Perovskite-silicon triple-junction photovoltaics offer efficiency gains beyond dual-junction devices, but at the expense of added complexity (1). Here, we address two key bottlenecks in perovskite-silicon-based triple-junction solar cells: reduced open-circuit voltage in the wide-bandgap top-cell and limited photocurrent generation in the middle-cell (1, 2). A non-volatile additive, 4-hydroxybenzylamine, regulates wide-bandgap perovskite crystallization and passivates defects, promoting oriented growth and suppressing non-radiative recombination. Together with improved energy-level alignment, this yields open-circuit voltages of up to 1.405 V and enhanced stability. To overcome the current limitations in the middle-cell, a three-step deposition strategy enables the formation of thick, low-bandgap perovskite absorbers while preserving microstructural integrity and enhancing electron extraction. In addition, low-refractive-index SiOx nanoparticles that accumulate in the front valleys of the textured silicon bottom-cell act as an optical middle-reflector, enhancing light absorption in the middle-cell. These advances are then combined in 1 cm² perovskite-perovskite-silicon devices, achieving a certified efficiency of 30.02%.

Alkynes are widely used as feedstock chemicals and functional groups in organic chemistry1,2. However, while the hydrogenation from an alkyne to an alkene is well established, typical methods for the reverse reaction - conversion of an alkene to an alkyne, are based on elimination chemistry reported in the 1860s3 and use forcing conditions (strong base or high temperatures)4-6. This precludes more general application on functional molecules. Here we report a recyclable selenanthrene reagent that mediates alkenes desaturation to alkynes under mild conditions. This method shows broad compatibility with both classical leaving groups and sensitive functional groups, enabling application late-stage in the efficient synthesis of complex alkynes. Moreover, this platform enables Z/E alkenes configuration inversion or sorting that are inaccessible with existing methods, highlighting its potential for diverse downstream derivatizations.

Insulin resistance (IR), a primary precursor to type 2 diabetes, is characterized by impaired insulin action in tissues1. However, diagnostic methods remain expensive and inaccessible, which hinders early intervention2,3. Here we present the WEAR-ME study, a large, remotely conducted study of IR (n = 1,165 participants; median body mass index (BMI) = 28 kg m-2, median age = 45 years, median haemoglobin A1c (HbA1c) = 5.4%) that uses time-series data from wearable devices and routine blood biomarkers to train deep neural networks against a ground-truth measure of IR (homeostatic model assessment of IR; HOMA-IR). Using a HOMA-IR cut-off of 2.9, our multimodal model achieved robust performance (area under the receiver operating characteristic curve (AUROC) = 0.80, sensitivity = 76%, specificity = 84%) with data from wearable devices, together with demographic and routine blood biomarker data. To enhance the use of time-series data from wearables, we fine-tuned a wearable foundation model (WFM) pretrained on 40 million hours of sensor data. In an independent validation cohort (n = 72), a model integrating WFM-derived representations with demographic data surpassed a demographics-only baseline (AUROC = 0.75 versus 0.66). Moreover, adding WFM-derived representations to a model with demographics, fasting glucose and a lipid panel substantially improved performance, compared with an identical model without data from wearables (AUROC = 0.88 versus 0.76). We integrate IR prediction into a large language model to contextualize the results and facilitate personalized recommendations. This work establishes a scalable, accessible framework for the early detection of metabolic risk, which could enable timely lifestyle interventions to prevent progression to type 2 diabetes.