Inner Mongolia University of Technology

Mimicking the comprehensive functions of human sensing via electronic skins (e-skins) is highly interesting for the development of human-machine interactions and artificial intelligences. Some e-skins with high sensitivity and stability were developed; however, little attention is paid to their comfortability, environmental friendliness, and antibacterial activity. Here, we report a breathable, biodegradable, and antibacterial e-skin based on all-nanofiber triboelectric nanogenerators, which is fabricated by sandwiching silver nanowire (Ag NW) between polylactic-co-glycolic acid (PLGA) and polyvinyl alcohol (PVA). With micro-to-nano hierarchical porous structure, the e-skin has high specific surface area for contact electrification and numerous capillary channels for thermal-moisture transfer. Through adjusting the concentration of Ag NW and the selection of PVA and PLGA, the antibacterial and biodegradable capability of e-skins can be tuned, respectively. Our e-skin can achieve real-time and self-powered monitoring of whole-body physiological signal and joint movement. This work provides a previously unexplored strategy for multifunctional e-skins with excellent practicability.

This paper investigates a critical access control issue in the Internet of Things (IoT). In particular, we propose a smart contract-based framework, which consists of multiple access control contracts (ACCs), one judge contract (JC), and one register contract (RC), to achieve distributed and trustworthy access control for IoT systems. Each ACC provides one access control method for a subject-object pair, and implements both static access right validation based on predefined policies and dynamic access right validation by checking the behavior of the subject. The JC implements a misbehavior-judging method to facilitate the dynamic validation of the ACCs by receiving misbehavior reports from the ACCs, judging the misbehavior and returning the corresponding penalty. The RC registers the information of the access control and misbehavior-judging methods as well as their smart contracts, and also provides functions (e.g., register, update, and delete) to manage these methods. To demonstrate the application of the framework, we provide a case study in an IoT system with one desktop computer, one laptop and two Raspberry Pi single-board computers, where the ACCs, JC, and RC are implemented based on the Ethereum smart contract platform to achieve the access control.

The term "zwitterionic polymers" refers to polymers that bear a pair of oppositely charged groups in their repeating units. When these oppositely charged groups are equally distributed at the molecular level, the molecules exhibit an overall neutral charge with a strong hydration effect via ionic solvation. The strong hydration effect constitutes the foundation of a series of exceptional properties of zwitterionic materials, including resistance to protein adsorption, lubrication at interfaces, promotion of protein stabilities, antifreezing in solutions, etc. As a result, zwitterionic materials have drawn great attention in biomedical and engineering applications in recent years. In this review, we give a comprehensive and panoramic overview of zwitterionic materials, covering the fundamentals of hydration and nonfouling behaviors, different types of zwitterionic surfaces and polymers, and their biomedical applications.

The permafrost organic carbon (OC) stock is of global significance because of its large pool size and the potential positive feedback to climate warming. However, due to the lack of systematic field observations and appropriate upscaling methodologies, substantial uncertainties exist in the permafrost OC budget, which limits our understanding of the fate of frozen carbon in a warming world. In particular, the lack of comprehensive estimates of OC stocks across alpine permafrost means that current knowledge on this issue remains incomplete. Here, we evaluated the pool size and spatial variations of permafrost OC stock to 3 m depth on the Tibetan Plateau by combining systematic measurements from a substantial number of pedons (i.e. 342 three-metre-deep cores and 177 50-cm-deep pits) with a machine learning technique (i.e. support vector machine, SVM). We also quantified uncertainties in permafrost carbon budget by conducting Monte Carlo simulations. Our results revealed that the combination of systematic measurements with the SVM model allowed spatially explicit estimates to be made. The OC density (OC amount per unit area, OCD) exhibited a decreasing trend from the south-eastern to the north-western plateau, with the exception that OCD in the swamp meadow was substantially higher than that in surrounding regions. Our results also demonstrated that Tibetan permafrost stored a large amount of OC in the top 3 m, with the median OC pool size being 15.31 Pg C (interquartile range: 13.03-17.77 Pg C). 44% of OC occurred in deep layers (i.e. 100-300 cm), close to the proportion observed across the northern circumpolar permafrost region. The large carbon pool size together with significant permafrost thawing suggests a risk of carbon emissions and positive climate feedback across the Tibetan alpine permafrost region.

Sodium‐ion battery (SIB) is especially attractive in cost‐effective energy storage device as an alternative to lithium‐ion battery. Particularly, metal phosphides as potential anodes for SIBs have recently been demonstrated owing to their higher specific capacities compared with those of carbonaceous materials. Unfortunately, most reported metal phosphides consist of irregular particles ranged from several hundreds nanometers to tens of micrometers, thus delivering limited cyclic stability. This paper reports the sodium storage properties of additive‐free Cu 3 P nanowire (CPNW) anode directly grown on copper current collector via an in situ growth followed by phosphidation method. Therefore, as a result of its structure features, CPNW anode demonstrates highly stable cycling ability with an ≈70% retention in capacity at the 260th cycle, whereas most reported metal phosphides have limited cycle numbers ranged between 30 and 150. Besides, the reaction mechanism between Cu 3 P and Na is investigated by examining the intermediate products at different charge/discharge stages using ex situ X‐ray diffraction measurements. Furthermore, to explore the practical application of CPNW anode, a pouch‐type Na + full cell consisting of CPNW anode and Na 3 V 2 (PO 4 ) 3 cathode is assembled and characterized. As a demonstration, a 10 cm × 10 cm light‐emmiting diode (LED) screen is successfully powered by the Na + full cell.

Nowadays, transition-metal oxides are regarded as the most potential materials for the supercapacitor and electrocatalyst. However, the poor electrical conductivity and insufficient active sites limited their development in various fields. Herein, we report the method of phosphorous-doped NiCo2O4 (named as P-NCO) prepared by the two-step strategy: the NiCo2O4 nanostructure is grown on the nickel foams by hydrothermal treatment and subsequently phosphatized in a tube furnace. Successfully, the rich oxygen vacancies and the P element introduced into the NiCo2O4 structure obviously improve the electrical conductivity, and the resulting P-NCO NWs/NF material shows an ultrahigh specific capacitance of 2747.8 F g–1 at 1 A g–1 and a prominent rate performance (maintain 50% at 100 A g–1). Furthermore, the assembled P-NCO NWs/NF//RGO asymmetric supercapacitor has an energy density of 28.2 W h kg–1 even at a high power density of 7750.35 W kg–1. After 10,000 cycles, the capacitance still also has an 88.48% retention rate. As an electrocatalyst, P-NCO NWs/NF has an excellent hydrogen evolution reaction (55 mV at 10 mA cm–2) and oxygen evolution reaction (300 mV at 10 mA cm–2) activities in 1 M KOH solution. This study provides an effective strategy to prepare multifunctional materials.

The preparation of porous materials from renewable energy sources is attracting intensive attention due to in terms of the application/economic advantage, and pore structural design is core in the development of efficient supercapacitors or available porous media. In this work, we focused on the transformation of natural biomass, such as cotton, into more stable porous carbonaceous forms for energy storage in practical applications. Biomorphic cotton fibers are pretreated under the effect of NaOH/urea swelling on cellulose and are subsequently used as a biomass carbon source to mold the porous microtubule structure through a certain degree of calcining. As a merit of its favorable structural features, the hierarchical porous carbon fibers exhibit an enhanced electric double layer capacitance (221.7 F g–1 at 0.3 A g–1) and excellent cycling stability (only 4.6% loss was observed after 6000 cycles at 2 A g–1). A detailed investigation displays that biomass-swelling behavior plays a significant role, not only in improving the surface chemical characteristics of biomorphic cotton fibers but also in facilitating the formation of a hierarchical porous carbon fiber structure. In contrast to traditional methods, nickel foams have been used as the collector for supercapacitor that requiring no additional polymeric binders or carbon black as support or conductive materials. Because of the absence of additive materials, we can further enhance capacitance. This remarkable capacitive performance can be due to sufficient void space within the porous microstructure. By effectively increasing the contact area between the carbon surface and the electrolyte, which can reduce the ion diffusion pathway or buffer the volume change during cycling. This approach opens a novel route to produce the abundantly different morphology of porous biomass-based carbon materials and proposes a green alternative method to meet sustainable development needs.

Monitoring various internal parameters plays a core role in ensuring the safety of lithium-ion batteries in power supply applications. It also influences the sustainability effect and online state of charge prediction. An improved multiple feature-electrochemical thermal coupling modeling method is proposed considering low-temperature performance degradation for the complete characteristic expression of multi-dimensional information. This is to obtain the parameter influence mechanism with a multi-variable coupling relationship. An optimized decoupled deviation strategy is constructed for accurate state of charge prediction with real-time correction of time-varying current and temperature effects. The innovative decoupling method is combined with the functional relationships of state of charge and open-circuit voltage to capture energy management effectively. Then, an adaptive equivalent-prediction model is constructed using the state-space equation and iterative feedback correction, making the proposed model adaptive to fractional calculation. The maximum state of charge estimation errors of the proposed method are 4.57% and 0.223% under the Beijing bus dynamic stress test and dynamic stress test conditions, respectively. The improved multiple feature-electrochemical thermal coupling modeling realizes the effective correction of the current and temperature variations with noise influencing coefficient, and provides an efficient state of charge prediction method adaptive to complex conditions.

Nitrogen-enriched electrospun carbon nanofiber networks were prepared to use as a free-standing LIB anode material with ultrahigh capacity and good rate capability.

Wind power can effectively alleviate the energy crisis. However, its integration into the grid affects power quality and power grid stability. Accurate wind speed prediction is a key factor in the efficient use of wind power. Because of its intermittent and nonstationary nature, wind speed forecasting is difficult, and is the topic of much research, especially long-time multistep forecasts. In this paper, the multistep wind speed prediction problem is regarded as a sequence-to-sequence mapping problem, and a multistep wind speed prediction model based on a transformer is proposed. This model is based on an encoder–decoder architecture, where the encoder generates representations of historical wind speed sequences of any length, the decoder generates arbitrarily long future wind speed sequences, and the encoder and decoder are associated by an attention mechanism. At the same time, the encoder and decoder of Transformer are completely based on a multi-head attention mechanism. For easy modeling, a 1-dimensional original wind speed sequence is transformed to a 16-dimensional sequence by ensemble empirical mode decomposition (EEMD), and the multidimensional wind speed data are directly modeled with Transformer. We trained the model with very large-scale (19 years of data) wind speed data averaged at 10-minute intervals, and performed the evaluation over one-year wind speed data. Results show that our one-step forecast model achieved an average mean absolute error (MAE) and root mean square error (RMSE) of 0.167 and 0.221, respectively. To the best of our knowledge, our 3-, 6-, 12-, and 24-hour multistep forecast model achieves a new state of the art in wind speed forecasting, with respective MAEs of 0.243, 0.290, 0.362, and 0.453, and RMSEs of 0.326, 0.401, 0.513, and 0.651. It is believed that performance can be further improved with better model parameter optimization.

Abstract Our knowledge of fundamental drivers of the temperature sensitivity ( Q 10 ) of soil carbon dioxide (CO 2 ) release is crucial for improving the predictability of soil carbon dynamics in Earth System Models. However, patterns and determinants of Q 10 over a broad geographic scale are not fully understood, especially in alpine ecosystems. Here we addressed this issue by incubating surface soils (0–10 cm) obtained from 156 sites across Tibetan alpine grasslands. Q 10 was estimated from the dynamics of the soil CO 2 release rate under varying temperatures of 5–25°C. Structure equation modeling was performed to evaluate the relative importance of substrate, environmental, and microbial properties in regulating the soil CO 2 release rate and Q 10 . Our results indicated that steppe soils had significantly lower CO 2 release rates but higher Q 10 than meadow soils. The combination of substrate properties and environmental variables could predict 52% of the variation in soil CO 2 release rate across all grassland sites and explained 37% and 58% of the variation in Q 10 across the steppe and meadow sites, respectively. Of these, precipitation was the best predictor of soil CO 2 release rate. Basal microbial respiration rate ( B ) was the most important predictor of Q 10 in steppe soils, whereas soil pH outweighed B as the major regulator in meadow soils. These results demonstrate that carbon quality and environmental variables coregulate Q 10 across alpine ecosystems, implying that modelers can rely on the “carbon‐quality temperature” hypothesis for estimating apparent temperature sensitivities, but relevant environmental factors, especially soil pH, should be considered in higher‐productivity alpine regions.

A review on the progress in experimental, simulation and theoretical studies for the modification of MnO 2 -based electrode materials from different perspectives of morphology, defect and heterojunction engineering.

Here we report a low-cost and facile synthesis approach for carbon-doped mesoporous anatase TiO2 by using Ti(BuO)4 as a source for both Ti and carbon through xerogel carbonization in a hypoxic atmosphere. The resultant mesoporous C-TiO2 with high crystallinity exhibits excellent photocatalytic activities for degradation of methyl orange (MO) and phenol under visible light irradiation.

Abstract Various strategies have been employed to enhance starch property, including thermal processing, chemical modification. The application of high hydrostatic pressure (HHP) may be a complementary, synergistic, or an additive starch enhancement technique. While most current applications of HHP are in starch processing, over 25 starches had been investigated by HHP, which can induce gelatinization and modification of some starches. Different starch responds differently to high pressure depending on the pressure range, starch source, pressurization temperature and time, different solvent and starch concentration. We have re‐examined the information on the various factors that influence the HHP‐induced structure, gelatinization, retrogradation, and modification of starches from different plant sources, with an emphasis on the HHP‐induced gelatinization. The compiled evidence of high pressure starch enhancement in this paper indicates that HHP is an effective technology with potential for greater utilization in starch application.

Abstract Aim To explore large‐scale patterns and the drivers of carbon:nitrogen:phosphorus (C:N:P) stoichiometry in heterotrophic microbes. Location A 3500‐km grassland transect on the Tibetan Plateau. Methods We investigated large‐scale C:N:P stoichiometry patterns in the soil microbial biomass and their relationships with abiotic factors and soil microbial community structures by obtaining soil samples from 173 sites across the Tibetan alpine grasslands. Results C:N:P ratios in the soil microbial biomass varied widely among grassland types, with higher microbial C:N, C:P and N:P ratios in the alpine steppe than the alpine meadow. The soil microbial C:N:P ratio (81:6:1) in the alpine steppe was significantly wider than the global average (42:6:1). Combined stepwise regression and generalized additive models revealed that variations in the microbial C:N ratio were primarily related to abiotic variables, with the microbial C:N ratio exhibiting a decreasing trend along the precipitation gradient. In contrast, variations in microbial C:P and N:P ratios were primarily associated with shifts in the community structure of soil microbes. The microbial C:P and N:P ratios were both negatively associated with all components of the soil microbial communities. However, the fungi to bacteria ratio only regulated the microbial C:P ratio. Main conclusions These results demonstrate that microbial C:N:P stoichiometry exhibits significant flexibility across various ecosystem types. This flexibility is partly induced by shifts in microbial community structure and variations in environmental conditions.

Abstract In recent years, perovskite/silicon tandem solar cells (PK/c‐Si tandem) have demonstrated high power conversion efficiency (PCE) and demonstrated great application potential in photovoltaic (PV) systems. However, the PCE of PK/c‐Si tandem devices is still below the theoretical limit. From a broader perspective, their poor stability and difficulty in large‐area realization are crucial barriers for commercial viability. In this report, the detailed constraints facing high PCE of tandem devices and the corresponding solutions are discussed. The authors propose that the main obstacle comes from the limitation of the perovskite top cell. However, careful understanding of the optical and electrical properties of each functional layer is expected to be the core process to further promote efficiency. Regarding the environmental and intrinsic instability issues, encapsulation is considered to be the most effective method to address environmental instability. Preventing ion migration is one of the fundamental methods to eliminate intrinsic instability. It is believed that low dimensional perovskite materials will become a competitive solution to simultaneously solve these two instabilities. Finally, some suggestions for reducing costs and preparation of PK/c‐Si tandem on a large‐scale are also discussed which provides guidance for further boosting the development of PK/c‐Si tandem.

MOTIVATION: More than half of proteins require binding of metal and acid radical ions for their structure and function. Identification of the ion-binding locations is important for understanding the biological functions of proteins. Due to the small size and high versatility of the metal and acid radical ions, however, computational prediction of their binding sites remains difficult. RESULTS: ) that are most frequently seen in protein databases. A sequence-based ab initio model is first trained on sequence profiles, where a modified AdaBoost algorithm is extended to balance binding and non-binding residue samples. A composite method IonCom is then developed to combine the ab initio model with multiple threading alignments for further improving the robustness of the binding site predictions. The pipeline was tested using 5-fold cross validations on a comprehensive set of 2,100 non-redundant proteins bound with 3,075 small ion ligands. Significant advantage was demonstrated compared with the state of the art ligand-binding methods including COACH and TargetS for high-accuracy ion-binding site identification. Detailed data analyses show that the major advantage of IonCom lies at the integration of complementary ab initio and template-based components. Ion-specific feature design and binding library selection also contribute to the improvement of small ion ligand binding predictions. AVAILABILITY AND IMPLEMENTATION: http://zhanglab.ccmb.med.umich.edu/IonCom CONTACT: hxz@imut.edu.cn or zhng@umich.eduSupplementary information: Supplementary data are available at Bioinformatics online.

The design and development of nanomaterials has become central to the advancement of pseudocapacitive performance. Many one-dimensional nanostructures (1D NSs), two-dimensional nanostructures (2D NSs), and three-dimensional hierarchical structures (3D HSs) composed of these building blocks have been synthesized as pseudocapacitive materials via different methods. However, due to the unclear assembly mechanism of these NSs, reports of HSs simultaneously assembled from two or more types of NSs are rare. In this article, NiCo2O4 multiple hierarchical structures (MHSs) composed of 1D nanowires and 2D nanosheets are simply grown on Ni foam using an ordered two-step hydrothermal synthesis followed by annealing processing. The low-dimensional nanowire is found to hold priority in the growth order, rather than the high-dimensional nanosheet, thus effectively promoting the integration of these different NSs in the assembly of the NiCo2O4 MHSs. With vast electroactive surface area and favorable mesoporous architecture, the NiCo2O4 MHSs exhibit a high specific capacitance of up to 2623.3 F g(-1), scaled to the active mass of the NiCo2O4 sample at a current density of 1 A g(-1). A nearly constant rate performance of 68% is achieved at a current density ranging from 1 to 40 A g(-1), and the sample retains approximately 94% of its maximum capacitance even after 3000 continuous charge-discharge cycles at a consistently high current density of 10 A g(-1).

When the number of books provided by library is relatively large, it becomes difficult for user to select appropriate book from a lot of candidate books. In this case, this paper designs a personalized recommendation system for college libraries based on hybrid recommendation algorithm. First of all, paper studies the application of collaborative filtering and content-based recommendation algorithm in the recommendation of university books, which involves reader classification, the establishment of user-item scoring matrix, the construction of vector space model and the calculation of similarity among users. And considering the characteristics of books and readers in universities, the user - item scoring matrix is improved, and clustering is used to alleviate the data sparsity problem. Do comparative experiments using the hybrid algorithm in data sets of Library of Inner Mongolia University of Technology. The results demonstrate that the hybrid methods can provide more accurate recommendations than pure approaches. Finally, the Spark big data platform combined with the hybrid recommendation algorithm is used to achieve the personalized book recommendation system design.

To convert the input into binary code, hashing algorithm has been widely used for approximate nearest neighbor search on large-scale image sets due to its computation and storage efficiency. Deep hashing further improves the retrieval quality by combining the hash coding with deep neural network. However, a major difficulty in deep hashing lies in the discrete constraints imposed on the network output, which generally makes the optimization NP hard. In this work, we adopt the greedy principle to tackle this NP hard problem by iteratively updating the network toward the probable optimal discrete solution in each iteration. A hash coding layer is designed to implement our approach which strictly uses the sign function in forward propagation to maintain the discrete constraints, while in back propagation the gradients are transmitted intactly to the front layer to avoid the vanishing gradients. In addition to the theoretical derivation, we provide a new perspective to visualize and understand the effectiveness and efficiency of our algorithm. Experiments on benchmark datasets show that our scheme outperforms state-of-the-art hashing methods in both supervised and unsupervised tasks.