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Abstract Understanding the fluorescence resonance energy transfer (FRET) of metal nanoparticles at the atomic level has long been a challenge due to the lack of accurate systems with definite distance and orientation of molecules. Here we present the realization of achieving FRET between two atomically precise copper nanoclusters through cocrystallization-induced spatial confinement. In this study, we demonstrate the establishment of FRET in a cocrystallized Cu₈(p-MBT)₈(PPh₃)₄@Cu₁₀(p-MBT)₁₀(PPh₃)₄system by exploiting the overlapping spectra between the excitation of the Cu₁₀(p-MBT)₁₀(PPh₃)₄cluster and the emission of the Cu₈(p-MBT)₈(PPh₃)₄cluster, combined with accurate control over the confined space between the two nanoclusters. Density functional theory is employed to provide deeper insights into the role of the distance and dipole orientations of molecules to illustrate the FRET procedure between two cluster molecules at the electronic structure level. 

AI is now a cornerstone of modern dataset analysis. In many real world applications, practitioners are concerned with controlling specific kinds of errors, rather than minimizing the overall number of errors. For example, biomedical screening assays may primarily be concerned with mitigating the number of false positives rather than false negatives. Quantifying uncertainty in AI-based predictions, and in particular those controlling specific kinds of errors, remains theoretically and practically challenging. We develop a strategy called multidimensional informed generalized hypothesis testing (MIGHT) which we prove accurately quantifies uncertainty and confidence given sufficient data, and concomitantly controls for particular error types. Our key insight was that it is possible to integrate canonical cross-validation and parametric calibration procedures within a nonparametric ensemble method. Simulations demonstrate that while typical AI based-approaches cannot be trusted to obtain the truth, MIGHT can be. We apply MIGHT to answer an open question in liquid biopsies using circulating cell-free DNA (ccfDNA) in individuals with or without cancer: Which biomarkers, or combinations thereof, can we trust? Performance estimates produced by MIGHT on ccfDNA data have coefficients of variation that are often orders of magnitude lower than other state of the art algorithms such as support vector machines, random forests, and Transformers, while often also achieving higher sensitivity. We find that combinations of variable sets often decrease rather than increase sensitivity over the optimal single variable set because some variable sets add more noise than signal. This work demonstrates the importance of quantifying uncertainty and confidence—with theoretical guarantees—for the interpretation of real-world data. 

Exoskeletons have enormous potential to improve human locomotive performance. However, their development and broad dissemination are limited by the requirement for lengthy human tests and handcrafted control laws2. Here we show an experiment-free method to learn a versatile control policy in simulation. Our learning-in-simulation framework leverages dynamics-aware musculoskeletal and exoskeleton models and data-driven reinforcement learning to bridge the gap between simulation and reality without human experiments. The learned controller is deployed on a custom hip exoskeleton that automatically generates assistance across different activities with reduced metabolic rates by 24.3%, 13.1% and 15.4% for walking, running and stair climbing, respectively. Our framework may offer a generalizable and scalable strategy for the rapid development and widespread adoption of a variety of assistive robots for both able-bodied and mobility-impaired individuals. 

Abstract Transdermal drug delivery is of vital importance for medical treatments. However, user adherence to long-term repetitive drug delivery poses a grand challenge. Furthermore, the dynamic and unpredictable disease progression demands a pharmaceutical treatment that can be actively controlled in real-time to ensure medical precision and personalization. Here, we report a spatiotemporal on-demand patch (SOP) that integrates drug-loaded microneedles with biocompatible metallic membranes to enable electrically triggered active control of drug release. Precise control of drug release to targeted locations (<1 mm²), rapid drug release response to electrical triggers (<30 s), and multi-modal operation involving both drug release and electrical stimulation highlight the novelty. Solution-based fabrication ensures high customizability and scalability to tailor the SOP for various pharmaceutical needs. The wireless-powered and digital-controlled SOP demonstrates great promise in achieving full automation of drug delivery, improving user adherence while ensuring medical precision. Based on these characteristics, we utilized SOPs in sleep studies. We revealed that programmed release of exogenous melatonin from SOPs improve sleep of mice, indicating potential values for basic research and clinical treatments. 

Powered knee exoskeletons have shown potential for mobility restoration and power augmentation. However, the benefits of exoskeletons are partially offset by some design challenges that still limit their positive effects on people. Among them, joint misalignment is a critical aspect mostly because the human knee joint movement is not a fixed-axis rotation. In addition, remarkable mass and stiffness are also limitations. Aiming to minimize joint misalignment, this paper proposes a bio-inspired knee exoskeleton with a joint design that mimics the human knee joint. Moreover, to accomplish a lightweight and high compliance design, a high stiffness cable-tension amplification mechanism is leveraged. Simulation results indicate our design can reduce 49.3 and 71.9% maximum total misalignment for walking and deep squatting activities, respectively. Experiments indicate that the exoskeleton has high compliance (0.4 and 0.1 Nm backdrive torque under unpowered and zero-torque modes, respectively), high control bandwidth (44 Hz), and high control accuracy (1.1 Nm root mean square tracking error, corresponding to 7.3% of the peak torque). This work demonstrates performance improvement compared with state-of-the-art exoskeletons. 

Abstract Organic emitters that exhibit room‐temperature phosphorescence (RTP) in neat films have application potential for optoelectronic devices, bio‐imaging, and sensing. Due to molecular vibrations or rotations, the majority of triplet excitons recombine rapidly via non‐radiative processes in purely organic emitters, making it challenging to observe RTP in amorphous films. Here, a chemical strategy to enhance RTP in amorphous neat films is reported, by utilizing through‐space charge‐transfer (TSCT) effect induced by intramolecular steric hindrance. The donor and acceptor groups interact via spatial orbital overlaps, while molecular motions are suppressed simultaneously. As a result, triplets generated under photo‐excitation are stabilized in amorphous films, contributing to phosphorescence even at room temperature. The solvatochromic effect on the steady‐state and transient photoluminescence reveals the charge‐transfer feature of involved excited states, while the TSCT effect is further experimentally resolved by femtosecond transient absorption spectroscopy. The designed luminescent materials with pronounced TSCT effect show RTP in amorphous films, with lifetimes up to ≈40 ms, comparable to that in a rigid polymer host. Photoluminescence afterglow longer than 3 s is observed in neat films at room temperature. Therefore, it is demonstrated that utilizing intramolecular steric hindrance to stabilize long‐lived triplets leads to phosphorescence in amorphous films at room temperature.