewable energy generation. Despite recent progress, major fundamental questions remain regarding the interfacial phenomena governing these so-called hydrovoltaic (HV) devices. Together with the lack of modelling tools, this limits the performance and application range of this emerging technology. By leveraging ordered arrays of Silicon nanopillars (NP) and developing a quantitative multiphysics model to study their HV response across a wide parameter space, this work reveals the complex interplay of surface-charge, liquid properties, and geometrical parameters, including previously unexplored electrokinetic interactions. Notably, we find that ion-concentration-dependent surface charge, together with ion mobility, dictates multiple local maxima in open circuit voltage, with optimal conditions deviating from conventional low-concentration expectations. Additionally, assessing the HV response up to molar concentrations, we provide unique evidence of ion adsorption and charge inversion for a number of monovalent cations. This effect interestingly enables the operation of HV devices even at such high concentrations. Finally, we highlight that, beyond electrokinetic parameters, geometrical asymmetries in the device structure generate an electrostatic potential that augments HV performance. Overall, our work, which lies in between single nanochannel studies and macro-scale porous system characterization, demonstrates that evaporation-driven HV devices can operate across a wide range of salinities, with optimal operating conditions being dictated by distinct interfacial phenomena. Thus it offers crucial insight and a design tool for enhancing the performance of evaporation-driven HV devices and enables their broader applicability across the salinity scale of natural and processed waters.

physics.flu-dyn↗

arXiv2021

dFDA-VeD: A Dynamic Future Demand Aware Vehicle Dispatching System

With the rising demand of smart mobility, ride-hailing service is getting popular in the urban regions. These services maintain a system for serving the incoming trip requests by dispatching available vehicles to the pickup points. As the process should be socially and economically profitable, the task of vehicle dispatching is highly challenging, specially due to the time-varying travel demands and traffic conditions. Due to the uneven distribution of travel demands, many idle vehicles could be generated during the operation in different subareas. Most of the existing works on vehicle dispatching system, designed static relocation centers to relocate idle vehicles. However, as traffic conditions and demand distribution dynamically change over time, the static solution can not fit the evolving situations. In this paper, we propose a dynamic future demand aware vehicle dispatching system. It can dynamically search the relocation centers considering both travel demand and traffic conditions. We evaluate the system on real-world dataset, and compare with the existing state-of-the-art methods in our experiments in terms of several standard evaluation metrics and operation time. Through our experiments, we demonstrate that the proposed system significantly improves the serving ratio and with a very small increase in operation cost.

math.OC↗

arXiv2018

Electrokinetic Energy Harvesting using Paper and Pencil

We exploit the combinatorial advantage of electrokinetics and tortutosity of cellulose-based paper network on a laboratory grade filter paper for the development of a simple, inexpensive, yet extremely robust (shows constant performance till 12 days) paper-and-pencil-based device for energy harvesting application. We successfully achieve to harvest maximum output power of 640 pW in single channel, while the same is significantly improved (by about 100 times) with the use of multichannel microfluidic array (maximum up to 20 channels). We envisage that such ultra-low cost devices may turn out to be extremely useful in energizing analytical microdevices in resource limited settings, for instance for extreme point of care diagnostics applications.

physics.app-ph↗

arXiv2017

Computing Influence of a Product through Uncertain Reverse Skyline

Understanding the influence of a product is crucially important for making informed business decisions. This paper introduces a new type of skyline queries, called uncertain reverse skyline, for measuring the influence of a probabilistic product in uncertain data settings. More specifically, given a dataset of probabilistic products P and a set of customers C, an uncertain reverse skyline of a probabilistic product q retrieves all customers c in C which include q as one of their preferred products. We present efficient pruning ideas and techniques for processing the uncertain reverse skyline query of a probabilistic product using R-Tree data index. We also present an efficient parallel approach to compute the uncertain reverse skyline and influence score of a probabilistic product. Our approach significantly outperforms the baseline approach derived from the existing literature. The efficiency of our approach is demonstrated by conducting extensive experiments with both real and synthetic datasets.

cs.DB↗