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Recovering a sequence of latent sharp frames from a motion-blurred image is a challenging task. The bio-inspired event camera, which produces an event stream with high temporal resolution, has been exploited to promote the recovery performance. However, recovering sharp sequences with arbitrary temporal scales has been ignored for a long time. Existing works can only recover a fixed number of latent frames from a blurry image once they are trained. In this work, we propose an event-assisted blurry image unfolding framework that can work across arbitrary temporal scales. A bi-directional recurrent network is employed to encode events corresponding to each latent frame, which gathers information over all events in the exposure time. Features of both the blurry image and events are fused together and fed to a bi-directional latent sequence decoder (BiLSD) to produce a sequence of latent sharp frames. Extensive experiments show that the proposed method not only performs favorably against state-of-the-art methods in recovering a fixed number of frames from a blurry image but can be well generalized to arbitrary-temporal-scale blurry image unfolding.

While Ru-catalyzed hydrogenolysis holds significant promise in converting waste polyolefins into value-added alkane fuels, a major constraint is the high cost of noble metal catalysts. In this work, we propose, for the first time, that Co-based catalysts derived from CoAl-layered double hydroxide (LDH) are alternatives for efficient polyolefin hydrogenolysis. Leveraging the chemical flexibility of the LDH platform, we reveal that metallic Co species serve as highly efficient active sites for polyolefin hydrogenolysis. Furthermore, we introduced Ni into the Co framework to tackle the issue of restricted hydrogenation ability associated with contiguous Co-Co sites. In-situ analysis indicates that the integration of Ni induces electron transfer and facilitates hydrogen spillover. This dual effect synergistically enhances the hydrogenation/desorption of olefin intermediates, resulting in a significant reduction in the yield of low-value CH 4 from 27.1 to 12.6%. Through leveraging the unique properties of LDH, we have developed efficient and cost-effective catalysts for the sustainable recycling and valorization of waste polyolefin materials.

Pancreatic ductal adenocarcinoma (PDAC) is a common kind of lethal cancer, with low early diagnostic rate and poor prognosis. In this study, we identified and verified the AMIGO2 with significant diagnostic and prognostic value in PDAC through LASSO regression combined with multiple machines learning methods, including RVM-RFE and Random Forest in TCGA and GEO datasets. The relevance between the expression of AMIGO2 and M2 polarization of macrophages was identified through pancancer, normal tissue, and cell lines data in TCGA, GTEx and CCLE datasets. The relevance between AMIGO2 and M2 polarization was then further identified in our local PDAC cohort. Finally, the role of AMIGO2 as cancer promoter and pivotal factor enrolled in M2 polarization was verified through siRNA transfection and M2 macrophages induction. These findings could facilitate diagnosis and treatment of PDAC. In addition, further research was deemed necessary on the deep mechanism between AMIGO2 and M2 polarization of macrophages in PDAC.

The goal of blurry image deblurring and unfolding task is to recover a single sharp frame or a sequence from a blurry one. Recently, its performance is greatly improved with introduction of a bio-inspired visual sensor, event camera. Most existing event-assisted deblurring methods focus on the design of powerful network architectures and effective training strategy, while ignoring the role of blur modeling in removing various blur in dynamic scenes. In this work, we propose to implicitly model blur in an image by computing blurriness representation with an event-assisted blurriness encoder. The learning of blurriness representation is formulated as a ranking problem based on specially synthesized pairs. Blurriness-aware image unfolding is achieved by integrating blur relevant information contained in the representation into a base unfolding network. The integration is mainly realized by the proposed blurriness-guided modulation and multi-scale aggregation modules. Experiments on GOPRO and HQF datasets show favorable performance of the proposed method against state-of-the-art approaches. More results on real-world data validate its effectiveness in recovering a sequence of latent sharp frames from a blurry image.

Crohn's disease (CD) is a chronic inflammatory disease with increasing incidence worldwide and unclear etiology. Its clinical manifestations vary depending on location, extent, and severity of the lesions. In order to diagnose Crohn's disease, medical professionals need to comprehensively analyze patients' multimodal examination data, which includes medical imaging such as colonoscopy, pathological, and text information from clinical records. The processes of multimodal data analysis require collaboration among medical professionals from different departments, which wastes a lot of time and human resources. Therefore, a multimodal medical assisted diagnosis system for Crohn's disease is particularly significant. Existing network frameworks find it hard to effectively capture multimodal patient data for diagnosis, and multimodal data for Crohn's disease is currently lacking. In addition,a combination of data from patients with similar symptoms could serve as an effective reference for disease diagnosis. Thus, we propose a multimodal information diagnosis network (MICDnet) to learn CD feature representations by integrating colonoscopy, pathology images and clinical texts. Specifically, MICDnet first preprocesses each modality data, then uses encoders to extract image and text features separately. After that, multimodal feature fusion is performed. Finally, CD classification and diagnosis are conducted based on the fused features. Under the authorization, we build a dataset of 136 hospitalized inspectors, with colonoscopy images of seven areas, pathology images, and clinical record text for each individual. Training MICDnet on this dataset shows that multimodal diagnosis can improve the diagnostic accuracy of CD, and the diagnostic performance of MICDnet is superior to other models., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Confinement effects in highly porous nanostructures can effectively adjust the selectivity and kinetics of electrochemical reactions, which can boost the methanol oxidation reaction (MOR). In this work, carbonized ZIF-8-confined hollow PtCo nanospheres (PtCo@carbonized ZIF-8) were fabricated using a facile strategy. A monodisperse confined region was successfully prepared, and the dispersion of the PtCo nanoparticles (NPs) could be precisely regulated, allowing for the effective tuning of the confined region. Thus, the precise regulation of the catalytic reaction was achieved. Importantly, hollow PtCo NPs were prepared using a method based on the Kirkendall effect, and their forming mechanism was systematically investigated. Because of the confinement effects of carbonized zeolitic imidazolate framework-8 (ZIF-8), the crystal and electronic structures of the PtCo NPs were able to be effectively tuned. Our electrochemical results show that PtCo@carbonized ZIF-8 composites manifest a higher mass activity (1.4 A mg Pt -1 ) and better stability compared to commercial Pt/C.

Generation of representative conformations for small molecules is a fundamental task in cheminformatics and computer-aided drug discovery, but capturing the complex distribution of conformations that contains multiple low energy minima is still a great challenge. Deep generative modeling, aiming to learn complex data distributions, is a promising approach to tackle the conformation generation problem. Here, inspired by stochastic dynamics and recent advances in generative modeling, we developed SDEGen, a novel conformation generation model based on stochastic differential equations. Compared with existing conformation generation methods, it enjoys the following advantages: (1) high model capacity to capture multimodal conformation distribution, thereby searching for multiple low-energy conformations of a molecule quickly, (2) higher conformation generation efficiency, almost ten times faster than the state-of-the-art score-based model, ConfGF, and (3) a clear physical interpretation to learn how a molecule evolves in a stochastic dynamics system starting from noise and eventually relaxing to the conformation that falls in low energy minima. Extensive experiments demonstrate that SDEGen has surpassed existing methods in different tasks for conformation generation, interatomic distance distribution prediction, and thermodynamic property estimation, showing great potential for real-world applications., Competing Interests: There are no conflicts to declare., (This journal is © The Royal Society of Chemistry.)

This study explored the environmental determinants of different months on snail density measured in April at different types of snail habitats (marshlands, inner embankments, and hills) by considering spatial effects. Data were gathered from surveys on snails that were conducted in Hunan Province in April 2016, and information was collected on environmental variables. To investigate the environmental factors influencing snail density in various types of snail habitats, the ordinary least square model, spatial lag model, and spatial error model were all used. The environmental determinants for snail density showed different effects in the three types of snail habitats. In marshlands, snail density measured in April was associated positively with the normalized difference vegetation index (NDVI) and was associated negatively with flooding duration and annual hours of sunshine. Extreme temperatures correlated strongly to snail density measured in April (P < 0.05). In areas inside embankments, snail density measured in April increased with a decreased distance between snail habitat and the nearest river (P < 0.05). In hills, extreme heat, annual hours of sunshine, NDVI in September, and annual average land surface temperature (LST) were associated negatively with snail density measured in April, whereas index of moisture (IM) was associated positively with snail density measured in April (P < 0.05). The effects of LST and hours of sunshine on snail density measured in April varied with months of the year in the three different types of snail habitats (P < 0.05). Our study might provide a theoretical foundation for preventing snail transmission and subsequent spread of schistosomiasis.

Understanding the spatial distribution of schistosome infection is critical for tailoring preventive measures to control and eliminate schistosomiasis. This study used spatial analysis to determine risk factors that may impact Schistosoma japonicum infection and predict risk in Hunan and Jiangxi Provinces in the People's Republic of China. The study employed survey data collected in Hunan and Jiangxi in 2016. Independent variable data were obtained from publicly available sources. Bayesian-based geostatistics was used to build models with covariate fixed effects and spatial random effects to identify factors associated with the spatial distribution of infection. Prevalence of schistosomiasis was higher in Hunan (12.8%) than Jiangxi (2.6%). Spatial distribution of schistosomiasis varied at pixel level (0.1 × 0.1 km), and was significantly associated with distance to nearest waterbody (km, β = -1.158; 95% credible interval [CrI]: -2.104, -0.116) in Hunan and temperature (°C, β = -4.359; 95% CrI: -9.641, -0.055) in Jiangxi. The spatial distribution of schistosomiasis in Hunan and Jiangxi varied substantially and was significantly associated with distance to nearest waterbody. Prevalence of schistosomiasis decreased with increasing distance to nearest waterbody in Hunan, indicating that schistosomiasis control should target individuals in close proximity to open water sources as they are at highest risk of infection.

Background: Penehyclidine hydrochloride (PHC) has been used for many years as an anticholinergic drug for the treatment of acute organophosphorus pesticide poisoning (AOPP). The purpose of this meta-analysis was to explore whether PHC has advantages over atropine in the use of anticholinergic drugs in AOPP., Methods: We searched Scopus, Embase, Cochrane, PubMed, ProQuest, Ovid, Web of Science, China Science and Technology Journal Database (VIP), Duxiu, Chinese Biomedical literature (CBM), WanFang, and Chinese National Knowledge Infrastructure (CNKI), from inception to March 2022. After all qualified randomized controlled trials (RCTs) were included, we conducted quality evaluation, data extraction, and statistical analysis. Statistics using risk ratios (RR), weighted mean difference (WMD), and standard mean difference (SMD)., Results: Our meta-analysis included 20,797 subjects from 240 studies across 242 different hospitals in China. Compared with the atropine group, the PHC group showed decreased mortality rate (RR=0.20, 95% confidence intervals [ CI]: 0.16-0.25, P  <0.001), hospitalization time (WMD=-3.89, 95% CI: -4.37 to -3.41, P  <0.001), overall incidence rate of complications (RR=0.35, 95% CI: 0.28-0.43, P  <0.001), overall incidence of adverse reactions (RR=0.19, 95% CI: 0.17-0.22, P  <0.001), total symptom disappearance time (SMD=-2.13, 95% CI: -2.35 to -1.90, P  <0.001), time for cholinesterase activity to return to normal value 50-60% (SMD=-1.87, 95% CI: -2.03 to -1.70, P  <0.001), coma time (WMD=-5.57, 95% CI: -7.20 to -3.95, P  <0.001), and mechanical ventilation time (WMD=-2.16, 95% CI: -2.79 to -1.53, P  <0.001)., Conclusion: PHC has several advantages over atropine as an anticholinergic drug in AOPP., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 The Authors.)

Sepsis after trauma increases the risk of mortality rate for patients in intensive care unit (ICUs). Currently, it is difficult to predict outcomes in individual patients with sepsis due to the complexity of causative pathogens and the lack of specific treatment. This study aimed to identify metabolomic biomarkers in patients with multiple trauma and those with multiple trauma accompanied with sepsis. Therefore, the metabolic profiles of healthy persons designated as normal controls (NC), multiple trauma patients (MT), and multiple trauma complicated with sepsis (MTS) (30 cases in each group) were analyzed with ultra-high performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF/MS)-based untargeted plasma metabolomics using collected plasma samples. The differential metabolites were enriched in amino acid metabolism, lipid metabolism, glycometabolism and nucleotide metabolism. Then, nine potential biomarkers, namely, acrylic acid, 5-amino-3-oxohexanoate, 3b-hydroxy-5-cholenoic acid, cytidine, succinic acid semialdehyde, PE [P-18:1(9Z)/16:1(9Z)], sphinganine, uracil, and uridine, were found to be correlated with clinical variables and validated using receiver operating characteristic (ROC) curves. Finally, the three potential biomarkers succinic acid semialdehyde, uracil and uridine were validated and can be applied in the clinical diagnosis of multiple traumas complicated with sepsis., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Feng, Dai, Liu, Li, Gou, Chen, Chen and Fu.)

In this study, to reduce the formation of organic acid during 1,3-propanediol biosynthesis in Klebsiella pneumoniae, a method combining UV mutagenesis and high-throughput screening with pH color plates was employed to obtain K. pneumoniae mutants. When compared with the parent strain, the total organic acid formation by the mutant decreased, whereas 1,3-propanediol biosynthesis increased after 24 h anaerobic shake flask culture. Subsequently, genetic changes in the mutant were analyzed by whole-genome sequencing and verified by signal gene deletion. Mutation of the rpoS gene was confirmed to contribute to the regulation of organic acid synthesis in K. pneumoniae. Besides, rpoS deletion eliminated the formation of 2,3-butanediol, the main byproduct produced during 1,3-propanediol fermentation, indicating the role of rpoS in metabolic regulation in K. pneumoniae. Thus, a K. pneumoniae mutant was developed, which could produce lower organic acid during 1,3-propanediol fermentation due to an rpoS mutation in this study., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

The exponential increase in the number of genomes deposited in public databases can help us gain a more holistic understanding of the phylogeny and epidemiology of Klebsiella pneumoniae. However, inferring the evolutionary relationships of K. pneumoniae based on big genomic data is challenging for existing methods. In this study, core genes of K. pneumoniae were determined and analysed in terms of differences in GC content, mutation rate, size, and potential functions. We then developed a stable genes-based method for big data analysis and compared it with existing methods. Our new method achieved a higher resolution phylogenetic analysis of K. pneumoniae. Using this genes-based method, we explored global phylogenetic relationships based on a public database of nearly 953 genomes. The results provide useful information to facilitate the phylogenetic and epidemiological analysis of K. pneumoniae, and the findings are relevant for security applications., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)

Schistosomiasis has been subjected to extensive control efforts in the People's Republic of China (China) which aims to eliminate the disease by 2030. We describe baseline results of a longitudinal cohort study undertaken in the Dongting and Poyang lakes areas of central China designed to determine the prevalence of Schistosoma japonicum in humans, animals (goats and bovines) and Oncomelania snails utilizing molecular diagnostics procedures. Data from the Chinese National Schistosomiasis Control Programme (CNSCP) were compared with the molecular results obtained.Sixteen villages from Hunan and Jiangxi provinces were surveyed; animals were only found in Hunan. The prevalence of schistosomiasis in humans was 1.8% in Jiangxi and 8.0% in Hunan determined by real-time polymerase chain reaction (PCR), while 18.3% of animals were positive by digital droplet PCR. The CNSCP data indicated that all villages harboured S. japonicum-infected individuals, detected serologically by indirect haemagglutination assay (IHA), but very few, if any, of these were subsequently positive by Kato-Katz (KK).Based on the outcome of the IHA and KK results, the CNSCP incorporates targeted human praziquantel chemotherapy but this approach can miss some infections as evidenced by the results reported here. Sensitive molecular diagnostics can play a key role in the elimination of schistosomiasis in China and inform control measures allowing for a more systematic approach to treatment.

Short-term prognosis of advanced schistosomiasis has not been well studied. We aimed to construct prognostic models using machine learning algorithms and to identify the most important predictors by utilising routinely available data under the government medical assistance programme. An established database of advanced schistosomiasis in Hunan, China was utilised for analysis. A total of 9541 patients for the period from January 2008 to December 2018 were enrolled in this study. Candidate predictors were selected from demographics, clinical features, medical examinations and test results. We applied five machine learning algorithms to construct 1 year prognostic models: logistic regression (LR), decision tree (DT), random forest (RF), artificial neural network (ANN) and extreme gradient boosting (XGBoost). An area under the receiver operating characteristic curve (AUC) was used to evaluate the model performance. The important predictors of the optimal model for unfavourable prognosis within 1 year were identified and ranked. There were 1249 (13.1%) cases having unfavourable prognoses within 1 year of discharge. The mean age of all participants was 61.94 years, of whom 70.9% were male. In general, XGBoost showed the best predictive performance with the highest AUC (0.846; 95% confidence interval (CI): 0.821, 0.871), compared with LR (0.798; 95% CI: 0.770, 0.827), DT (0.766; 95% CI: 0.733, 0.800), RF (0.823; 95% CI: 0.796, 0.851), and ANN (0.806; 95% CI: 0.778, 0.835). Five most important predictors identified by XGBoost were ascitic fluid volume, haemoglobin (HB), total bilirubin (TB), albumin (ALB), and platelets (PT). We proposed XGBoost as the best algorithm for the evaluation of a 1 year prognosis of advanced schistosomiasis. It is considered to be a simple and useful tool for the short-term prediction of an unfavourable prognosis for advanced schistosomiasis in clinical settings., (Copyright © 2021 Australian Society for Parasitology. Published by Elsevier Ltd. All rights reserved.)

The electronic nose is the olfactory organ of the robot, which is composed of a large number of sensors to perceive the smell of objects through free diffusion. Traditionally, it is difficult to realize the active perception function, and it is difficult to meet the requirements of small size, low cost, and quick response that robots require. In order to address these issues, a novel electronic nose with active perception was designed and an ensemble learning method was proposed to distinguish the smell of different objects. An array of three MQ303 semiconductor gas sensors and an electrochemical sensor DART-2-Fe5 were used to construct the novel electronic nose, and the proposed ensemble learning method with four algorithms realized the active odor perception function. The experiment results verified that the accuracy of the active odor perception can reach more than 90%, even though it used 30% training data. The novel electronic nose with active perception based on the ensemble learning method can improve the efficiency and accuracy of odor data collection and olfactory perception.

Advanced schistosomiasis has become a major public health problem in areas with a heavy burden of schistosomiasis infection. Our objective was to determine the incidence and prevalence of advanced schistosomiasis and risk factors associated with case fatality of advanced schistosomiasis. Data were abstracted from hospitalization records of patients with advanced schistosomiasis from Hunan Province, China. The incidence and prevalence of advanced schistosomiasis were determined and the risk factors for death in advanced patients were assessed using logistic regression analysis. A total of 10,362 patients with advanced schistosomiasis were recruited into our study and 65% of them were categorized as the ascites type. There were 1249 deaths between 2005 and 2018 and the case fatality was 12.05%. The incidence of advanced schistosomiasis increased from 2002 to 2010, peaked in 2010 and then leveled off. The prevalence of advanced schistosomiasis increased from 2005 to 2014, and was stable afterwards. HBV was a risk factor for death in advanced patients (adjusted odds ratio (aOR=1.93, 95% confidence interval (CI: 1.55 to 2.41). Patients without splenectomy had a higher risk of death (aOR=1.29, 95%CI: 1.08 to 1.56). Upper gastrointestinal bleeding was positively associated with the risk of death (aOR=1.42, 95% CI: 1.15 to 1.76). Besides, abnormal ALT, ascites and anemia were also significantly associated with the risk of death in advanced patients. Advanced schistosomiasis was effectively controlled in recent years. Splenectomy could reduce the case fatality of advanced patients. HBV infection, abnormal ALT, upper gastrointestinal bleeding and anemia also predicted the risk of death for advanced patients., (Copyright © 2021. Published by Elsevier B.V.)

Background: Constant emerging sites infested with Oncomelania hupensis (O. hupensis) impede the goal realization of eliminating schistosomiasis. The study assessed the spatial and temporal distributions of new Oncomelania snail habitats in Hunan Province from 1949 to 2016., Methods: We used the data from annual snail surveys throughout Hunan Province for the period from 1949 to 2016. Global Moran's I, Anselin local Moran's I statistics (LISA) and a retrospective space-time permutation model were applied to determine the spatial and temporal distributions of emerging snail-infested sites., Results: There were newly discovered snail-infested sites almost every year in 1949-2016, except for the years of 1993, 2009 and 2012. The number of emerging sites varied significantly in the five time periods (1949-1954, 1955-1976, 1977-1986, 1986-2003 and 2004-2016) (H = 25.35, p < 0.05). The emerging sites lasted 37.52 years in marshlands, 30.04 years in hills and 24.63 at inner embankments on average, with the values of Global Moran's I being 0.52, 0.49 and 0.44, respectively. High-value spatial clusters (HH) were mainly concentrated along the Lishui River and in Xiangyin County. There were four marshland clusters, two hill clusters and three inner embankment clusters after 1976., Conclusions: Lower reaches of the Lishui River and the Dongting Lake estuary were the high-risk regions for new Oncomelania snail habitats with long durations. Snail surveillance should be strengthened at stubborn snail-infested sites at the inner embankments. Grazing prohibition in snail-infested grasslands should be a focus in marshlands. The management of bovines in Xiangyin County is of great importance.

Rational and purposeful designs of amorphous materials with desirable structures are difficult to implement due to the complex and unordered nature of such materials. In this work, a modelling algorithm was proposed for amorphous covalent triazine-based polymers to construct atomistic representative models that can reproduce the experimentally measured properties of experimental samples. The constructed models were examined through comparisons of simulated and experimental properties, such as surface area, pore volume, and structure factor, and further validated by the good consistency observed among these properties. To assess the predictive capability of the modelling algorithm, we used a new covalent triazine-based polymer and predicted its porosity by constructing a simulated model. The predicted results on the surface area and pore volume of the simulated model were quantitatively consistent with the experimental data derived from the experimentally synthesized sample. This consistency reveals the predictive capacity of the proposed modelling algorithm. The algorithm could be a promising approach to predict and develop advanced covalent triazine-based polymers for multiple applications.

The structures of amorphous materials are generally difficult to characterize and comprehend due to their unordered nature and indirect measurement techniques. However, molecular simulation has been considered as an alternative method that can provide molecular-level information supplementary to experimental techniques. In this work, a new approach for modelling the atomistic structures of amorphous covalent triazine-based polymers is proposed and employed on two experimentally synthesized covalent triazine-based polymers. To examine the proposed modelling approach, the properties of the established models, such as surface areas, pore volumes, structure factors and N 2 adsorption isotherms, were calculated and compared with the experimental data. Excellent consistencies were observed between the simulated models and experimental samples, consequently validating the proposed models and the modelling approach. Moreover, the proposed modelling approach can be applied to new covalent triazine-based polymers for predictive purposes and to provide design strategies for future synthesis works., Competing Interests: There are no conflicts to declare., (This journal is © The Royal Society of Chemistry.)

Harvesting biomechanical energy is an important route for providing electricity to sustainably drive wearable electronics, which currently still use batteries and therefore need to be charged or replaced/disposed frequently. Here we report an approach that can continuously power wearable electronics only by human motion, realized through a triboelectric nanogenerator (TENG) with optimized materials and structural design. Fabricated by elastomeric materials and a helix inner electrode sticking on a tube with the dielectric layer and outer electrode, the TENG has desirable features including flexibility, stretchability, isotropy, weavability, water-resistance and a high surface charge density of 250 μC m -2 . With only the energy extracted from walking or jogging by the TENG that is built in outsoles, wearable electronics such as an electronic watch and fitness tracker can be immediately and continuously powered.

The drastic expansion of consumer electronics (like personal computers, touch pads, smart phones, etc.) creates many human-machine interfaces and multiple types of interactions between human and electronics. Considering the high frequency of such operations in our daily life, an extraordinary amount of biomechanical energy from typing or pressing buttons is available. In this study, we have demonstrated a highly flexible triboelectric nanogenerator (TENG) solely made from elastomeric materials as a cover on a conventional keyboard to harvest biomechanical energy from typing. A dual-mode working mechanism is established with a high transferred charge density of ∼140 μC/m(2) due to both structural and material innovations. We have also carried out fundamental investigations of its performance dependence on various structural factors for optimizing the electric output in practice. The fully packaged keyboard-shaped TENG is further integrated with a horn-like polypyrrole-based supercapacitor as a self-powered system. Typing in normal speed for 1 h, ∼8 × 10(-4) J electricity could be stored, which is capable of driving an electronic thermometer/hydrometer. Our keyboard cover also performs outstanding long-term stability, water resistance, as well as insensitivity to surface conditions, and the last feature makes it useful to research the typing behaviors of different people.

A soft, stretchable, and fully enclosed self-charging power system is developed by seamlessly combining a stretchable triboelectric nanogenerator with stretchable supercapacitors, which can be subject to and harvest energy from almost all kinds of large-degree deformation due to its fully soft structure. The power system is washable and waterproof owing to its fully enclosed structure and hydrophobic property of its exterior surface. The power system can be worn on the human body to effectively scavenge energy from various kinds of human motion, and it is demonstrated that the wearable power source is able to drive an electronic watch. This work provides a feasible approach to design stretchable, wearable power sources and electronics.

Ocean wave associated energy is huge, but it has little use toward world energy. Although such blue energy is capable of meeting all of our energy needs, there is no effective way to harvest it due to its low frequency and irregular amplitude, which may restrict the application of traditional power generators. In this work, we report a hybrid nanogenerator that consists of a spiral-interdigitated-electrode triboelectric nanogenerator (S-TENG) and a wrap-around electromagnetic generator (W-EMG) for harvesting ocean energy. In this design, the S-TENG can be fully isolated from the external environment through packaging and indirectly driven by the noncontact attractive forces between pairs of magnets, and W-EMG can be easily hybridized. Notably, the hybrid nanogenerator could generate electricity under either rotation mode or fluctuation mode to collect energy in ocean tide, current, and wave energy due to the unique structural design. In addition, the characteristics and advantages of outputs indicate that the S-TENG is irreplaceable for harvesting low rotation speeds (<100 rpm) or motion frequencies (<2 Hz) energy, which fits the frequency range for most of the water wave based blue energy, while W-EMG is able to produce larger output at high frequencies (>10 Hz). The complementary output can be maximized and hybridized for harvesting energy in a broad frequency range. Finally, a single hybrid nanogenerator unit was demonstrated to harvest blue energy as a practical power source to drive several LEDs under different simulated water wave conditions. We also proposed a blue energy harvesting system floating on the ocean surface that could simultaneously harvest wind, solar, and wave energy. The proposed hybrid nanogenerator renders an effective and sustainable progress in practical applications of the hybrid nanogenerator toward harvesting water wave energy offered by nature.

The rapid growth of deformable and stretchable electronics calls for a deformable and stretchable power source. We report a scalable approach for energy harvesters and self-powered sensors that can be highly deformable and stretchable. With conductive liquid contained in a polymer cover, a shape-adaptive triboelectric nanogenerator (saTENG) unit can effectively harvest energy in various working modes. The saTENG can maintain its performance under a strain of as large as 300%. The saTENG is so flexible that it can be conformed to any three-dimensional and curvilinear surface. We demonstrate applications of the saTENG as a wearable power source and self-powered sensor to monitor biomechanical motion. A bracelet-like saTENG worn on the wrist can light up more than 80 light-emitting diodes. Owing to the highly scalable manufacturing process, the saTENG can be easily applied for large-area energy harvesting. In addition, the saTENG can be extended to extract energy from mechanical motion using flowing water as the electrode. This approach provides a new prospect for deformable and stretchable power sources, as well as self-powered sensors, and has potential applications in various areas such as robotics, biomechanics, physiology, kinesiology, and entertainment.

To sustainably power electronics by harvesting mechanical energy using nanogenerators, energy storage is essential to supply a regulated and stable electric output, which is traditionally realized by a direct connection between the two components through a rectifier. However, this may lead to low energy-storage efficiency. Here, we rationally design a charging cycle to maximize energy-storage efficiency by modulating the charge flow in the system, which is demonstrated on a triboelectric nanogenerator by adding a motion-triggered switch. Both theoretical and experimental comparisons show that the designed charging cycle can enhance the charging rate, improve the maximum energy-storage efficiency by up to 50% and promote the saturation voltage by at least a factor of two. This represents a progress to effectively store the energy harvested by nanogenerators with the aim to utilize ambient mechanical energy to drive portable/wearable/implantable electronics.

Kelvin probe force microscopy (KPFM), a characterization method that could image surface potentials of materials at the nanoscale, has extensive applications in characterizing the electric and electronic properties of metal, semiconductor, and insulator materials. However, it requires deep understanding of the physics of the measuring process and being able to rule out factors that may cause artifacts to obtain accurate results. In the most commonly used dual-pass KPFM, the probe works in tapping mode to obtain surface topography information in a first pass before lifting to a certain height to measure the surface potential. In this paper, we have demonstrated that the tapping-mode topography scan pass during the typical dual-pass KPFM measurement may trigger contact electrification between the probe and the sample, which leads to a charged sample surface and thus can introduce a significant error to the surface potential measurement. Contact electrification will happen when the probe enters into the repulsive force regime of a tip-sample interaction, and this can be detected by the phase shift of the probe vibration. In addition, the influences of scanning parameters, sample properties, and the probe's attributes have also been examined, in which lower free cantilever vibration amplitude, larger adhesion between the probe tip and the sample, and lower cantilever spring constant of the probe are less likely to trigger contact electrification. Finally, we have put forward a guideline to rationally decouple contact electrification from the surface potential measurement. They are decreasing the free amplitude, increasing the set-point amplitude, and using probes with a lower spring constant.

A flexible self-charging power system is built by integrating a fiber-based supercapacitor with a fiber-based triboelectric nanogenerator for harvesting mechanical energy from human motion. The fiber-based supercapacitor exhibits outstanding electrochemical properties, owing to the excellent pseudocapacitance of well-prepared RuO2 ·xH2 O by a vapor-phase hydrothermal method as the active material. The approach is a step forward toward self-powered wearable electronics., (© 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Although a triboelectric nanogenerator (TENG) has been developed to be an efficient approach to harvest mechanical energy, its robustness and lifetime are still to be improved through an effective and widely applicable way. Here, we show a rational designing methodology for achieving a significant improvement of the long-term stability of TENGs through automatic transition between contact and noncontact working states. This is realized by structurally creating two opposite forces in the moving part of the TENG, in which the pulling-away force is controlled by external mechanical motions. In this way, TENGs can work in the noncontact state with minimum surface wear and also transit into contact state intermittently to maintain high triboelectric charge density. A wind-driven disk-based TENG and a rotary barrel-based TENG that can realize automatic state transition under different wind speeds and rotation speeds, respectively, have been demonstrated as two examples, in which their robustness has been largely improved through this automatic transition. This methodology will further expand the practical application of TENGs for long-time usage and for harvesting mechanical energies with fluctuating intensities.

Panum's limiting case generally refers to the phenomenon that two features presented to one eye and a single feature presented to the other are combined and then perceived as two features at different depths. It is still not clear why experimental results derived from the Panum-type configuration (all lines parallel) support a double fusion viewpoint, but they do not for the Wheatstone-type configuration (one line not parallel to the others). Some experimental results support the double fusion theory, while others do not, even under a small disparity. Here we report that, under a small disparity, when the vertical gradients of the horizontal disparity ofdichoptic feature pairs in previous Wheatstone-type configurations were increased or decreased, the evidence which was considered to be very convincing in previous studies, either supporting or against the double fusion viewpoint, was challenged, and even turned to support the opposite view. Moreover, it was discovered that changes in the way features were arranged altered the results. Together, these results indicate that double fusion is the common basis for all kinds of Panum-type configurations. But for the Wheatstone-type configurations double fusion is also constrained by the vertical gradient of disparity of the configurations in addition to disparity and influenced by the degree of similarity/conflict between binocular cues and monocular cues resulting from different arrangements of features.