Gel-Based Triboelectric Nanogenerators for Flexible Sensing: Principles, Properties, and Applications.

The rapid development of the Internet of Things and artificial intelligence technologies has increased the need for wearable, portable, and self-powered flexible sensing devices. Triboelectric nanogenerators (TENGs) based on gel materials (with excellent conductivity, mechanical tunability, environmental adaptability, and biocompatibility) are considered an advanced approach for developing a new generation of flexible sensors. This review comprehensively summarizes the recent advances in gel-based TENGs for flexible sensors, covering their principles, properties, and applications. Based on the development requirements for flexible sensors, the working mechanism of gel-based TENGs and the characteristic advantages of gels are introduced. Design strategies for the performance optimization of hydrogel-, organogel-, and aerogel-based TENGs are systematically summarized. In addition, the applications of gel-based TENGs in human motion sensing, tactile sensing, health monitoring, environmental monitoring, human-machine interaction, and other related fields are summarized. Finally, the challenges of gel-based TENGs for flexible sensing are discussed, and feasible strategies are proposed to guide future research.

Lightweight and Strong Cellulosic Triboelectric Materials Enabled by Cell Wall Nanoengineering.

As intelligent technology surges forward, wearable electronics have emerged as versatile tools for monitoring health and sensing our surroundings. Among these advancements, porous triboelectric materials have garnered significant attention for their lightness. However, these materials face the challenge of improving structural stability to further enhance the sensing accuracy of triboelectric sensors. In this study, a lightweight and strong porous cellulosic triboelectric material is designed by cell wall nanoengineering. By tailoring of the cell wall structure, the material shows a high mechanical strength of 51.8 MPa. The self-powered sensor constructed by this material has a high sensitivity of 33.61 kPa-1, a fast response time of 36 ms, and excellent pressure detection durability. Notably, the sensor still enables a high sensing performance after the porous cellulosic triboelectric material exposure to 200 °C and achieves real-time feedback of human motion, thereby demonstrating great potential in the field of wearable electronic devices.

Dynamic Thermostable Cellulosic Triboelectric Materials from Multilevel-Non-Covalent Interactions.

Triboelectric materials present great potential for harvesting huge amounts of dispersed energy, and converting them directly into useful electricity, a process that generates power more sustainably. Triboelectric nanogenerators (TENGs) have emerged as a technology to power electronics and sensors, and it is expected to solve the problem of energy harvesting and self-powered sensing from extreme environments. In this paper, a high-temperature-resistant triboelectric material is designed based on multilevel non-covalent bonding interactions, which achieves an ultra-high surface charge density of 192 µC m-2 at high temperatures. TENGs based on the triboelectric material exhibit more than an order of magnitude higher power output (2750 mW m-2 at 200 °C) than the existing devices at high temperatures. These remarkable properties are achieved based on enthalpy-driven molecular assembly in highly unbonded states. Thus, the material maintains bond strength and ultra-high surface charge density in entropy-dominated high-temperature environments. This molecular design concept points out a promising direction for the preparation of polymers with excellent triboelectric properties.

Fabrication of Advanced Cellulosic Triboelectric Materials via Dielectric Modulation.

The rapid rise of triboelectric nanogenerators (TENGs), which are emerging energy conversion devices in advanced electronics and wearable sensing systems, has elevated the interest in high-performance and multifunctional triboelectric materials. Among them, cellulosic materials, affording high efficiency, biodegradability, and customizability, are becoming a new front-runner. The inherently low dielectric constant limits the increase in the surface charge density. However, owing to its unique structure and excellent processability, cellulose shows great potential for dielectric modulation, providing a strong impetus for its advanced applications in the era of Internet of Things and artificial intelligence. This review aims to provide comprehensive insights into the fabrication of dielectric-enhanced cellulosic triboelectric materials via dielectric modulation. The exceptional advantages and research progress in cellulosic materials are highlighted. The effects of the dielectric constant, polarization, and percolation threshold on the charge density are systematically investigated, providing a theoretical basis for cellulose dielectric modulation. Typical dielectric characterization methods are introduced, and their technical characteristics are analyzed. Furthermore, the performance enhancements of cellulosic triboelectric materials endowed by dielectric modulation, including more efficient energy harvesting, high-performance wearable electronics, and impedance matching via material strategies, are introduced. Finally, the challenges and future opportunities for cellulose dielectric modulation are summarized.

Recoverable Dual-Modal Responsive Sensing Materials Based on Mechanoluminescence and Thermally Stimulated Luminescence toward Noncontact Tactile Sensors.

Tactile sensing with stress and temperature sensing as core elements have shown promising prospects in intelligent robots and the human-machine interface. Mechanoluminescence (ML)-based stress sensing can realize the direct sensing of mechanical stimulation, whereas indirect temperature sensing based on luminescent sensing materials usually requires the stimulation of extra light or force. Herein, a trap-controlled material Sr2MgAl22O36:Mn2+ with bifunctional mechano/thermal sensing applications was developed and investigated in detail. Visualized bright green-emitting ML and thermally stimulated luminescence (TSL) directly and rapidly responded to mechano/thermal dual stimulation in the Sr2MgAl22O36:Mn2+/PDMS composite film. It is worth mentioning that this study proposed a new idea of direct temperature sensing by the initial intensity of TSL due to thermal-photo energy conversion, unlike previous temperature sensor technology. Based on this, we designed a flexible optical skin with a simple structure and verified its application prospect as a tactile sensing material with dual mechano/thermal response, establishing a unique imaging mode and providing a convenient, reliable, and sensitive way to remotely visualize the distribution of stress and temperature. This study paves a new way for the development of optical skins with simple structures and sensitive visibility in the application of intelligent robot tactile sensing.

DrSim: Similarity Learning for Transcriptional Phenotypic Drug Discovery.

Transcriptional phenotypic drug discovery has achieved great success, and various compound perturbation-based data resources, such as connectivity map (CMap) and library of integrated network-based cellular signatures (LINCS), have been presented. Computational strategies fully mining these resources for phenotypic drug discovery have been proposed. Among them, the fundamental issue is to define the proper similarity between transcriptional profiles. Traditionally, such similarity has been defined in an unsupervised way. However, due to the high dimensionality and the existence of high noise in high-throughput data, similarity defined in the traditional way lacks robustness and has limited performance. To this end, we present DrSim, which is a learning-based framework that automatically infers similarity rather than defining it. We evaluated DrSim on publicly available in vitro and in vivo datasets in drug annotation and repositioning. The results indicated that DrSim outperforms the existing methods. In conclusion, by learning transcriptional similarity, DrSim facilitates the broad utility of high-throughput transcriptional perturbation data for phenotypic drug discovery. The source code and manual of DrSim are available at https://github.com/bm2-lab/DrSim/.

Design and tuning Cr3+-doped near-infrared phosphors for multifunctional applications via crystal field engineering.

Nowadays, near-infrared (NIR)-emitting luminescence materials with broad application prospects have drawn great attention. SrGa12O19:Cr3+ is a new type of solid light source material that emits NIR light with wide application prospects. However, the narrow full width at half maximum (FWHM) restricts its further multifunctional applications. Therefore, we propose a novel methodology to increase the FWHM by artificially adjusting the strength of the crystal field by doping Sc3+ ions. By employing Rietveld refinement results, parameters evolution and Raman spectra, Sc3+ ions are proved to successfully occupy the Ga3+ crystallographic sites. Combining the spectroscopy characteristics, it was confirmed that the FWHM was increased from 78 nm (127 977 cm-1) to 104 nm (96 739 cm-1) with the optimum quantum efficiency of 96.55%. In addition, the excellent thermal stability and unprecedented suitability to a 450 nm blue-chip indicate that it is a potential luminescent material candidate for fluorescence conversion NIR light-emitting diode (LED). Finally, the successful implementation of the night vision on vegetation, biological imaging of human tissue and food inspection for different pork portions illustrate the strong commonality of the method.

A temperature-dependent phosphorus doping on Ti3C2Tx MXene for enhanced supercapacitance.

A novel type of phosphorus doped Ti3C2Tx MXene nanosheets (P-Ti3C2Tx) is synthesized via a facile and controllable strategy of annealing MXene nanosheets with the presence of sodium hypophosphite. A combination of theoretical density functional theory calculation and experimental X-ray photoelectron spectroscopy discloses that the doped P atoms are prone to fill into Ti vacancies first due to their lowest formation free energy (ΔGP* = -0.028 eV·Å-2) and next to bond with surface terminals on MXene layers (ΔGP* = 0.013 eV·Å-2), forming P-C and P-O species, respectively. More importantly, the as-obtained P-Ti3C2Tx is, for the first time, investigated as the electrode material for supercapacitors, demonstrating a significantly boosted electrochemical performance by P doping. As a result, P-Ti3C2Tx electrode delivers a high specific capacitance of 320 F·g-1 at a current density of 0.5 A·g-1 (much higher than 131 F·g-1 for undoped MXene), an ultrahigh rate retention of 83.8% capacitance at 30 A·g-1, and a high cycling stability over continuous 5000 cycles.

The tumor therapy landscape of synthetic lethality.

Synthetic lethality is emerging as an important cancer therapeutic paradigm, while the comprehensive selective treatment opportunities for various tumors have not yet been explored. We develop the Synthetic Lethality Knowledge Graph (SLKG), presenting the tumor therapy landscape of synthetic lethality (SL) and synthetic dosage lethality (SDL). SLKG integrates the large-scale entity of different tumors, drugs and drug targets by exploring a comprehensive set of SL and SDL pairs. The overall therapy landscape is prioritized to identify the best repurposable drug candidates and drug combinations with literature supports, in vitro pharmacologic evidence or clinical trial records. Finally, cladribine, an FDA-approved multiple sclerosis treatment drug, is selected and identified as a repurposable drug for treating melanoma with CDKN2A mutation by in vitro validation, serving as a demonstrating SLKG utility example for novel tumor therapy discovery. Collectively, SLKG forms the computational basis to uncover cancer-specific susceptibilities and therapy strategies based on the principle of synthetic lethality.

Deep-red photoluminescence from enhanced 5D0-7F4 transition in Eu3+ doped Ca2Ga2GeO7 phosphors.

In order to explore diverse luminescence properties of Eu3+ in inorganic phosphors, a series of novel deep-red-emitting Ca2Ga2GeO7 phosphors were successfully synthesized by solid-state reactions. The phase formation was verified by the X-ray diffraction patterns and Rietveld refinement and there is only one kind of Ca2+ site in Ca2Ga2GeO7. The excitation spectrum shows typical excitation peaks of Eu3+ and the broad band ranging from 200 nm to 300 nm was composed of the charge transfer band of O2--Eu3+ and Ga3+-O2- transition band. The emission spectrum depicts that the intensity of the peak located at 704 nm (5D0 â†’ 7F4) is higher than that of 618 nm (5D0 â†’ 7F2), which finally leads to the deep-red emission of the phosphor with high color saturation. The coordination dodecahedron of EuO8 distorted from a cubic geometry to the square antiprism is responsible for the unusual emission of Eu3+. The research of the concentration quenching behavior, lifetime and luminescence decay curves imply that the energy transfer between Eu3+ ions finally leads to the concentration quenching, and the interaction type is d-d interaction. The charge compensation, quantum efficiency and the thermal stability of Ca2Ga2GeO7:Eu3+ were also studied.

iDMer: an integrative and mechanism-driven response system for identifying compound interventions for sudden virus outbreak.

Emerging viral infections seriously threaten human health globally. Several challenges exist in identifying effective compounds against viral infections: (1) at the initial stage of a new virus outbreak, little information, except for its genome information, may be available; (2) although the identified compounds may be effective, they may be toxic in vivo and (3) cytokine release syndrome (CRS) triggered by viral infections is the primary cause of mortality. Currently, an integrative tool that takes all those aspects into consideration for identifying effective compounds to prevent viral infections is absent. In this study, we developed iDMer, as an integrative and mechanism-driven response system for addressing these challenges during the sudden virus outbreaks. iDMer comprises three mechanism-driven compound identification modules, that is, a virus-host interaction-oriented module, an autophagy-oriented module and a CRS-oriented module. As a one-stop integrative platform, iDMer incorporates compound toxicity evaluation and compound combination identification for virus treatment with clear mechanisms. iDMer was successfully tested on five viruses, including the current severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Our results indicated that, for all five tested viruses, compounds that were reported in the literature or experimentally validated for virus treatment were enriched at the top, demonstrating the generalized effectiveness of iDMer. Finally, we demonstrated that combinations of the individual modules successfully identified combinations of compounds effective for virus intervention with clear mechanisms.

Systematically Characterizing A-to-I RNA Editing Neoantigens in Cancer.

A-to-I RNA editing can contribute to the transcriptomic and proteomic diversity of many diseases including cancer. It has been reported that peptides generated from RNA editing could be naturally presented by human leukocyte antigen (HLA) molecules and elicit CD8+ T cell activation. However, a systematical characterization of A-to-I RNA editing neoantigens in cancer is still lacking. Here, an integrated RNA-editing based neoantigen identification pipeline PREP (Prioritizing of RNA Editing-based Peptides) was presented. A comprehensive RNA editing neoantigen profile analysis on 12 cancer types from The Cancer Genome Atlas (TCGA) cohorts was performed. PREP was also applied to 14 ovarian tumor samples and two clinical melanoma cohorts treated with immunotherapy. We finally proposed an RNA editing neoantigen immunogenicity score scheme, i.e. REscore, which takes RNA editing level and infiltrating immune cell population into consideration. We reported variant peptide from protein IFI30 in breast cancer which was confirmed expressed and presented in two samples with mass spectrometry data support. We showed that RNA editing neoantigen could be identified from RNA-seq data and could be validated with mass spectrometry data in ovarian tumor samples. Furthermore, we characterized the RNA editing neoantigen profile of clinical melanoma cohorts treated with immunotherapy. Finally, REscore showed significant associations with improved overall survival in melanoma cohorts treated with immunotherapy. These findings provided novel insights of cancer biomarker and enhance our understanding of neoantigen derived from A-to-I RNA editing as well as more types of candidates for personalized cancer vaccines design in the context of cancer immunotherapy.

ASNEO: Identification of personalized alternative splicing based neoantigens with RNA-seq.

Cancer neoantigens have shown great potential in immunotherapy, while current software focuses on identifying neoantigens which are derived from SNVs, indels or gene fusions. Alternative splicing widely occurs in tumor samples and it has been proven to contribute to the generation of candidate neoantigens. Here we present ASNEO, which is an integrated computational pipeline for the identification of personalized Alternative Splicing based NEOantigens with RNA-seq. Our analyses showed that ASNEO could identify neopeptides which are presented by MHC I complex through mass spectrometry data validation. When ASNEO was applied to two immunotherapy-treated cohorts, we found that alternative splicing based neopeptides generally have a higher immune score than that of somatic neopeptides and alternative splicing based neopeptides could be a marker to predict patient survival pattern. Our identification of alternative splicing derived neopeptides would contribute to a more complete understanding of the tumor immune landscape. Prediction of patient-specific alternative splicing neopeptides has the potential to contribute to the development of personalized cancer vaccines.

Highly Monodisperse Cu-Sn Alloy Nanoplates for Efficient Nitrophenol Reduction Reaction via Promotion Effect of Tin.

The hexagonal copper-tin alloy (Cu-Sn) nanoplates were synthesized using a high temperature solvent method, the length of six equilateral edges of hexagonal Cu-Sn nanoplates was 23 nm, and the thickness was 13 nm. The obtained hexagonal Cu-Sn nanoplates were highly monodisperse and allowed the formation of nanoarrays arranged with long-range order. The hexagonal Cu-Sn nanoplates exhibited high catalytic activity on catalytic hydrogenation of 4-nitrophenol to 4-aminophenol. Due to the promotion effect of Sn, the apparent rate constant (ka) of hexagonal Cu-Sn nanoplates was three times that of Cu nanoparticles. The density functional theory (DFT) calculations and experimental results demonstrated that Sn could promote the coordination process of -NO2 of 4-nitrophenol with Cu-Sn nanoplates and contribute to activation of 4-nitrophenol. In addition, the hexagonal Cu-Sn nanoplates showed high stability and reusability for the reduction reaction, good adaptability in different pH and the ionic strength, and wide applicability for the degradation of methylene blue, methyl orange, and rhodamine B, even in the industrial wastewater, suggesting that the Cu-Sn nanoplates are promising catalysts in organic industry wastewater treatment.

Essential oil extracted from leaf of Phoebe bournei (Hemsl.) yang: chemical constituents, antitumor, antibacterial, hypoglycemic activities.

The essential oil were extracted from the leaf of Phoebe bournei (Hemsl.) Yang by a hydrothermal method and then analyzed by gas chromatography-mass spectrometry. The leaf oil mainly included α-copaene (5.44%), α-muurolene (7.32%), δ-cadinene (11.44%), 1s-calamenene (5.18%). Phoebe bournei (Hemsl.) Yang leaf essential oil had significant inhibitory activity against Epidermophyton floccosum and Microsporum gypseum, the potential antitumor activity towards leukemia, breast, and colon cancer cell lines was good. Phoebe bournei (Hemsl.) Yang leaf essential oil had weaker activity on the four tested bacteria, it exhibited a certain role in promoting glucose uptake by adipocytes.

pTuneos: prioritizing tumor neoantigens from next-generation sequencing data.

BACKGROUND: Cancer neoantigens are expressed only in cancer cells and presented on the tumor cell surface in complex with major histocompatibility complex (MHC) class I proteins for recognition by cytotoxic T cells. Accurate and rapid identification of neoantigens play a pivotal role in cancer immunotherapy. Although several in silico tools for neoantigen prediction have been presented, limitations of these tools exist. RESULTS: We developed pTuneos, a computational pipeline for prioritizing tumor neoantigens from next-generation sequencing data. We tested the performance of pTuneos on the melanoma cancer vaccine cohort data and tumor-infiltrating lymphocyte (TIL)-recognized neopeptide data. pTuneos is able to predict the MHC presentation and T cell recognition ability of the candidate neoantigens, and the actual immunogenicity of single-nucleotide variant (SNV)-based neopeptides considering their natural processing and presentation, surpassing the existing tools with a comprehensive and quantitative benchmark of their neoantigen prioritization performance and running time. pTuneos was further tested on The Cancer Genome Atlas (TCGA) cohort data as well as the melanoma and non-small cell lung cancer (NSCLC) cohort data undergoing checkpoint blockade immunotherapy. The overall neoantigen immunogenicity score proposed by pTuneos is demonstrated to be a powerful and pan-cancer marker for survival prediction compared to traditional well-established biomarkers. CONCLUSIONS: In summary, pTuneos provides the state-of-the-art one-stop and user-friendly solution for prioritizing SNV-based candidate neoepitopes, which could help to advance research on next-generation cancer immunotherapies and personalized cancer vaccines. pTuneos is available at https://github.com/bm2-lab/pTuneos , with a Docker version for quick deployment at https://cloud.docker.com/u/bm2lab/repository/docker/bm2lab/ptuneos .

The Landscape of Tumor Fusion Neoantigens: A Pan-Cancer Analysis.

Compared with SNV&indel-based neoantigens, fusion-based neoantigens are not well characterized. In the present study, we performed a comprehensive analysis of the landscape of tumor fusion neoantigens in cancer and proposed a score scheme to quantitatively assess their immunogenic potentials. By analyzing three large-scale tumor datasets, we demonstrated that (1) the tumor fusion candidate neoantigen burden is not related to the immunotherapy outcome; (2) fusion neoantigens tend to have notably higher immunogenic potentials than SNV&indel-based candidate neoantigens, making them better candidates for cancer vaccines; (3) fusion candidate neoantigens distribute sparsely between individual patients. Although several recurrent candidate neoantigens exist, they usually have extremely low immunogenic potentials, suggesting that vaccination-based cancer immunotherapy must be personalized; (4) compared with fusion mutations involving tumor passenger genes, fusion mutations involving oncogenic genes have remarkably low immunogenic potentials, indicating that they undergo selection pressure during tumorigenesis.

Core-Shell ZnO@SnO2 Nanoparticles for Efficient Inorganic Perovskite Solar Cells.

The ideal charge transport materials should exhibit a proper energy level, high carrier mobility, sufficient conductivity, and excellent charge extraction ability. Here, a novel electron transport material was designed and synthesized by using a simple and facile solvothermal method, which is composed of the core-shell ZnO@SnO2 nanoparticles. Thanks to the good match between the energy level of the SnO2 shell and the high electron mobility of the core ZnO nanoparticles, the PCE of inorganic perovskite solar cells has reached 14.35% (JSC: 16.45 mA cm-2, VOC: 1.11 V, FF: 79%), acting core-shell ZnO@SnO2 nanoparticles as the electron transfer layer. The core-shell ZnO@SnO2 nanoparticles size is 8.1 nm with the SnO2 shell thickness of 3.4 nm, and the electron mobility is seven times more than SnO2 nanoparticles. Meanwhile, the uniform core-shell ZnO@SnO2 nanoparticles is extremely favorable to the growth of inorganic perovskite films. These preliminary results strongly suggest the great potential of this novel electron transfer material in high-efficiency perovskite solar cells.

MOF-Confined Sub-2 nm Stable CsPbX3 Perovskite Quantum Dots.

The metal halide with a perovskite structure has attracted significant attention due to its defect-tolerant photophysics and optoelectronic features. In particular, the all-inorganic metal halide perovskite quantum dots have potential for development in future applications. Sub-2 nm CsPbX3 (X = Cl, Br, and I) perovskite quantum dots were successfully fabricated by a MOF-confined strategy with a facile and simple route. The highly uniform microporous structure of MOF effectively restricted the CsPbX3 quantum dots aggregation in a synthetic process and endowed the obtained sub-2 nm CsPbX3 quantum dots with well-dispersed and excellent stability in ambient air without a capping agent. The photoluminescence emission spectra and lifetimes were not decayed after 60 days. The CsPbX3 quantum dots maintained size distribution stability in the air without any treatment. Because of the quantum confinement effect of CsPbX3 quantum dots, the absorption and photoluminescence (PL) emission peak were blue shifted to shorter wavelengths compare with bulk materials. Furthermore, this synthetic strategy provides a novel method in fabricating ultra-small photoluminescence quantum dots.

MXene Boosted CoNi-ZIF-67 as Highly Efficient Electrocatalysts for Oxygen Evolution.

Oxygen evolution reaction (OER) is a pivotal step for many sustainable energy technologies, and exploring inexpensive and highly efficient electrocatalysts is one of the most crucial but challenging issues to overcome the sluggish kinetics and high overpotentials during OER. Among the numerous electrocatalysts, metal-organic frameworks (MOFs) have emerged as promising due to their high specific surface area, tunable porosity, and diversity of metal centers and functional groups. It is believed that combining MOFs with conductive nanostructures could significantly improve their catalytic activities. In this study, an MXene supported CoNi-ZIF-67 hybrid (CoNi-ZIF-67@Ti3C2Tx) was synthesized through the in-situ growth of bimetallic CoNi-ZIF-67 rhombic dodecahedrons on the Ti3C2Tx matrix via a coprecipitation reaction. It is revealed that the inclusion of the MXene matrix not only produces smaller CoNi-ZIF-67 particles, but also increases the average oxidation of Co/Ni elements, endowing the CoNi-ZIF-67@Ti3C2Tx as an excellent OER electrocatalyst. The effective synergy of the electrochemically active CoNi-ZIF-67 phase and highly conductive MXene support prompts the hybrid to process a superior OER catalytic activity with a low onset potential (275 mV vs. a reversible hydrogen electrode, RHE) and Tafel slope (65.1 mV∙dec-1), much better than the IrO2 catalysts and the pure CoNi-ZIF-67. This work may pave a new way for developing efficient non-precious metal catalyst materials.