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Abstract Efficient thermal management has become one of the most critical issues of electronics because of the high heat flux generated from highly integrated, miniaturized, and increased power. Here we report highly flexible composites with aligned and overlapping interconnected boron nitride nanosheets (BNNSs) assembled in wrinkle structures. Besides high in-plane thermal conductivity of more than 26.58 W m−1 K−1, such structure rendered enhanced through-plane conduction along with increasing pre-stain. As thermal interface materials (TIMs) of both rigid and flexible devices, the composites revealed an outstanding thermal cooling capability outperforming some commercial TIMs. During a record-long bending process of more than 3000 cycles, the maximum temperature fluctuation of the flexible device with 100%-prestrained composite was only within 0.9 °C, less than one-third of that with commercial thermal pad. Moreover, the composite revealed a superior impermeability for flexible seals. Our results illustrate that the composites could be an ideal candidate for the thermal management of emerging flexible electronics.

Salted duck eggs are a type of traditional Chinese pickled delicacy, and moisture and lipid content are important indexes for evaluating the quality during processing. This study used a hyperspectral imaging (HSI) system in conjunction with chemometrics to investigate the content change and distribution of moisture and lipid during different salting stages of duck eggs. The HSI was used to obtain reflectance spectral information of salted duck eggs in the 432~961 nm wavelength range. To minimize the noise in spectral signals, three preprocessing methods including Savitzky-Golay smoothing (SG), Gauss filter smoothing (Gauss), and standard normal variation (SNV) were used. The competitive adaptive reweighted sampling (CARS) was used to select the optimal wavelengths for predicting moisture and lipid content, and then the partial least squares regression (PLSR) and artificial neural network (ANN) methods were used to predict moisture and lipid content quantitatively. Results showed that ANN model could exhibited a better performance in predicting moisture and lipid content with coefficients of determination of the protein moisture, yolk moisture and yolk lipid of 0.9306, 0.9552 and 0.8896 respectively. Finally, the ANN model was used to create a distribution map of moisture and lipid content in the profile of salted duck eggs. The visualization distribution maps successfully display the distribution of moisture and lipid content during different salting periods. This study could lead to the development of a rapid detection method based on spectrum technology for production and processing of salted duck egg industry.

This study evaluated the differences in oral processing and texture perception of breads with varying compositions. The research investigated the dynamic changes in moisture content (MC), reducing sugars (RSs), and chewiness of the bolus formed from white bread (B0) and 50% whole-wheat bread (B50) during oral processing. Hyperspectral imaging (HSI) combined with chemometric methods was used to establish quantitative prediction models for MC, RSs, and chewiness, and to create visual distribution maps of these parameters. The results showed that B0 had a higher moisture content and a faster hydration rate than B50 during the initial stages of oral processing, indicating greater hydrophilicity and ease of saliva wetting. Additionally, the uniformity of moisture distribution in the bolus of B0 was higher than that of B50. B50 exhibited significantly lower RSs content and poorer distribution uniformity compared to B0. The primary differences in chewiness between the two types of bread were observed during the early stages of oral processing, with B50 requiring more chewing effort initially. This study demonstrated that HSI technology can effectively monitor and elucidate the compositional changes in food particles during oral processing, providing new insights into bread texture perception and offering a scientific basis for improving bread processing and texture.

Abstract Wearable electronics with miniaturization and high-power density call for devices with advanced thermal management capabilities, outstanding flexibility, and excellent permeability. However, it is difficult to achieve these goals simultaneously due to the conflict between high thermal conductivity and permeability and flexibility. Here, we report an approach to fabricate flexible, breathable composites with advanced thermal management capability by coating the boron nitride nanosheets (BNNSs) layer with high thermal conductivity on the grids of patterned electrospun thermoplastic polyurethane (TPU) fibrous mats. The composite exhibited a significant enhancement of thermal conductivity and preserved instinctive breathability simultaneously. When the composite was integrated into flexible devices, its saturating operating temperature dropped significantly compared to that of pure Ecoflex packaging. Moreover, the surface temperature fluctuation was less than 0.5 °C during more than 2000 cycles bending-releasing process. Finally, a prototype to fabricate wearable electronics with advanced thermal management capability was proposed.

Highlights A core–sheath structured phase change nanocomposite (PCN) with aligned and overlapping interconnected BNNS networks were successfully fabricated. The PCN has an ultrahigh in-plane thermal conductivity (28.3 W m−1 K−1), excellent flexibility and high phase change enthalpy (101 J g−1). The PCN exhibits intensively potential applications in the thermal management of 5G base stations and thermoelectric generators.

Abstract Dielectric polymer nanocomposites are considered as one of the most promising candidates for high-power-density electrical energy storage applications. Inorganic nanofillers with high insulation property are frequently introduced into fluoropolymer to improve its breakdown strength and energy storage capability. Normally, inorganic nanofillers are thought to introducing traps into polymer matrix to suppress leakage current. However, how these nanofillers effect the leakage current is still unclear. Meanwhile, high dopant (> 5 vol%) is prerequisite for distinctly improved energy storage performance, which severely deteriorates the processing and mechanical property of polymer nanocomposites, hence brings high technical complication and cost. Herein, boron nitride nanosheet (BNNS) layers are utilized for substantially improving the electrical energy storage capability of polyvinylidene fluoride (PVDF) nanocomposite. Results reveal that the high conduction band minimum of BNNS produces energy barrier at the interface of adjacent layers, preventing the electron in PVDF from passing through inorganic layers, leading to suppressed leakage current and superior breakdown strength. Accompanied by improved Young’s modulus (from 1.2 GPa of PVDF to 1.6 GPa of nanocomposite), significantly boosted discharged energy density (14.3 J cm−3) and charge–discharge efficiency (75%) are realized in multilayered nanocomposites, which are 340 and 300% of PVDF (4.2 J cm−3, 25%). More importantly, thus remarkably boosted energy storage performance is accomplished by marginal BNNS. This work offers a new paradigm for developing dielectric nanocomposites with advanced energy storage performance.

The development of wearable and portable electronics calls for flexible piezoelectric materials to fabricate self-powered devices. However, a big challenge in piezoelectric material design is to boost the output performance while ensuring its flexibility and biocompatibility. Here, all-organic and core-shell structured silk fibroin (SF)/poly(vinylidene difluoride) (PVDF) piezoelectric nanofibers (NFs) with excellent flexibility are fabricated using a simple electrospinning strategy. The strong intermolecular interaction between SF and PVDF promotes the β-phase nucleation in the core-shell structure, which significantly enhances the output performance. An output of 16.5 ​V was achieved in SF/PVDF NFs, which is more than 6-fold enhancement compared with that of pure PVDF NFs. In addition, the piezoelectric device can sensitively detect the mechanical stimulation from joint bending, demonstrating its great potential in self-powered sensor. Otherwise, the piezoelectric device can be also applied to control the movement of a smart car, successfully, achieving its application in the human-machine interaction.

This work aims to investigate a feasible and practical technique for the authentication of edible animal blood food (EABF) using Fourier transform near-infrared (FT-NIR) coupled with fast chemometrics. A total of 540 samples were used, including raw duck blood tofu (DBT), cow blood-based gel (CBG), pig blood-based gel (PBG), and DBT binary and ternary adulterated with CBG and PBG. The protein, fat, total sugar, and 16 kinds of amino acids were measured to validate the difference in basic organic matters among EABFs according to species. Fisher linear discriminate analysis (Fisher LDA) and extreme learning machine (ELM) were implemented comparatively to identify the adulterated EABF. To predict adulteration levels, four extreme learning machine regression (ELMR) models were constructed and optimized. Results showed that, by analyzing 27 crucial spectral variables, the ELM model provides higher accuracy of 93.89% than Fisher LDA for the independent samples. All the correlation coefficients of the optimized ELMR models’ training and prediction sets were better than 0.94, the root mean square errors were all less than 3.5%, and the residual prediction deviation and the range error ratios were all higher than 4.0 and 12.0, respectively. In conclusion, the FT-NIR paired with ELM have great potential in authenticating the EABF. This work presents amino acids content in EABFs for the first time and built tracing models for rapid authentication of DBT, which can be used to manage the EABF market, thereby preventing illegal adulteration and unfair competition.

Maturity is a crucial indicator in assessing the quality of tomatoes, and it is closely related to lycopene content. Using hyperspectral imaging, this study aimed to monitor tomato maturity and predict its lycopene content at different maturity stages. Standard normal variable (SNV) transformation was applied to preprocess the hyperspectral data. Then, using competitive adaptive reweighted sampling (CARS), the characteristic wavelengths were selected to simplify the calibration models. Based on the full and characteristic wavelengths, a support vector classifier (SVC) model was developed to determine tomato maturity qualitatively. The results demonstrated that the classification accuracy using the characteristic wavelength led to the obtention of better results with an accuracy of 95.83%. In addition, the support vector regression (SVR) and partial least squares regression (PLSR) models were utilized to predict lycopene content. With a coefficient of determination for prediction (R2P) of 0.9652 and a root mean square error for prediction (RMSEP) of 0.0166 mg/kg, the SVR model exhibited the best quantitative prediction capacity based on the characteristic wavelengths. Following this, a visual distribution map was created to evaluate the lycopene content in tomato fruit intuitively. The results demonstrated the viability of hyperspectral imaging for detecting tomato maturity and quantitatively predicting the lycopene content during storage.

Abstract Phase change materials (PCMs) can be used for efficient thermal energy harvesting, which has great potential for cost-effective thermal management and energy storage. However, the low intrinsic thermal conductivity of polymeric PCMs is a bottleneck for fast and efficient heat harvesting. Simultaneously, it is also a challenge to achieve a high thermal conductivity for phase change nanocomposites at low filler loading. Although constructing a three-dimensional (3D) thermally conductive network within PCMs can address these problems, the anisotropy of the 3D framework usually leads to poor thermal conductivity in the direction perpendicular to the alignment of fillers. Inspired by the interlaced structure of spider webs in nature, this study reports a new strategy for fabricating highly thermally conductive phase change composites (sw-GS/PW) with a 3D spider web (sw)-like structured graphene skeleton (GS) by hydrothermal reaction, radial freeze-casting and vacuum impregnation in paraffin wax (PW). The results show that the sw-GS hardly affected the phase transformation behavior of PW at low loading. Especially, sw-GS/PW exhibits both high cross-plane and in-plane thermal conductivity enhancements of ~ 1260% and ~ 840%, respectively, at an ultra-low filler loading of 2.25 vol.%. The thermal infrared results also demonstrate that sw-GS/PW possessed promising applications in battery thermal management.

Abstract With the trend of integration, miniaturization, and increasing power density of stretchable electronic devices, real-time thermal dissipation is becoming crucial. Seeking materials and/or structures with advanced thermal management for stretchable electronics becomes an urgent issue. For passive thermal management, the traditional thermal interfacial materials (TIMs) with flexibility cannot meet the demand of stretchable electronics, because stretchable devices are usually required to experience a large scale of bending, twisting, stretching, and so on. The main challenge facing thermal management for stretchable electronics is how to maintain stable thermal conductance under large deformation. Here, we examine the development of materials and structures available in this field. We also propose a comparative analysis of the existing challenges and provide possible solutions for the thermal management of stretchable electronics in the future.

Though stretchable strain sensors are attractive for next-generation applications due to their high sensitivity, heat generated in these devices limits their reliability. Here, the authors report boron nitride nanosheet-based stretchable strain sensors with enhanced thermal management.

Surface charge density has been demonstrated to be significantly impacted by the dielectric properties of tribomaterials. However, the ambiguous physical mechanism of dielectric manipulated charge behavior still restricts the construction of high-performance tribomaterials. Here, using the atomic force microscopy and Kelvin probe force microscopy, an in situ method was conducted to investigate the contact electrification and charge dynamics on a typical tribomaterial (i.e., BaTiO3/PVDF-TrFE nanocomposite) at nanoscale. Combined with the characterization of triboelectric device at macroscale, it is found that the number of transferred electrons increases with contact force/area and tends to reach saturation under increased friction cycles. The incorporated high permittivity BaTiO3 nanoparticles enhance the capacitance and electron trapping capability of the nanocomposites, efficiently inhibiting the lateral diffusion of electrons and improving the output performance of the triboelectric devices. Exponential decay of the surface potential is observed over monitoring time for all dielectric samples. At high BaTiO3 loadings, more electrons can drift into the bulk and combine with the induced charges on the back electrode, forming a large leakage current and accordingly accelerating the electron dissipation. Hence, the charge trapping/storing and dissipating, as well as the charge attracting properties, should be comprehensively considered in the design of high-performance tribomaterials.

Polymer nanocomposites have attracted broad attention in the area of dielectric and energy storage. However, the electrical and chemical performance mismatch between inorganic nanoparticles and polymer leads to interfacial incompatibility. In this study, phosphonic acid molecules with different functional ligands were introduced to the surface of BaTiO3 (BT) nanoparticles to tune their surface properties and tailor the host–guest interaction between BT and poly(vinylideneflyoride-co-hexafluroro propylene) (P(VDF-HFP)). The dielectric properties and electrical energy storage capability of the nanocomposites were recorded by broadband dielectric spectroscopy and electric displacement measurements, respectively. The influence of the ligand length and polarity on the dielectric properties and electrical energy storage of the nanocomposites was documented. The nanocomposite with 5 vol% 2,3,4,5,6-pentafluorobenzyl phosphonic acid (PFBPA)-modified BT had the highest energy density of 12.8 J cm−3 at 400 MV m−1, i.e., a 187% enhancement in the electrical energy storage capability over the pure P(VDF-HFP). This enhancement can be attributed to the strong electron-withdrawing effect of the pentafluorobenzyl group of PFBPA, which changed the electronic nature of the polymer–particle interface. On the other hand, PFBPA improves the compatibility of the host–guest interface in the nanocomposites and decreases the electrical mismatch of the interface. These results provide new insights into the design and preparation of high-performance dielectric nanocomposites.

In this study, electronic nose (E-nose) and Hyperspectral Imaging (HSI) was employed for nondestructive monitoring of ultrasound efficiency (20KHZ) in the inactivation of Salmonella Typhimurium, and Escherichia coli in inoculated pork samples treated for 10, 20 and 30 min.Weibull, and Log-linear model fitted well (R2 ≥ 0.9) for both Salmonella Typhimurium, and Escherichia coli inactivation kinetics. The study also revealed that ultrasound has antimicrobial effects on the pathogens. For qualitative analysis, unsupervised (PCA) and supervised (LDA) chemometric algorithms were applied. PCA was used for successful sample clustering and LDA approach was used to construct statistical models for the classification of ultrasound treated and untreated samples. LDA showed classification accuracies of 99.26%,99.63%,99.70%, 99.43% for E-nose - S. Typhimurium, E-nose -E. coli, HSI - S. Typhimurium and HSI -E. coli respectively. PLSR quantitative models showed robust models for S. Typhimurium- (E-nose Rp2 = 0.9375, RMSEP = 0.2107 log CFU/g and RPD = 9.7240 and (HSI Rp2 = 0.9687 RMSEP = 0.1985 log CFU/g and RPD = 10.3217) and E. coli -(E-nose -Rp2 = 0.9531, RMSEP = 0.2057 log CFU/g and RPD = 9.9604) and (HIS- Rp2 = 0.9687, RMSEP = 0.2014 log CFU/g and RPD = 10.1731).This novel study shows the overall effectiveness of applying E-nose and HSI for in-situ and nondestructive detection, discrimination and quantification of bacterial foodborne pathogens during the application of food processing technologies like ultrasound for pathogen inactivation.

High voltage power cables play a critical role in global electricity transmission and distribution. The currently used power cables cannot fulfil the green and sustainable requirement of modern society because of the thermoset nature of cable insulation and shields. This study is aimed at developing thermoplastic shields for high voltage power cable, which is one bottleneck restricting the development of environmental-friendly cables. Using carbon black (CB) as the main conductive component and a small amount of carbon nanotubes (CNTs) or graphene as the second filler, highly conductive polypropylene based composite materials were prepared for potential shield applications. It was found that, at a fixed conductive filler loading, the replacement of a small amount CB by CNTs can significantly enhance the electrical conductivity and suppress its temperature dependence. However, when CB was replaced by graphene, only limited enhancement of electrical conductivity could be achieved and the electrical conductivity is still highly dependent on temperature. Dissipative particle dynamics simulations demonstrated that the enhanced conduction property in the CNTs-containing composites could be understood by the shorter average distance between CB and CNTs. Finally, the coordination between the newly developed conductive composites and the environmental-friendly thermoplastic polypropylene insulation was evaluated via high voltage direct current measurements, and the results revealed that the CNTs-containing composites showed excellent suppression effect on the space charge injection and accumulation in the insulation. This research paved a new way for developing environmental-friendly high voltage power cable shields.

The present study was aimed at developing a low-cost but rapid technique for qualitative and quantitative detection of beef adulterated with pork. An electronic nose based on colorimetric sensors was proposed. The fresh beef rib steaks and streaky pork were purchased and used from the local agricultural market in Suzhou, China. The minced beef was mixed with pork ranging at levels from 0%~100% by weight at increments of 20%. Protein, fat, and ash content were measured for validation of the differences between the pure beef and pork used in basic chemical compositions. Fisher linear discriminant analysis (Fisher LDA) and extreme learning machine (ELM) were utilized comparatively for identification of the ground pure beef, beef−pork mixtures, and pure pork. Back propagation-artificial neural network (BP-ANN) models were built for prediction of the adulteration levels. Results revealed that the ELM model built was superior to the Fisher LDA model with higher identification rates of 91.27% and 87.5% in the training and prediction sets respectively. Regarding the adulteration level prediction, the correlation coefficient and the root mean square error were 0.85 and 0.147 respectively in the prediction set of the BP-ANN model built. This suggests, from all the results, that the low-cost electronic nose based on colorimetric sensors coupled with chemometrics has a great potential in rapid detection of beef adulterated with pork.

Polymer dielectrics with high energy density and low dielectric loss are highly desired due to the rapid development of electric devices. Among known polymers, poly(vinylidene fluoride-ter-trifluoroethylene-ter-chlorofluoroethylene) P(VDF-TrFE-CFE) is one of the promising materials for energy storage capacitor applications because of its high dielectric constant. Nevertheless, it suffers from high dielectric loss especially at the high electric field, which suppresses its breakdown strength and energy storage density. Herein, sandwiched structure dielectric films were fabricated by employing polymethyl methacrylate (PMMA) as the outer layer and P(VDF-TrFE-CFE) as the central layer. By modulating the thickness of the central layer, an enhanced discharged energy density of 7.03 J/cm^3 is achieved at a high electric field of 480 MV/m, which is 132% more than that of P(VDF-TrFE-CFE) at its maximum electric field 300 MV/m. Meanwhile, this sandwiched structure film also retains a high discharge efficiency of 78% at 480 MV/m, which is never been seen in polyvinylidene fluoride-based polymers. Results show that PMMA acts as charge barrier and simultaneously enhance the breakdown strength and suppress the dielectric loss of P(VDF-TrFE-CFE).

Heat dissipation becomes a critical problem because of the miniaturisation and the increase of power density in electronic devices and electric equipment, which calls for electrical insulating materials with high thermal management capability. Epoxy thermosets have been widely used as electrical insulating materials, but suffer from their low thermal conductivity. This study reviewed the research progress on the development of epoxy thermosets with high pristine thermal conductivity. First, the thermal conduction mechanism of polymers was briefly introduced. Second, the approaches used to enhance the thermal conductivity of epoxy thermosets were summarised, which mainly dealt with the formation of microscopically anisotropic but macroscopically isotropic structure in the epoxy thermosets. Third, the applications of high thermal conductivity epoxy thermoset resins were reviewed. Finally, the review provided the existing challenges and the future directions for the development of epoxy thermosets with high pristine thermal conductivity.

Electronic tongue coupled with linear and non-linear multivariate algorithms was attempted to address the drawbacks of fish freshness detection. Parabramis pekinensis fish samples stored at 4°C were used. Total volatile basic nitrogen (TVB-N) and total viable count (TVC) of the samples were measured. Fisher liner discriminant analysis (Fisher LDA) and support vector machine (SVM) were applied comparatively to classify the samples stored at different days. The results revealed that SVM model was better than Fisher LDA model with a higher identification rate of 97.22% in the prediction set. Partial least square (PLS) and support vector regression (SVR) were applied comparatively to predict the TVB-N and TVC values. The quantitative models were evaluated by the root mean square error of prediction (RMSEP) and the correlation coefficient in the prediction set (Rpre). The results revealed that SVR model was superior to PLS model with RMSEP = 5.65 mg/100 g, Rpre = 0.9491 for TVB-N prediction and RMSEP = 0.73 log CFU/g, Rpre = 0.904 for TVC prediction. This study demonstrated that the electronic tongue together with SVM and SVR has a great potential for a convenient and nondestructive detection of fish freshness.

The authors wish to make the following corrections to this paper [...]

In this paper, we report the use of magnesium alloy (AZ31B) as the matching material for PZT-5H ultrasonic transducers. The AZ31B has an acoustic impedance of 10.3 MRayl, which provides a good acoustic impedance match for PZT-5H ultrasonic transducers in water medium based on the double matching layer theory. Two PZT-5H transducers with different center frequencies were designed and fabricated using the AZ31B. The respective center frequencies of the two fabricated transducers were 4.6 MHz and 9.25 MHz. The 4.6 MHz transducer exhibits a −6 dB bandwidth of 79% and two-way insertion loss of −11.11 dB. The 9.25 MHz transducer also shows good performance: −6 dB bandwidth of 71% and two-way insertion loss of −14.43 dB. The properties of the two transducers are superior to those of transducers using a composite matching layer, indicating that the magnesium alloy may be a promising alternative for high-performance transducers.

The Tibetan Plateau (TP) hosts a variety of vegetation types ranging from broadleaved and needle-leaved forests at the lower altitudes and mesic areas to alpine grassland at the higher altitudes and xeric areas. Accurate and detailed mapping of the vegetation distribution on TP is essential for an improved understanding of climate change effects on terrestrial ecosystems. Yet, existing land cover datasets of TP are either provided at a low spatial resolution or have insufficient vegetation types to characterize certain unique TP ecosystems, such as the alpine scree. Here, we produced a 10 m resolution TP land cover map with 12 vegetation classes and 3 non-vegetation classes for the year 2022 (referred as TP_LC10-2022) by leveraging state-of-the-art remote sensing approaches including the Sentinel-1 and Sentinel-2 imagery, environmental and topographic datasets, and 4 machine learning models using Google Earth Engine platform. Our dataset TP_LC10-2022 achieved an overall classification accuracy of 86.5% with a Kappa coefficient of 0.854. By comparing with 4 existing global land cover products, TP_LC10-2022 showed significant improvements in terms of reflecting local-scale vertical variations in the southeast TP region. Moreover, we found that alpine scree occupied 13.99% of the TP region which was ignored in existing land cover datasets, and that shrublands occupied 4.63% of the TP region characterized by distinct forms of deciduous shrublands and evergreen shrublands largely determined by topography and missed in existing land cover datasets. Our dataset provides a solid foundation for further analyses which need accurate delineation of these unique vegetation types in TP. The TP_LC10-2022 and the sample dataset are freely available at https://doi.org/10.5281/zenodo.8228112 (Huang et al., 2023b) and https://doi.org/10.5281/zenodo.8227942 (Huang et al., 2023a) respectively. Additionally, the classification map can be viewed through https://cold-classifier.users.earthengine.app/view/tplc10-2022. [ABSTRACT FROM AUTHOR]