Indian Institute of Technology Bhubaneswar

Instead of a simple core-shell giant quantum dot (GQD), in graded alloy GQDs, the graded alloy shell minimises lattice-mismatch-induced strain. Here, we synthesized a series of graded alloy GQDs of CdZnSeS/ZnSe1-ySy/Zn1-xCdxS architecture (y = 0-1; x = 0, 0.25, 0.5, 1), purposefully tuning the outermost shell composition to introduce a sharp strain gradient in the outer layer through controlled lattice mismatch, exhibiting long-term environmental stability, high thermal stability, and photostability. As the cadmium content in the outer layer increased, the photoluminescence became broad and red-shifted, with longer lifetimes. Microstrain analysis, together with wavelength-resolved photoluminescence excitation and time-resolved photoluminescence, confirms the emergence of strain-induced heterogeneous luminescent states. Ultrafast transient absorption and PL upconversion further demonstrated the rapid depletion of excitons and shallow traps, accompanied by the concurrent formation of long-lived photoluminescent states. Interaction with benzoquinone confirms facile electron extraction from the core via these luminescent states, with extraction efficiency scaling with the density of these states, and the immediate blueshift in photoluminescence constrains the long-lived states to surface proximity. Thus, outer layer strain engineering furnishes robust GQDs that co-optimize stability and charge extraction through strain-induced luminescent states, offering a promising design principle for photocatalytic applications.

Maintaining a healthy lifestyle requires proper nutrition awareness, but people frequently find it difficult to decipher food labels, comprehend ingredient compositions, and meaningfully assess nutritional value. The accuracy of traditional nutrition apps is limited when handling a variety of food items, handwritten labels, or blurry images because they rely on static databases and keyword-based matching. In order to overcome these obstacles, we suggest an AI-Powered Nutrition Insight System that combines an image-to-text pipeline, Groq-accelerated LLM inference, and Retrieval-Augmented Generation (RAG). The system uses multimodal models to extract nutritional text from images, embeds that text into vector representations, and uses a RAG module in conjunction with RapidFuzz-based similarity matching to retrieve precise nutrition insights. The system was evaluated using a diverse set of food label images and textual dietary inputs to assess retrieval accuracy, response latency, and contextual reasoning performance. Experimental evaluation demonstrates significant improvements in retrieval precision and response time compared to baseline keyword-based search techniques. The Groq-accelerated inference pipeline further enables low-latency responses, supporting near real-time nutrition analysis and interactive user guidance. Compared to baseline search techniques, experimental evaluation shows big improvements in speed, accuracy, and contextual reasoning. The proposed system therefore provides an efficient, scalable, and context-aware framework for automated nutrition analysis, enabling users to better understand dietary information and make informed food choices.

Glacial lake outburst floods (GLOFs) originating from breaches of moraine-dammed lakes represent serious risk in high mountain regions around the world, as recently exemplified by the 2023 South Lhonak GLOF. Informed disaster risk reduction requires reliable modelling inputs. While computational capacities and modelling tools improved greatly in past years, some of the key input parameters remain poorly addressed. Among these, the estimation of realistic potential flood volume represents a major challenge. To bridge this gap, we compiled a dataset of breached moraine-dammed outbursts and calculated mean slope of the breached channel after the GLOF, as it can be used to approximate breach depth / lake level drop and so volume. The mean slope of the breached channels in the dataset varies from 2.3° (Q0) to 19.5° (Q4), while Q1 is 3.1° and Q3 is 6.6° while the median (Q2) is 5.0° and the mean is 5.5°. We found that a little change in slope of the breached channel changes estimated potential flood volume substantially, especially in cases of rather flat wide dams, suggesting high sensitivity of predictive GLOF modelling studies to this parameter.EGUsphere preprint: https://doi.org/10.5194/egusphere-2025-413

Conductive hydrogel systems possessing adequate mechanical resilience and skin adhesiveness is desirable to promote their application in flexible electronics applications. In this report, carbon nanotube (CNT) incorporated ionic hydrogel system based on poly acrylamido-2-methylpropane sulfonic acid (PAMPS) and polyacrylamide (PAAm) segments is synthesized and studied for monitoring various bodily motions. The resulting hydrogel system displays adequate tensile strength, ionic conductivity and adhesiveness desirable for the strain sensing application. The system also displays effective gauge factor value for various extension ranges supporting its suitability for strain sensing applications. Overall, the study reveals that similar strategy may be adopted to improve the conductivity of other conventional hydrogel systems and CNT structure may be further tailored to optimize the performance in future.

A permafrost probability index (PPI) based on rock glacier inventory and machine learning models, including random forest, support vector machine, artificial neural network, and logistic regression, was generated for Kinnaur district, Himachal Pradesh, India. Intact rock glaciers were considered the dependent variable, and elevation, slope, aspect, and potential incoming solar radiation were used as independent variables to generate a spatially distributed, high-resolution permafrost probability index. Daily weather station data and daily multitemporal MODIS satellite data were used to train a linear regression model to predict the annual 0℃ isotherm in the region for the period of 2023-24, aiming to understand potential degradation by overlaying the isotherm on permafrost distribution. The random forest technique produced the best results with an overall accuracy of 89.43%. Seven glacial lakes were identified as located in potentially permafrost-degraded slopes, and the Kashang glacial lake was selected for detailed downstream glacial lake outburst flood process chain modeling based on its size, moraine-dammed proglacial setting, and potential downstream impact. The volume of the lake was estimated to be 8.6 × 106 m3 by extrapolating the contours from overdeepening of the main glacier. Three sources of avalanches were identified based on permafrost degradation and slopes greater than 30 degrees. Subsequently, three scenario-based process chains for glacial lake outburst floods were modeled. We simulate avalanche initialization, displacement wave generation, overtopping, moraine erosion, and downstream flooding. The modelling results revealed that the potential GLOF can cause a peak discharge of 16,167 ms⁻¹, and floodwater can reach the Kashang, where a hydropower is located, within 16 minutes in the high-magnitude scenario. The findings can give important insights into GLOF hazard mitigation in the valley and can aid as preliminary data for various stakeholders working towards mitigating glacier-related hazards.Keywords: Permafrost, GLOF, machine learning, r.avaflow, Himalaya

Abstract. Hazard and risk from glacial lake outburst floods (GLOFs) in Bhutan have traditionally been assessed with limited consideration of the downstream exposure and vulnerability associated with individual lakes. However, exposure and vulnerability are key components of risk, and when explicitly attributed to each lake, can provide a more robust basis for prioritising hazard investigations and mitigation efforts. We modelled hypothetical GLOF scenarios for all glacial lakes with an area greater than 0.05 km2 and located within 1 km of a glacier terminus. We then determined GLOF risk by explicitly accounting for downstream impacts using depth–velocity outputs at each exposed element affected by the simulated GLOF from each lake, as well as the vulnerability of the affected community. Our study shows that approximately > 11 000 people, > 2500 buildings, > 250 km of road, > 400 bridges and ∼ 20 km2 of farmland are exposed to potential GLOF in Bhutan. We classified lake130 (Thorthormi Tsho) as a very high hazard glacial lake in Bhutan, five lakes as high hazard and 22 other lakes as moderate hazard. Among these high hazard glacial lakes, three of them: lake93 (Phudung Tsho), lake251, and lake278 (Wonney Tsho) were not recognised as being high hazard in previous studies. Five downstream local government administrative units (LGUs) were associated with very high GLOF risk, while eight others are associated with high GLOF risk. Five of these very high and high risk LGUs had not been previously documented as being at risk from GLOF. Our study underscores the significance of integrating potential inundation mapping and downstream exposure data to define high hazard glacial lakes. We recommend strengthening and expanding the existing GLOF preparedness and risk mitigation efforts in Bhutan, particularly in the LGUs, as having high GLOF risk identified in this study, to reduce potential future damage and loss.