Nathaniel Jeffries | Searcharxiv

arXiv2026

Observation of long-lived spin order in nanoconfined water

Liquids confined to nanometer-scale geometries exhibit behavior that departs markedly from their bulk counterparts, yet studying their dynamics under controlled conditions remains experimentally challenging. Here, we use nitrogen-vacancy (NV) center nuclear magnetic resonance (NMR) spectroscopy to probe water confined in 5.6 nm channels as a function of temperature. The system remains liquid throughout the investigated temperature range and exhibits strongly suppressed diffusivity, enabling direct detection of its 1H NMR spectrum. Occasionally, the proton resonance transforms into a doublet with a splitting of several tens of kilohertz, which we tentatively attribute to hyperfine interactions mediated by long-lived paramagnetic charge complexes, in turn seeded by solvated electrons optically injected during laser illumination. The intermittent appearance of this feature suggests a metastable state comprising a correlated population of charge-hydration complexes extending throughout the confined liquid.

cond-mat.mes-hall↗

arXiv2026

MetaboNet-Bench: A Multi-modal Benchmark for Glucose Forecasting in Type 1 Diabetes

Glucose forecasting algorithms are an important aspect of glycemic control management in type 1 diabetes. So far, the research community has developed numerous algorithms and models for forecasting. However, it is well-recognized that the lack of standardized model performance evaluation benchmarks makes fair comparison difficult and hinders further innovation, and thus benchmark standardization is in urgent need. Furthermore, many published glucose forecasting algorithms are limited to CGM data alone, ignoring other multimodal signals such as insulin dosing and carbohydrate intake. Here, we introduce MetaboNet-Bench, a benchmark for multimodal glucose forecasting for patients with type 1 diabetes that provides an extensible open-source evaluation framework for comparison of glucose forecasting algorithms that leverage glucose, insulin, and carbohydrate data. We then demonstrate its utility by benchmarking several recently published glucose forecasting models and a custom multimodal time-series model, representing different model architectures. The results show that the benefit of adding data modalities is conditioned on the complexity of the model and that incorporating more clinical metrics helps identify meaningful gaps to fill for future research.

cs.LG↗