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Komal Singh

Publications and source records attributed to Komal Singh.

4 recordsLinked to original sources
arXiv2025

Hydrothermal synthesis of SnO2 particles for the degradation of Methylene Blue (MB) dye in presence of sunlight

Three distinct samples were proceed through synthesis utilizing the hydrothermal method to develop tin dioxide (SnO2) nanoparticles. Consistency in all other parameters was ensured by maintaining a constant temperature and time throughout the synthesis. X-ray diffraction (XRD) and scanning electron microscopy (SEM) were used to examine how the surfactant affected the structural, morphological, and crystallographic characteristics. A tetragonal rutile SnO2 phase was confirmed to have formed by XRD investigation, with high crystallinity indicated by strong diffraction peaks. Higher precursor concentration samples showed aggregation, indicating that the smaller size of the nanoparticles caused them to interact. These findings show that changing the surfactant has a major impact on the crystallinity, size, and shape of SnO2 nanoparticles, which makes this technique establish for certain uses, including energy storage, gas sensors, and photocatalysis. Additionally, we examined the photocatalytic activity of the aforementioned samples, indicating from the UV-vis characterization results that the sample containing PEG as a surfactant performs better than others.

cond-mat.mtrl-sci↗
arXiv2025

Towards an AI-Augmented Textbook

Textbooks are a cornerstone of education, but they have a fundamental limitation: they are a one-size-fits-all medium. Any new material or alternative representation requires arduous human effort, so that textbooks cannot be adapted in a scalable manner. We present an approach for transforming and augmenting textbooks using generative AI, adding layers of multiple representations and personalization while maintaining content integrity and quality. We refer to the system built with this approach as Learn Your Way. We report pedagogical evaluations of the different transformations and augmentations, and present the results of a a randomized control trial, highlighting the advantages of learning with Learn Your Way over regular textbook usage.

cs.CY↗
arXiv2025

Evaluating Gemini in an arena for learning

Artificial intelligence (AI) is poised to transform education, but the research community lacks a robust, general benchmark to evaluate AI models for learning. To assess state-of-the-art support for educational use cases, we ran an "arena for learning" where educators and pedagogy experts conduct blind, head-to-head, multi-turn comparisons of leading AI models. In particular, $N = 189$ educators drew from their experience to role-play realistic learning use cases, interacting with two models sequentially, after which $N = 206$ experts judged which model better supported the user's learning goals. The arena evaluated a slate of state-of-the-art models: Gemini 2.5 Pro, Claude 3.7 Sonnet, GPT-4o, and OpenAI o3. Excluding ties, experts preferred Gemini 2.5 Pro in 73.2% of these match-ups -- ranking it first overall in the arena. Gemini 2.5 Pro also demonstrated markedly higher performance across key principles of good pedagogy. Altogether, these results position Gemini 2.5 Pro as a leading model for learning.

cs.CY↗