Performance of triboelectric nanogenerators is limited by low and unstable charge density on tribo-layers. An external-charge pumping method was recently developed and presents a promising and efficient strategy towards high-output triboelectric nanogenerators. However, integratibility and charge accumulation efficiency of the system is rather low. Inspired by the historical development of electromagnetic generators, here, we propose and realize a self-charge excitation triboelectric nanogenerator system towards high and stable output in analogy to the principle of traditional magnetic excitation generators. By rational design of the voltage-multiplying circuits, the completed external and self-charge excitation modes with stable and tailorable output over 1.25 mC m −2 in contact-separation mode have been realized in ambient condition. The realization of the charge excitation system in this work may provide a promising strategy for achieving high-output triboelectric nanogenerators towards practical applications.

Surface charge density is the key factor for developing high performance triboelectric nanogenerators (TENG). The previously invented charge excitation TENG provides a most efficient way to achieve maximum charge output of a TENG device. Herein, criteria to quantitatively evaluate the contact efficiency and air breakdown model on charge excitation TENG are established to enhance and evaluate charge density. The theoretical results are further verified by systematic experiments. A high average charge density up to 2.38 mC m −2 is achieved using the 4 μm PEI film and homemade carbon/silicone gel electrode in ambient atmosphere with 5% relative humidity. This work also reveals the actual charge density (over 4.0 mC m −2) in a TENG electrode based on quantified surface micro-contact efficiency and provides a prospective technical approach to improve the charge density, which could push the output performance of TENG to a new horizon.

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Reproducible quantification of large biological cohorts is critical for clinical/pharmaceutical proteomics yet remains challenging because most prevalent methods suffer from drastically declined commonly quantified proteins and substantially deteriorated quantitative quality as cohort size expands. MS2-based data-independent acquisition approaches represent tremendous advancements in reproducible protein measurement, but often with limited depth. We developed IonStar, an MS1-based quantitative approach enabling in-depth, high-quality quantification of large cohorts by combining efficient/reproducible experimental procedures with unique data-processing components, such as efficient 3D chromatographic alignment, sensitive and selective direct ion current extraction, and stringent postfeature generation quality control. Compared with several popular label-free methods, IonStar exhibited far lower missing data (0.1%), superior quantitative accuracy/precision [∼5% intragroup coefficient of variation (CV)], the widest protein abundance range, and the highest sensitivity/specificity for identifying protein changes (<5% false altered-protein discovery) in a benchmark sample set ( = 20). We demonstrated the usage of IonStar by a large-scale investigation of traumatic injuries and pharmacological treatments in rat brains ( = 100), quantifying >7,000 unique protein groups (>99.8% without missing data across the 100 samples) with a low false discovery rate (FDR), two or more unique peptides per protein, and high quantitative precision. IonStar represents a reliable and robust solution for precise and reproducible protein measurement in large cohorts.

Impacts on the probability of transition to entrepreneurship in rural China associated with the utilization of information communication technology (ICT) are estimated using longitudinal data from the China Family Panel Studies (CFPS) survey. We identify cell phone ownership and internet use as proxy variables for ICT utilization and find that cell phone ownership and internet use have positive impacts on entrepreneurship. After controlling for observables and time and regional fixed effects, cell phone users (internet users) are 2.0 (6.4) percentage points more likely to engage in entrepreneurship than the others. Considering that the average entrepreneurship rate for rural households is only 9.5% in the sample, the influence of cell phone ownership and internet use are very strong in the economic sense. Our results are robust to unobservable individual characteristics, model misspecification, and reverse causality of entrepreneurship to ICT utilization. Evidence also suggests that social network and information and knowledge acquisition play the mediating roles in the impact of ICT utilization on entrepreneurship.