Publications

Water flow energy in rivers and lakes is a huge clean energy source widely distributed in nature. Its low water velocity makes it difficult for electromagnetic generators to effectively harvest this energy. Therefore, harvesting high-entropy water flow energy is of great significance to the development of distributed power supply and sensing. In this study, we present a double helix rotating triboelectric nanogenerator (DHR-TENG) that can effectively convert the ultra-low water flow energy into electrical energy. The DHR-TENG is designed to optimize space utilization and has a reciprocating transmission mechanism to ensure the continuous operation, achieving a surprisingly high charge density of 356.69 μC⋅m−3 and peak power density of 11.66 W⋅m−3, and compared with similar structures reported by predecessors, the maximum elevation is about 242 times and 2 times, respectively. At ultra-low water flow velocity of 0.4~0.8 m/s, DHR-TENG has stable electrical output performance, and can drive self-powered temperature sensor systems and wireless signal transmission systems by harvesting water flow energy. This work not only effectively improves the electrical output performance at ultra-low water flow velocity, but also helps to promote environmental monitoring and provides a feasible method for establishing early warning in the environment.

Abstract The harvesting and utilization of vibration energy from transmission lines can effectively advance the development of smart grids. However, variations in wind direction significantly affect the vibration status of transmission lines‐a problem often overlooked in research. This paper clarifies the gap problem of wind‐induced vibration energy harvesting in transmission lines for the first time, and presents a dual‐mode complementary strategy of wind‐induced vibration for transmission lines. The core innovation is to achieve self‐adaptive omnidirectional wind domain energy harvesting by wind‐vibration energy collaborative response, which overcomes the limitation of single vibration energy harvesting in transmission lines that depend on a specific wind direction. The designed wind‐vibration hybrid nanogenerator (W‐VHNG) ingeniously integrates two different TENG mechanisms to achieve broadband breeze vibration energy capture (vibration frequency range of 5–60 Hz), wide wind speed and direction adaptability (wind speed range of 4.2–14 m s −1 and the wind direction range of ±30°), and mW‐scale power output (maximum mean power of 1.33 mW). Finally, the potential of the W‐VHNG for self‐powered sensing of transmission lines is highlighted through three application strategies. This research provides a new solution for the sustainable harvesting of complex and variable high‐entropy energy in transmission lines.

Reinforcement learning with verifiable rewards has become a common way to improve explicit reasoning in large language models, but final-answer correctness alone does not reveal whether the reasoning trace is faithful, reliable, or useful to the model that consumes it. This outcome-only signal can reinforce traces that are right for the wrong reasons, overstate reasoning gains by rewarding shortcuts, and propagate flawed intermediate states in multi-step systems. To this end, we propose TraceLift, a planner-executor training framework that treats reasoning as a consumable intermediate artifact. During planner training, the planner emits tagged reasoning. A frozen executor turns this reasoning into the final artifact for verifier feedback, while an executor-grounded reward shapes the intermediate trace. This reward multiplies a rubric-based Reasoning Reward Model (RM) score by measured uplift on the same frozen executor, crediting traces that are both high-quality and useful. To make reasoning quality directly learnable, we introduce TRACELIFT-GROUPS, a rubric-annotated reason-only dataset built from math and code seed problems. Each example is a same-problem group containing a high-quality reference trace and multiple plausible flawed traces with localized perturbations that reduce reasoning quality or solution support while preserving task relevance. Extensive experiments on code and math benchmarks show that this executor-grounded reasoning reward improves the two-stage planner-executor system over execution-only training, suggesting that reasoning supervision should evaluate not only whether a trace looks good, but also whether it helps the model that consumes it. Our code is available at: https://github.com/MasaiahHan/TraceLift

Abstract Despite recent progress in producing perovskite nanowires (NWs) for optoelectronics, it remains challenging to solution‐print an array of NWs with precisely controlled position and orientation. Herein, we report a robust capillary‐assisted solution printing (CASP) strategy to rapidly access aligned and highly crystalline perovskite NW arrays. The key to the CASP approach lies in the integration of capillary‐directed assembly through periodic nanochannels and solution printing through the programmably moving substrate to rapidly guide the deposition of perovskite NWs. The growth kinetics of perovskite NWs was closely examined by in situ optical microscopy. Intriguingly, the as‐printed perovskite NWs array exhibit excellent optical and optoelectronic properties and can be conveniently implemented for the scalable fabrication of photodetectors.

A systematic theoretical analysis for contact-separation mode TENGs is presented, including theoretical models, analysis methods, validation approaches, and extended analysis for applications.

Abstract Industrialization and anthropogenic activities are expected and unavoidable to consummate the current resources of humankind, which also lead to accidents in the laboratory, chemical plants, or other high risk areas that cause severe burns, or even casualties. Increased casualties in such accidents are due to inappropriate safety measures and prevention. Here, a smart anti‐chemical protective suit with a bio‐motion energy harvesting and self‐powered safety monitoring system is demonstrated, which can protect the body from chemical harm, detect sudden chemical spills, monitor human real‐time living signals, and trigger alarms in an emergency. Particularly, a fabric triboelectric nanogenerator (F‐TENG), which is fabricated by the all‐fiber single‐electrode triboelectric nanogenerator yarn (SETY), works as the basic elements of the intelligent suit. The SETY with core‐shell structured design shows a high sensitivity to the corrosive liquids including acids and alkalis. Furthermore, the working principle of the yarn based nanogenerator that is powered by contacting with acid liquid droplets is demonstrated for the first time. In addition, discretionary thickness, permeability, and any other functionalities are also achieved by taking advantage of the fabric structure. This self‐powered smart anti‐chemical protective suit equipped with a real time monitoring system will benefit the wearer who works in a very high‐risk environment.

Contact electrification (CE) (or triboelectrification) is the phenomena where charges are produced through physical contact between two materials. Here we report the atomic featured photon emission spectra during CE between two solid materials. Photon emission provides the evidence that electron transfer takes place at the interface from an atom in one material to another atom in the other material during CE. This process is the contact electrification induced interface photon emission spectroscopy (CEIIPES). It naturally paves a way to a spectroscopy corresponding to the CE at an interface, which might impact our understanding of the interaction between solids, liquids, and gases. The physics presented here could be expanded to Auger electron excitation, x-ray emission, and electron emission in CE for general cases, which remain to be explored. This could lead to a general field that may be termed as contact electrification induced interface spectroscopy (CEIIS).

Recently, a triboelectric generator (TEG) has been invented to convert mechanical energy into electricity by a conjunction of triboelectrification and electrostatic induction. Compared to the traditional electromagnetic generator (EMG) that produces a high output current but low voltage, the TEG has different output characteristics of low output current but high output voltage. In this paper, we present a comparative study regarding the fundamentals of TEGs and EMGs. The power output performances of the EMG and the TEG have a special complementary relationship, with the EMG being a voltage source and the TEG a current source. Utilizing a power transformed and managed (PTM) system, the current output of a TEG can reach as high as ∼3 mA, which can be coupled with the output signal of an EMG to enhance the output power. We also demonstrate a design to integrate a TEG and an EMG into a single device for simultaneously harvesting mechanical energy. In addition, the integrated NGs can independently output a high voltage and a high current to meet special needs.

One of the trends in the era of post-Moore's Law is to develop sophisticated electronic devices that integrate the function of sensation/data-storage/computation for diverse applications. Two-dimensional semiconductor transistor based on charge storage mechanism is a promising alternative for future information devices. Here, we propose a neuromorphic triboelectric charge-trapping MoTe2 transistor with stacked high-k dielectric structure, aiming at mechano-driven logic-in-memory for neuromorphic computation. Gating by triboelectric potential, the neuromorphic device exhibits excellent electrical performance, including a high switching ratio (>105), low off-state current (~0.6 pA), and robust cyclic stability. By controlling the trapped charges in the stack gate structure via tribopotential modulation, the conductivity state of the MoTe2 channel can be readily set, realizing an excellent mechano-driven nonvolatile memory with a retention time up to 104 seconds, stable switching behavior over 100 cycles, and 8 levels of multi-level data storage capability. A mechano-driven programmable inverter can also be realized by serially connecting a load resistor. Besides, the triboelectric charge-trapping transistor is ready to emulate typical synaptic characteristics in low energy levels (~147 fJ). Based on the finely tunable conductivity through tribopotential, a mechano-assisted artificial neural network is demonstrated to recognize handwritten digits with an accuracy of approximately 88.59%. These results highlight the great significance of the triboelectric charge-trapping transistor in mechanical-assisted real-time interaction, low-energy data storage, and neuromorphic computation.