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Next-generation biosensors for infectious disease surveillance: Innovations, challenges, and global health impact
No full textRecent advances in next-generation biosensors are transforming on how infectious diseases are monitored and offering rapid, real-time, highly sensitive detection of pathogens. Emerging platforms such as wearable, ingestible, and implantable biosensors are enabling continuous health tracking and facilitating early diagnosis, which is critical in managing outbreaks and preventing disease progression. Innovations in nanotechnology, electrochemical sensing, and machine learning are further enhancing the precision, scalability, and affordability of these tools. Biosensors hold particular promise for infectious disease surveillance, especially in low-resource environments where traditional diagnostics may be slow, costly, or unavailable. These technologies can support timely outbreak response, antimicrobial resistance tracking, and personalized treatment strategies. Despite these promising developments, several challenges like regulatory approval processes, limited access to research funding, and difficulties in integrating new biosensing technologies into existing healthcare infrastructure continue to hinder widespread adoption. Overcoming these barriers will require interdisciplinary collaboration among engineers, clinicians, public health experts, and data scientists. This chapter explores key technological breakthroughs, implementation challenges, and the expanding role of biosensors in public health. It also examines future directions and opportunities for strengthening infectious disease monitoring systems to improve patient outcomes and reinforce global health resilience
Aqueous Two-Phase System Enabled Dual-Layered Hydrogels with Tunable Nanoparticle Localization
No full textHydrogels are cross-linked polymeric networks capable of absorbing large amounts of water and have been widely explored for applications in drug delivery, tissue engineering, biosensing, and environmental remediation. The recent development of dual-layered hydrogels (DLHs) has expanded their potential, enabling spatial control over their mechanical and chemical properties. Furthermore, incorporating nanoparticles into each of these layers introduces unique optical, electronic, or catalytic properties, expanding the scope of these materials. However, complex processes often hinder the fabrication of DLHs, requiring precise control over the reaction conditions. This poses challenges in achieving a uniform nanoparticle distribution without aggregation. This work presents an approach for synthesizing DLHs with an aqueous two-phase system (ATPS), integrating phase separation and selective nanoparticle localization, followed by subsequent polymerization. Using a model system of poly(ethylene glycol) (PEG) and dextran (DEX) to generate the ATPS, we combined acrylamide, bis(acrylamide), and Irgacure (photoinitiator) to fabricate DLHs. Rheological studies provided insights into the viscoelastic behavior of DLHs, while mercury porosimetry was employed to analyze the pore size distribution. We illustrate that citrate-capped gold nanoparticles can be localized within the PEG-rich layer, while bovine serum albumin (BSA)-capped silver nanoparticles can be localized within the DEX-rich layer. We performed molecular dynamics simulations to investigate the factors contributing to the preferential partitioning. Finally, we exploit the fabricated DLHs with localized nanoparticles to catalyze the conversion of p-nitrophenol to p-aminophenol in the presence of gold nanoparticles. Upon laser irradiation at 450 nm, the reaction rate is further enhanced due to photothermal heating induced by the silver nanoparticles. We anticipate that this process offers a fabrication route toward multifunctional DLHs with spatially organized nanoparticles, opening avenues for advanced catalytic and responsive materials
Theoretical study of linear and non-linear optical properties of small CaCn (n = 2�7) clusters
No full textThe linear and NLO properties of small calcium doped carbon cluster CaCn (n = 2�7) were investigated in the framework of time-dependent density functional theory (TD-DFT) with CAM-B3LYP/6-311+G(d). The absorption wavelength (?), oscillatory strength (f), transition energy (Eex), and nature of transitions of major excitations have been calculated. It was shown that the most intense peak was fall in the UV region of the spectrum. The most intense peaks among all studied clusters for CaC6was found at 230 nm with oscillatory strength of 0.261, and excitation was due to H-2 ? L, H-1 ? L + 1, H ? L + 13 transition. We observe a few peaks with weaker peaks in the visible region for CaC2, CaC5, and CaC7at560 nm, 525 nm, and 467, respectively. The average polarizability ? and first hyperpolarizability ?tot have been calculated and found that the polarizability of studied clusters increases with the number of carbon atoms in the cluster. Hyperpolarizability of CaC2 and CaC5 was calculated as 2874.7 a.u. and 1329.9 a.u. Indicating strong NLO prospects among all studied clusters. The optical response of studied clusters suggests that these materials can be considered as auspicious for optoelectronic devices. � 2024 Elsevier B.V., All rights reserved
On the sensitivity normalization for blue stimulated luminescence of quartz
No full textIn the single aliquot regeneration (SAR) dating method for quartz, the maximum dating limit depends on the saturation dose of sensitivity-corrected luminescence signal (L/T) and is generally found to be around ∼250 Gy. Since saturation is the restraining aspect in luminescence dating, it is important to understand the factors that influence it. This paper, investigates the blue stimulated luminescence (BSL) signals of quartz of different provenance using the multiple aliquot additive dose (MAAD) methodology. Results show that the BSL signal increases beyond the saturation limits of SAR. The early saturation in the SAR is observed primarily due to a disproportional increase in the test dose signal (T) at higher doses resulting from its dependence on the prior regeneration dose. The work further searches for normalization methods, which are independent of regeneration doses at high doses. Results show that zero glow thermo-luminescence (TL), BSL (after annealing, UV emission) and TL (after annealing, blue emission) normalization carry negligible previous dose information. These normalization signals are tested for constructing dose-response curve (DRC) using MAAD and multiple aliquot regeneration (MAR) methods. Laboratory generated DRCs are found to be best fitted with double saturating exponential with a second exponential saturation dose of 5800 ± 800 Gy. However, the scatter in the BSL, multiple aliquot data at higher doses (∼kGy) is significant and needs future investigation. The proposed methodology yields higher equivalent doses for the natural samples than SAR but still found to be lower than expected doses
Physics-guided machine learning of excited-state properties for the design of high-performance TADF emitters
No full textThe rational design of thermally activated delayed fluorescence (TADF) and inverted singlet-triplet (INVEST) emitters demands accurate prediction of critical photophysical properties, particularly singlet-triplet energy gaps (ΔEST) and oscillator strengths (f). Conventional machine learning (ML) models often neglect the underlying physics, limiting their transferability and interpretability across chemical space. In this work, we develop a physics-informed machine learning (PIML) framework that leverages physically meaningful molecular descriptors to predict ΔEST and f with high accuracy and robust generalization. Training on a chemically diverse dataset of over 39 000 compounds, our models achieve correlation coefficients (r) between 0.77 and 0.88 and mean absolute errors (MAE) below 0.1 eV for ΔEST and 0.02 for f on unseen test data. The reliability of the PIML models is further validated via leave-one-out cross-validation and external datasets, including 28 experimentally reported emitters, for which our model outperforms state-of-the-art quantum chemical and ML approaches. Beyond predictive accuracy, integrating interpretability tools reveals the exchange integral, dynamic spin polarization, and excited-state energies as dominant factors controlling the target properties—offering mechanistic insights often inaccessible in standard black-box models. Finally, leveraging the predictive power of the trained models, we performed high-throughput screening of 400 newly designed TADF emitters, successfully identifying promising candidates with optimal ΔEST and f combinations for OLED applications. This study highlights the strength of combining physical intuition with data-driven modeling, offering an efficient, scalable, and interpretable route for accelerating the discovery of next-generation optoelectronic materials
Prime and punishment: Effect of religious priming and group membership on prosocial behavior
No full textThis research investigates the influence of religious priming and group membership on prosocial behavior, measured by the willingness to donate to fictitious charities in a hypothetical scenario. A sample of 258 Hindu participants, averaging 21.3 years of age, were engaged in an online study designed on PsyToolkit. The study employed a 3*2 factorial design, wherein participants were subliminally primed with concepts of “reward” and “punishment” within religious contexts through a lexical decision task. Post-priming, individuals were presented with a decision to allocate a portion of a potential lucky draw prize to selected charities, which represented either their religious ingroup or an outgroup. The findings demonstrated that religious priming did not significantly enhance prosocial behavior toward either group. Bayesian analysis supported the absence of an effect for priming and group conditions. Moreover, other variables such as religiosity, sex, and political orientation showed no substantial effect on the likelihood of charitable giving. However, consistent with previous research, past charity behavior emerged as the most salient predictor of prosocial behavior, underscoring the importance of experiential factors in shaping altruistic tendencies. The study reflects on the influence of India’s secular and culturally rich backdrop, which may modulate the propensity to engage in charitable acts, especially when the donations come from unexpected gains rather than one’s own money. It reveals that prosocial behavior is shaped by a mix of situational and personal factors, not just religious beliefs. The research contributes to the understanding of prosocial behavior in diverse religious contexts and the role of personal experience in predicting charitable actions, advocating for further investigation into these dynamics
Seasonal variations in thermal comfort: assessing biophysical impacts of green infrastructure in a hot-arid urban setting
No full textIn historically hot-arid climates like Ahmedabad, the urban environment amplifies thermal discomfort across seasons, with extreme heat dominating summer and a notable drop in wintertime temperatures. These seasonal contrasts highlight the need to evaluate how green infrastructure (GI) affects biophysical conditions and thermal comfort throughout the year. We specifically examine the effects of three GI interventions—green roofs, permeable pavements, and bioretention cells—that are feasible for cities with limited space availability and have been adopted as measures to reduce urban flooding. Our study investigates how these individual GIs influence the thermal responses of diverse population groups during both summer and winter, acknowledging the varied physiological and demographic sensitivities to seasonal extremes. Using high-resolution (3 meters) ENVI-met simulations for representative summer and winter days, we assess the thermal comfort of individuals of varying ages, genders, and social strata, using parameters like clothing insulation, metabolic rate, body weight, and surface area. We also account for seasonal shifts in thermal comfort definitions, where summer emphasizes mitigating heat stress and winter addresses cold exposure. Our results demonstrate significant seasonal differences in how GIs modulate microclimate and influence thermal responses, with implications for equitable urban design. By addressing seasonal and demographic variability, this study provides actionable insights for tailoring GI strategies to improve thermal comfort year-round in hot-arid urban contexts
