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Triboelectric nanogenerators offer an environmentally friendly approach to harvesting energy from mechanical excitations. This capability has made them widely sought‐after as an efficient, renewable, and sustainable energy source, with the potential to decrease reliance on traditional fossil fuels. However, developing triboelectric nanogenerators with specific output remains a challenge mainly due to the uncertainties associated with their complex designs for real‐life applications. Artificial intelligence‐enabled inverse design is a powerful tool to realize performance‐oriented triboelectric nanogenerators. This is an emerging scientific direction that can address the concerns about the design and optimization of triboelectric nanogenerators leading to a next generation nanogenerator systems. This perspective paper aims at reviewing the principal analysis of triboelectricity, summarizing the current challenges of designing and optimizing triboelectric nanogenerators, and highlighting the physics‐informed inverse design strategies to develop triboelectric nanogenerators. Strategic inverse design is particularly discussed in the contexts of expanding the four‐mode analytical models by physics‐informed artificial intelligence, discovering new conductive and dielectric materials, and optimizing contact interfaces. Various potential development levels of artificial intelligence‐enhanced triboelectric nanogenerators are delineated. Finally, the potential of physics‐informed artificial intelligence inverse design to propel triboelectric nanogenerators from prototypes to multifunctional intelligent systems for real‐life applications is discussed.

 

The piezo-phototronic effect (a coupling effect of piezoelectric, photoexcitation and semiconducting properties, coined in 2010) has been demonstrated to be an ingenious and robust strategy to manipulate optoelectronic processes by tuning the energy band structure and photoinduced carrier behavior. The piezo-phototronic effect exhibits great potential in improving the quantum yield efficiencies of optoelectronic materials and devices and thus could help increase the energy conversion efficiency, thus alleviating the energy shortage crisis. In this review, the fundamental principles and challenges of representative optoelectronic materials and devices are presented, including photocatalysts (converting solar energy into chemical energy), solar cells (generating electricity directly under light illumination), photodetectors (converting light into electrical signals) and light-emitting diodes (LEDs, converting electric current into emitted light signals). Importantly, the mechanisms of how the piezo-phototronic effect controls the optoelectronic processes and the recent progress and applications in the above-mentioned materials and devices are highlighted and summarized. Only photocatalysts, solar cells, photodetectors, and LEDs that display piezo-phototronic behavior are reviewed. Material and structural design, property characterization, theoretical simulation calculations, and mechanism analysis are then examined as strategies to further enhance the quantum yield efficiency of optoelectronic devices via the piezo-phototronic effect. This comprehensive overview will guide future fundamental and applied studies that capitalize on the piezo-phototronic effect for energy conversion and storage. 

ABSTRACT Increasing data volumes on high-throughput sequencing instruments such as the NovaSeq 6000 leads to long computational bottlenecks for common metagenomics data preprocessing tasks such as adaptor and primer trimming and host removal. Here, we test whether faster recently developed computational tools (Fastp and Minimap2) can replace widely used choices (Atropos and Bowtie2), obtaining dramatic accelerations with additional sensitivity and minimal loss of specificity for these tasks. Furthermore, the taxonomic tables resulting from downstream processing provide biologically comparable results. However, we demonstrate that for taxonomic assignment, Bowtie2’s specificity is still required. We suggest that periodic reevaluation of pipeline components, together with improvements to standardized APIs to chain them together, will greatly enhance the efficiency of common bioinformatics tasks while also facilitating incorporation of further optimized steps running on GPUs, FPGAs, or other architectures. We also note that a detailed exploration of available algorithms and pipeline components is an important step that should be taken before optimization of less efficient algorithms on advanced or nonstandard hardware. IMPORTANCE In shotgun metagenomics studies that seek to relate changes in microbial DNA across samples, processing the data on a computer often takes longer than obtaining the data from the sequencing instrument. Recently developed software packages that perform individual steps in the pipeline of data processing in principle offer speed advantages, but in practice they may contain pitfalls that prevent their use, for example, they may make approximations that introduce unacceptable errors in the data. Here, we show that differences in choices of these components can speed up overall data processing by 5-fold or more on the same hardware while maintaining a high degree of correctness, greatly reducing the time taken to interpret results. This is an important step for using the data in clinical settings, where the time taken to obtain the results may be critical for guiding treatment. 

For this study, the effects of thermal annealing and compressive strain rate on the complexity of the serration behavior in a Zr-based bulk metallic glass (BMG) was investigated. Here, as-cast and thermally-annealed (300 °C, 1 week) Zr52.5Cu17.9Ni14.6Al10Ti5 BMG underwent room-temperature compression tests in the unconstrained condition at strain rates of 2 × 10−5 s−1 and 2 × 10−4 s−1. The complexity of the serrated flow was determined, using the refined composite multiscale entropy technique. Nanoindentation testing and X-ray diffraction characterization were performed to assess the changes in the microstructure and mechanical properties of the BMG that occurred during annealing. The results indicated that the BMG did not crystallize during annealing in the prescribed heating condition. Nanoindentation tests revealed that annealing led to a significant increase in the depth-dependent nanoindentation hardness and Young’s modulus, which were attributed to the structural relaxation in the glass. Furthermore, both annealing and an increased strain rate resulted in a marked enhancement in the complexity of the serrated flow during compression. It was concluded that the increase in the sample entropy with increasing strain rate is related to an increase in the number of defect interactions during the serrated flow. 

Abstract The human metabolome provides a window into the mechanisms and biomarkers of various diseases. However, because of limited availability, many sample types are still difficult to study by metabolomic analyses. Here, we present a mass spectrometry (MS)-based metabolomics strategy that only consumes sub-nanoliter sample volumes. The approach consists of combining a customized metabolomics workflow with a pulsed MS ion generation method, known as triboelectric nanogenerator inductive nanoelectrospray ionization (TENGi nanoESI) MS. Samples tested with this approach include exhaled breath condensate collected from cystic fibrosis patients as well as in vitro - cultured human mesenchymal stromal cells. Both test samples are only available in minimum amounts. Experiments show that picoliter-volume spray pulses suffice to generate high-quality spectral fingerprints, which increase the information density produced per unit sample volume. This TENGi nanoESI strategy has the potential to fill in the gap in metabolomics where liquid chromatography-MS-based analyses cannot be applied. Our method opens up avenues for future investigations into understanding metabolic changes caused by diseases or external stimuli.