Publications

'Peltier Element' published in 'Encyclopedia of Nanotechnology'

Abstract The exploration of triboelectricity at the liquid–solid (L‐S) interface has sparked significant interest due to its potential for sustainable energy harvesting and technological advancement. Motivated by the need for innovative energy solutions and the unique advantages offered by liquid‐based environments, a comprehensive review of the fundamental concepts, mechanisms, and applications of liquid–solid triboelectric nanogenerators (TENGs) is provided. Three basic working modes of liquid–solid TENGs and the distinct properties and mechanisms of each model are discussed systematically. The physical fundamental of liquid–solid TENGs is further investigated, which includes “Wang Transition”, Wang's Hybrid Electric Double Layer model, tribovoltaic effect, equivalent circuit model, and the mechanisms of liquid–solid contact electrification based on density functional theory. Understanding charge transfer and charge distribution at the liquid–solid interface is also crucial to confirm the underlying mechanisms of liquid–solid TENGs. Finally, a broad range of applications of liquid–solid TENGs are explored, emphasizing their potential in addressing energy challenges and complex interdisciplinary issues that link the disciplines of materials science, chemistry, physics, and even electrical engineering.

We present Self-powered Paper INterfaces (SPIN) combining folding paper creases with triboelectric nanogenerator (TENG). Embedding TENG into paper creases, we developed a design editor and set of fabrication techniques to create paper-based interfaces that power sensors and actuators. Our SPIN design editor enables users to design their own crease pattern by changing parameters, embed power generating modules into the design, estimate total power generation, and export the files. Then following the fabrication instructions, users can cut and crease materials, and assemble them to build their own interfaces. We employ repetitive push-and-pull based embodied interactions with the mechanism of paper creases and demonstrate four application examples that show new expressive possibilities applying different scales of embodied interactions.

Abstract The widespread deployment of Internet of Things (IoT) networks has actualized omnipresent device interconnectivity. Despite technological advancements, IoT edge devices suffer persistent energy bottlenecks from suboptimal coordination of power acquisition and adaptive management. Self‐charging power sources (SCPS) aim to achieve autonomous operation through monolithic integration of three core components: energy harvesters, power management circuits, and supercapacitors/batteries. These devices enable continuous ambient energy harvesting, providing uninterrupted power supply for wearable electronics and IoT applications. Nevertheless, material selection and component design remain key challenges in SCPS development. As an essential artificial intelligence paradigm, machine learning (ML) enables data‐driven material and structural design based on historical experimental datasets, thereby elevating SCPS performance to superior level. This paper reviews the development of SCPSs and the application of ML in SCPSs, with a particular focus on SCPSs with triboelectric nanogenerators (TENGs) and supercapacitors (SCs). A generalized ML workflow with suggested parameters is proposed to guide the performance prediction of TENG by incorporating previous theoretical research. Additionally, ML‐guided design of carbon‐based SC materials and computer‐aided suppression of self‐discharge performance are selected as typical examples to discuss. The combination of ML and SCPS is expected to push forward more efficient and self‐sufficient IoT applications.

Abstract The exploration of water wave energy to generate electric power is a promising and valuable way to solve global energy crisis. Triboelectric nanogenerators (TENGs) are considered as one of the most promising approaches for harvesting water wave energy. Here, a butterfly‐inspired triboelectric nanogenerator (B‐TENG) with spring‐assisted four‐bar linkage is fabricated to acquire multidirectional water wave energy from different types of movements generated by the device. The arc‐shaped outer shell can efficiently absorb the impact force of water wave and inner spring‐assisted four‐bar linkage can induce multiple contact–separation motion of TENG module, both of which allow the B‐TENG to harvest energy from different types of low‐frequency waves. The short‐circuit current and open‐circuit voltage of this B‐TENG device can reach 75.35 µA and 707.01 V, while a maximum output power density of 9.559 W m −3 can be achieved and 180 light‐emitting diodes can be directly lightened up by this B‐TENG. The energy generated from this B‐TENG can also be stored in a capacitor and to drive some marine sensor devices, such as an electronic thermometer. This proposed B‐TENG device is specially designed for multidirectional water energy harvesting and it may have good application prospects for ocean information monitoring and power supply to oceanic islands.

ABSTRACT Background Long COVID, a heterogeneous condition characterized by a range of physical and neuropsychiatric presentations, can be presented with a proportion of COVID‐19‐infected individuals. Methods Transcriptomic data sets of those within gene expression profiles of COVID‐19, long COVID, and healthy controls were retrieved from the GEO database. Differentially expressed genes (DEGs) falling under COVID‐19 and long COVID were identified with R packages, and contemporaneously conducted module detection was performed with the Modular Pharmacology Platform ( http://112.86.129.72:48081/ ). The integration of both DEGs and differentially expressed module‐genes (DEMGs) regarding long COVID and COVID‐19 was intersected by following Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA). Results There were 11 and 62 differentially expressed modules, 1837 and 179 DEGs, as well as 103 and 508 DEMGs acquiring identified for both COVID‐19 and long COVID, notably enriched in the immune‐correlated signaling pathways. The immune infiltrating cells of long COVID and COVID‐19 were comparatively and respectively assessed via CIBERSORT, ssGSEA, and xCell algorithms. Subsequently, the screening of hub genes involved employing the SVM‐RFE, RF, XGBoost algorithms, and logistic regression analysis. Among the 67 candidate genes were processed with machine learning algorithms and logistic regression, a subgroup consisting of CEP55, CDCA2, MELK, and DEPDC1B, was at last identified as potential biomarkers for predicting the risk of the progression into long COVID after COVID‐19 infections. The predicting performance of the potential biomarkers was quantified with a ROC value of 0.8762542, which proved the combination of potential biomarkers provided the highest performance. Conclusions In summary, we identified a subgroup of potential biomarkers for predicting the risk of the progression into long COVID after COVID‐19 infection, which could be partly elucidation of the associated molecular mechanisms for long COVID.