Superior hydrogen evolution electrocatalysis enabled by CoP nanowire array on graphite felt

Renewable electricity-powered hydrogen production is an attractive alternative to unsustainable industrial processes, but the large-scale implantation of such sustainable technology still requires efficient and noble-metal-free electrocatalysts for driving cathodic hydrogen evolution reaction (HER), especially under alkaline conditions. In this paper, CoP nanowire array was in-situ developed on porous graphite felt (CoP/GF) as a new 3D electrocatalyst in facilitating hydrogen evolution electrocatalysis. This CoP/GF presents outstanding HER activity, requiring a low overpotential of 130 mV to deliver a current density of 20 mA cm^?2 as tested in 1.0 M KOH. Furthermore, this free-standing catalyst exhibits impressive long-term durability of up to 50 h under working conditions.     

Temperature dependent negative permittivity in solid solutions Sr2Mn1-xSnxO4 (x = 0, 0.3, 0.5)

A few compositions of the system Sr₂Mn_1-xSn_xO₄ (x = 0.0, 0.3, 0.5) were synthesized in the air by the solid-state ceramic route. A change in the sign (positive to negative) of the permittivity above a particular temperature (T_C) is observed at all the measured frequencies. The negative permittivity was analyzed by the Drude-Lorentz model. It was found that negative permittivity is caused by the plasma oscillations of thermally excited free charge carriers. Analysis of XPS spectra confirmed the presence of mixed-valence states of both Mn (Mn⁴⁺ and Mn³⁺) and Sn (Sn⁴⁺ and Sn²⁺) ions. The UV–vis.-IR spectroscopy results indicated generation of a large number of defect states in the forbidden bandgap region of Sr₂MnO₄ on the substitution of Sn at Mn site. Synthesized samples are promising metamaterials for radio frequency (10 Hz -2 MHz) region applications due to the high-temperature plasmonic behavior.     

Grain orientation evolution and multi-scale interfaces enhanced thermoelectric properties of textured Sr0.9La0.1TiO3 based ceramics

Sr_0.9La_0.1TiO₃ based textured ceramics (SLTT-S3T) with a texture fraction of 0.81 are successfully fabricated by the reactive template grain growth method, in which Sr_0.9La_0.1TiO₃/20 wt%Ti was used as matrix and 10 wt% plate-like Sr₃Ti₂O₇ template seeds were used as templates. The phase transition, microstructure evolution, and the anisotropic thermoelectric properties of SLTT-S3T ceramics were investigated. The results show that the ceramics are mainly composed of Sr_0.9La_0.1TiO₃ and rutile TiO₂ phases. Grains grow with a preferred orientation along (h00). A maximum ZT of 0.26 at 1073 K was achieved in the direction perpendicular to the tape casting direction. The low lattice thermal conductivity of 1.9 W/(m K) at 1073 K was obtained decreased by 34%, 40%, and 38% compared with non-textured, SrTiO₃ and Sr_0.9La_0.1TiO₃ ceramics prepared by the same process, can be attributed to the enhanced phonon scattering by the complex multi-scale boundaries and interfaces. This work provides a strategy of microstructural design for thermoelectric oxides to decrease intrinsic lattice thermal conductivity and further regulate thermoelectric properties via texture engineering.     

Cooking and pasteurizing evaluation of barramundi (Lates calcarifer) meats subjected to an emerging microwave-assisted induction heating (MAIH) technology

Microwave-assisted induction heating (MAIH) is a complex heating process characterized by rapid and uniform heating based on a combination of microwave heating (power: 1000 W; frequency: 2450 MHz) applied from above and electromagnetic induction heating (power: 1500 W) applied from below. In this study, we assessed the utility of MAIH in processing the flesh of barramundi (Lates calcarifer), an important aquaculture species in Asian countries. To determine the effects of different heating conditions on the microbial contamination and physicochemical qualities of barramundi meat, fresh barramundi meats (100 g) were sealed in heat-resistant crystallized polyethylene terephthalate containers containing 150 mL of saline solution, and were subsequently heated in MAIH to 90 °C or 70 °C and held to different time. The results revealed that in response to an increase in the duration of heating, there were reductions in the aerobic plate count, psychrotrophic bacteria count, hydrogen sulphide-producing bacteria count, and coliform count, along with lower contents of total volatile basic nitrogen, in treated samples. Conversely, with a prolongation of the time exposed to heat, we detected increases in the L* (lightness),W (whiteness), ΔE(colour difference), and texture (hardness and chewiness) of fish meat, whereas there was a reduction of a* (redness). When the samples were heated for at least 110 s to 90 °C or for at least 130 s to 70 °C, the barramundi meats obtained appeared to be fully cooked with microbial counts below the limits of detection. In addition, under both heating conditions, thermal imaging revealed a uniform distribution of temperature within treated samples. Furthermore, monitoring of temperature profiles at central points in fish samples revealed that maximum temperatures of 97 °C and 79 °C were reached in response to MAIH processing at 90 °C for 110 s and 70 °C for 130 s, respectively. Thus, on the basis of the quality considerations of appearance, microbial count, and texture, the recommended optimal heating conditions for barramundi meats produced using MAIH are 90 °C for 110 s and 70 °C for 130 s.Industrial relevanceThe MAIH equipment is a novel thermal processing technology and allows food to be heated and pasteurized simultaneously after the package is sealed, eliminates issues of secondary contamination due to the need for subsequent packaging, and extends the shelf life. MAIH technology, therefore, serves as a viable future alternative for thermal processing of manufacturing refrigerated ready-to-eat seafood.     

Highly-hydrophobic nanofiber mat for efficient colonic delivery of lactoferrin: Preparation,characterization and release mechanism

Lactoferrin (Lf) is a bioactive protein with varied biological effects. To improve its anti-digestive stability in oral administration, a novel nanocarrier with high hydrophobicity for colonic delivery of Lf was creatively developed by modified coaxial electrospinning. First, a suitable biocompatible solvent, acetic acid, was screened as the mono-solvent for ES100 electrospinning, creating highly-hydrophobic ES100 nanofiber mat (contact angle = 133.8^o). Then, Lf-loaded W/O emulsion was prepared as the core fluid to ensure the successful coaxial electrospinning and generate Lf encapsulated core/shell nanofiber mat (ES@Lf). Lf was demonstrated maintaining structural integrity and anti-colon cancer activity during encapsulation and oral delivery. In vitro assay indicated 92.3% Lf was sustainably released in colon, and its release followed a complex mechanism in which the erosion was dominant. Instead of pH-dependent erosion, the synergistic action through gut microbiota adhesion and their metabolites, especially short-chain fatty acids, was illustrated for disintegration of ES@Lf nanofiber for the first time.     

Investigation of stress fields for non-standard sized glass plates loaded by ring-on-ring

A ring-on-ring (ROR) test is a prevailing test method for evaluating the equi-biaxial strength of glass materials. However, current ROR test standards limit the strength and size of glass to prevent a nonlinear behavior. In this study, the feasibility of ROR testing for non-standard, high-strength glass, such as tempered or ion-exchanged rectangular glass is investigated. To this end, ROR simulation based on theory and experiment is conducted for thirty non-standard glasses with widths of 100–300 mm and aspect ratios of 1.0–2.0. As a result, the maximum measurable stress was about 215.6 MPa for 100 × 200 mm glass and 481.3 MPa for 300 × 600 mm glass with a 3% deviation, which is well above the strength of regular tempered glass. The main purpose of this work is to understand the range of aspect ratio of horizontal and vertical widths of a glass plate that can be evaluated by the standard ROR test.     

3D-printing of polymer‐derived SiCN ceramic matrix composites by digital light processing

In this study, we present a DLP 3D-printing strategy for the fabrication of SiCN ceramic matrix composites (CMCs). The polysilazane-based preceramic polymer containing inert fillers was UV-cured into a green body and then converted to SiCN CMCs after pyrolysis. The introduced fillers (Si₃N₄ particles and Si₃N₄ whiskers) as reinforcements are well dispersed in the matrix, which can not only effectively reduce the linear shrinkage and weight loss, but also greatly improve the mechanical properties of the SiCN CMCs. The bending strength of the SiCN CMCs reinforced with 10 wt% Si₃N₄ whiskers (without surface polished) reached 180.7 ± 15.6 MPa. Furthermore, the effect of fillers content on microstructure and porosity of the SiCN CMCs are discussed, and it was found that the excessive fillers led to increased pore defects and decreased continuity of the matrix, thereby reducing the mechanical properties of the SiCN CMCs. This strategy provides a promising ceramic manufacturing technique to fabricate polymer‐derived CMCs with complex-shaped and high-performance for potential demanding applications.     

Estimating long-term time-resolved indoor PM2.5 of outdoor and indoor origin using easily obtainable inputs

To evaluate the separate impacts on human health and establish effective control strategies, it is crucial to estimate the contribution of outdoor infiltration and indoor emission to indoor PM_2.5 in buildings. This study used an algorithm to automatically estimate the long-term time-resolved indoor PM_2.5 of outdoor and indoor origin in real apartments with natural ventilation. The inputs for the algorithm were only the time-resolved indoor/outdoor PM_2.5 concentrations and occupants’ window actions, which were easily obtained from the low-cost sensors. This study first applied the algorithm in an apartment in Tianjin, China. The indoor/outdoor contribution to the gross indoor exposure and time-resolved infiltration factor were automatically estimated using the algorithm. The influence of outdoor PM_2.5 data source and algorithm parameters on the estimated results was analyzed. The algorithm was then applied in four other apartments located in Chongqing, Shenyang, Xi'an, and Urumqi to further demonstrate its feasibility. The results provided indirect evidence, such as the plausible explanations for seasonal and spatial variation, to partially support the success of the algorithm used in real apartments. Through the analysis, this study also identified several further development directions to facilitate the practical applications of the algorithm, such as robust long-term outdoor PM_2.5 monitoring using low-cost light-scattering sensors.     

A recurrent neural network using historical data to predict time series indoor PM2.5 concentrations for residential buildings

Due to the severe outdoor PM2.5 pollution in China, many people have installed air-cleaning systems in homes. To make the systems run automatically and intelligently, we developed a recurrent neural network (RNN) that uses historical data to predict the future indoor PM2.5 concentration. The RNN architecture includes an autoencoder and a recurrent part. We used data measured in an apartment over the course of an entire year to train and test the RNN. The data include indoor/outdoor PM2.5 concentration, environmental parameters and time of day. By comparing three different input strategies, we found that a strategy employing historical PM2.5 and time of day as inputs performed best. With this strategy, the model can be applied to predict the relatively stable trend of indoor PM2.5 concentration in advance. When the input length is 2 h and the prediction horizon is 30 min, the median prediction error is 8.3 µg/m³ for the whole test set. For times with indoor PM2.5 concentrations between (20,50] µg/m³ and (50,100] µg/m³, the median prediction error is 8.3 and 9.2 µg/m³, respectively. The low prediction error between the ground-truth and predicted values shows that the RNN can predict indoor PM2.5 concentrations with satisfactory performance.