Lightweight, flame retardant Janus carboxymethyl cellulose aerogel with fire-warning properties for smart sensor.

Lightweight, flame retardant biomass aerogels combining with multi-functionalities are promising for thermal insulation, noise absorption and smart sensors. However, high flammability hinders the application of these aerogels in extreme condition. Herein, lightweight, flame retardant aerogel with fire-warning properties fabricated from resource-abundant graphite and green carboxymethyl cellulose (CMC) is reported. During sonicating expandable graphite (EG) in CMC solution, CMC not only fabricates the downsizing process via hydrogen bonding effect but also forms stable dispersions. Then biomass aerogel is fabricated by freeze-drying strategy and enhanced by metal ionic cross-linking method. This aerogel demonstrates Janus properties for electrical conductivity and thermal conductivity. Due to the synergistic flame retardant effect of graphite nanocomposite and metal ions with a barrier effect and catalytic carbonization capacity, the flame retardancy of these aerogels are enhanced with fire-warning properties. Furthermore, these aerogels are used for monitoring physical deformations as smart sensors, which provides inspiration and a sustainable solution for developing low-cost biomass aerogel with multifunction.

Versatile Liquid Metal/Alginate Composite Fibers with Enhanced Flame Retardancy and Triboelectric Performance for Smart Wearable Textiles.

Liquid metal (LM) shows the superiority in smart wearable devices due to its biocompatibility and electromagnetic interference (EMI) shielding. However, LM based fibers that can achieve multifunctional integrated applications with biodegradability remain a daunting challenge. Herein, versatile LM based fibers are fabricated first by sonication in alginate solution to obtain LM micro/nano droplets and then wet-spinning into LM/alginate composite fibers. By mixing with high-concentration alginate solution (4-6 wt.%), the LM micro/nano droplets stability (colloidal stability for > 30 d and chemical stability for > 45 d) are not only improved, but also facilitate its spinning into fibers through bimetallic ions (e.g., Ga3+ and Ca2+ ) chelation strategy. These resultant fibers can be woven into smart textiles with excellent flexibility, air permeability, water/salt resistance, and high temperature tolerance (-196-150 °C). In addition, inhibition of smoldering result from the LM droplets and bimetallic ions is achieved to enhance flame retardancy. Furthermore, these fibers combine the exceptional properties of LM droplets (e.g., photo-thermal effect and EMI shielding) and alginate fibers (e.g., biocompatibility and biodegradability), applicable in wearable heating devices, wireless communication, and triboelectric nanogenerator, making it a promising candidate for flexible smart textiles.

Premature conclusions about the signal-to-noise ratio in structural equation modeling research: A commentary on Yuan and Fang (2023).

In a recent article published in this journal, Yuan and Fang (British Journal of Mathematical and Statistical Psychology, 2023) suggest comparing structural equation modeling (SEM), also known as covariance-based SEM (CB-SEM), estimated by normal-distribution-based maximum likelihood (NML), to regression analysis with (weighted) composites estimated by least squares (LS) in terms of their signal-to-noise ratio (SNR). They summarize their findings in the statement that "[c]ontrary to the common belief that CB-SEM is the preferred method for the analysis of observational data, this article shows that regression analysis via weighted composites yields parameter estimates with much smaller standard errors, and thus corresponds to greater values of the [SNR]." In our commentary, we show that Yuan and Fang have made several incorrect assumptions and claims. Consequently, we recommend that empirical researchers not base their methodological choice regarding CB-SEM and regression analysis with composites on the findings of Yuan and Fang as these findings are premature and require further research.

Copper-Coordinated Cellulose Fibers for Electric Devices with Motion Sensitivity and Flame Retardance.

Nanocomposite conductive fibers are of great significance in applications of wearable devices, smart textiles, and flexible electronics. Integration of conductive nanomaterials into flexible bio-based fibers with multifunctionality remains challenging due to interface failure, poor flexibility, and inflammability. Although having broader applications in textiles, regenerated cellulose fibers (RCFs) cannot meet the requirements of wearable electronics owing to their intrinsic insulation. In this study, we constructed conductive RCFs fabricated by coordinating copper ions with cellulose and reducing them into stable Cu nanoparticles coated on their surface. The Cu sheath offered excellent electrical conductivity (4.6 × 105 S m-1), electromagnetic interference shielding, and enhanced flame retardance. Inspired by plant tendrils, the conductive RCF was wrapped around an elastic rod to develop wearable sensors for human health and motion monitoring. The resultant fibers not only form stable conductive nanocomposites on the fiber surface by chemical bonds but also exhibit a huge potential for wearable devices, smart sensors, and flame-retardant circuits.

Molecular cloning and characterization of a novel bifunctional cellobiohydrolase/ß-xylosidase from a metagenomic library of mangrove soil.

A metagenomic library of mangrove soil samples consisting of approximately 11,000 clones was constructed, and a rare bifunctional cellobiohydrolase/ß-xylosidase Cbh2124 was identified by functional screening. Cbh2124 displayed the highest homology (56.43%) with a protein of the glycoside hydrolase 10 (GH10) family from Proteobacteria. Phylogenetic analysis confirmed that Cbh2124 belongs to the GH10 family. The recombinant enzyme showed a strong cellobiohydrolase activity and a relatively high ß-xylosidase activity, and its catalytic efficiency to the cellobiose substrate was as high as 1.27 × 105 s-1·mM-1, the highest efficiency among reported cellobiohydrolases. Of particular interest, some enzymatic properties of the ß-xylosidase activity of Cbh2124 were significantly different from those of the cellobiohydrolase activity. The optimal pH and temperature of the cellobiohydrolase activity of Cbh2124 was 6.4 and 36 °C, and the activity was essentially lost after treatment at 45 °C for 1 h. The optimal pH and temperature of the ß-xylosidase activity of Cbh2124 was 8.0 and 60 °C, and the residual activity was still over 90% after treatment at 80 °C for 6 h. The molecular docking results of the ß-xylosidase activity of Cbh2124 revealed the additional presence of catalytic amino acids Ser175 and Lys420, thus increasing the number of hydrogen bonds involved in the catalytic process, which possibly let to the improved thermostability compared with that of the cellobiohydrolase activity.

Transportable, Endurable, and Recoverable Liquid Metal Powders with Mechanical Sintering Conductivity for Flexible Electronics and Electromagnetic Interference Shielding.

Liquid metals (LMs, e.g., EGaIn) promise a vast potential in accelerating the development of flexible electronics, smart robots, and wearable and biomedical devices. Although a variety of emerging processing methods are reported, they suffer several risks (e.g., leakage, weak adhesion, and low colloidal and chemical stability) because of their excellent fluidity, high surface tension, and rapid oxidation. Herein, liquid metal powders (LMPs) are fabricated based on a versatile method by vigorously stirring EGaIn with nonmetallic or organic particles through interfacial interactions. During the mixing process, EGaIn microdroplets are wrapped with a nonmetallic or an organic shell by electrostatic adsorption, and a more sticky oxide layer is constantly generated and then broken owing to the shearing friction. These transportable powders exhibit superior stability under extreme conditions (e.g., water and high temperature), being capable of recovering electrical conductivity and strong adhesion on different substrates upon mechanical sintering. A flexible, robust, and conductive coating can be constructed via swabbing with an integrated Joule heating effect and excellent electromagnetic interference shielding performances, and it is applicable in flexible wearable electronics, microcircuits, and wireless power transmission systems.

Exploration of Laser Marking Path and Algorithm Based on Intelligent Computing and Internet of Things.

Nowadays, laser processing technology is being used more and more in various fields, and the requirements for laser control procedures are getting higher and higher. This paper aims to study the path generation problem of laser marking technology in order to improve the efficiency of laser marking as well as the protection of the marking material. Therefore, we creatively propose two-path generation methods, namely, sawtooth parallel and contour parallel, and design the boundary curve offset algorithm and domain partition intersection algorithm for the computer simulation of the two marking paths, respectively. Through the simulation, we discussed the efficiency and marking quality of the two path generation methods and gave the conclusion that the efficiency of the sawtooth parallel path generation method is greater than that of the contour parallel path generation method under specific parameters.

An uncharacterized protein from the metagenome with no obvious homology to known lipases shows excellent alkaline lipase properties and potential applications in the detergent industry.

A novel lipase, Lip486, which has no obvious homology with known lipases, was discovered using functional metagenomics technology. Phylogenetic tree analysis suggested that the enzyme belongs to a new subfamily called lipolytic enzyme family II. To explore the enzymatic properties, lip486 was expressed heterologously and efficiently in Escherichia coli. The recombinant enzyme displayed the highest activity on the substrate p-nitrophenyl caprate with a carbon chain length of 10, and its optimum temperature and pH were 53 °C and 8.0, respectively. The recombinant Lip486 showed good activity and stability in strong alkaline and medium-low-temperature environments. The results of compatibility and soaking tests showed that the enzyme had good compatibility with 4 kinds of commercial detergents, and an appropriate soaking time could further improve the enzyme activity. Oil stain removal test results for a cotton cloth indicated that the washing performance of commercial laundry detergent supplemented with Lip486 was further improved. In addition, as one of the smallest lipases found to date, Lip486 also has the advantages of high yield, good stability and easy molecular modification. These characteristics reflect the good application prospects for Lip486 in the detergent and other industries in the future.

3D Printing Method for Tough Multifunctional Particle-Based Double-Network Hydrogels.

3D printing of hydrogels finds widespread applications in biomedicine and engineering. Artificial cartilages and heart valves, tissue regeneration and soft robots, require high mechanical performance of complex structures. Although many tough hydrogels have been developed, complicated synthesis processes hinder their fabrication in 3D printing. Here, a strategy is proposed to formulate hydrogel inks, which can be printed into various strong and tough particle-based double-network (P-DN) hydrogels of arbitrary shapes without any rheological modifiers. These hydrogel inks consist of microgels and a hydrogel precursor. The microgels are individual highly cross-linked networks. They are prepared by swelling dried microparticles in the hydrogel precursor that consists of monomers, initiators, and cross-linkers. Microgels regulate the rheological properties of the hydrogel ink and enable the direct printing. After printing and curing, the precursor forms a sparsely cross-linked network that integrates the microgels, leading to a P-DN hydrogel. The proposed hydrogel inks allow 3D printing of multifunctional hydrogel structures with high mechanical performance and strong adhesion to diverse materials. This strategy will open new avenues to fabricate multifunctional devices in tissue engineering and soft robotics.

Designing AI-Aided Analysis and Prediction Models for Nonprecious Metal Electrocatalyst-Based Proton-Exchange Membrane Fuel Cells.

Traditionally, a larger number of experiments are needed to optimize the performance of the membrane electrode assembly (MEA) in proton-exchange membrane fuel cells (PEMFCs) since it involves complex electrochemical, thermodynamic, and hydrodynamic processes. Herein, we introduce artificial intelligence (AI)-aided models for the first time to determine key parameters for nonprecious metal electrocatalyst-based PEMFCs, thus avoiding unnecessary experiments during MEA development. Among 16 competing algorithms widely applied in the AI field, decision tree and XGBoost showed good accuracy (86.7 % and 91.4 %) in determining key factors for high-performance MEA. Artificial neural network (ANN) shows the best accuracy (R2=0.9621) in terms of predictions of the maximum power density and a decent reproducibility (R2>0.99) on uncharted I-V polarization curves with 26 input features. Hence, machine learning is shown to be an excellent method for improving the efficiency of MEA design and experiments.

Ternary heterogeneous Pt-Ni-Au nanowires with enhanced activity and stability for PEMFCs.

Heterogeneous ternary Pt-Ni-Au nanowires (NWs) with randomly distributed Pt-Ni and Pt-Au micro phases were successfully synthesized following an oriented attachment mechanism. The as-prepared NWs exhibit enhanced activity and durability in both a rotating disk electrode (RDE) and single-cell, originating from the one dimensional (1D) heterogeneous structure.

Characterization and purification via nucleic acid aptamers of a novel esterase from the metagenome of paper mill wastewater sediments.

A new esterase gene est906 was identified from paper mill wastewater sediments via a function-based metagenomic approach. The gene encoded a protein of 331 amino acids, that shared 86% homology with known esterases. Based on the results of multiple sequence alignment and phylogenetic analysis, it was confirmed that Est906 contained a characteristic hexapeptide motif (G-F-S-M-G-G), which classified it as a lipolytic enzyme family V protein. Est906 displayed the highest hydrolysis activity to ρ-nitrophenyl caproate (C6), and its optimal temperature and pH were 54 °C and 9.5, respectively. Additionally, this enzyme had good stability under strong alkaline conditions (pH 10.0-11.0) in addition to moderate heat resistance and good tolerance against several metal ions and organic solvents. Furthermore, a specific nucleic acid aptamer (Apt1) bound to Est906 was obtained after five rounds of magnetic bead SELEX screening. Apt1 displayed high specific recognition and capture ability to Est906. In conclusion, this study not only identified a new esterase of family V with potential industrial application by metagenomic technology but also provided a new method to purify recombinant esterases via nucleic acid aptamers, which will facilitate the isolation and purification of target proteins in the future.

A gate-free monolayer WSe2 pn diode.

Interest in bringing p- and n-type monolayer semiconducting transition metal dichalcogenides (TMD) into contact to form rectifying pn diode has thrived since it is crucial to control the electrical properties in two-dimensional (2D) electronic and optoelectronic devices. Usually this involves vertically stacking different TMDs with pn heterojunction or, laterally manipulating carrier density by gate biasing. Here, by utilizing a locally reversed ferroelectric polarization, we laterally manipulate the carrier density and created a WSe2 pn homojunction on the supporting ferroelectric BiFeO3 substrate. This non-volatile WSe2 pn homojunction is demonstrated with optical and scanning probe methods and scanning photoelectron micro-spectroscopy. A homo-interface is a direct manifestation of our WSe2 pn diode, which can be quantitatively understood as a clear rectifying behavior. The non-volatile confinement of carriers and associated gate-free pn homojunction can be an addition to the 2D electron-photon toolbox and pave the way to develop laterally 2D electronics and photonics.

The Impacts of Attitudes and Engagement on Electronic Word of Mouth (eWOM) of Mobile Sensor Computing Applications.

As one of the latest revolutions in networking technology, social networks allow users to keep connected and exchange information. Driven by the rapid wireless technology development and diffusion of mobile devices, social networks experienced a tremendous change based on mobile sensor computing. More and more mobile sensor network applications have appeared with the emergence of a huge amount of users. Therefore, an in-depth discussion on the human-computer interaction (HCI) issues of mobile sensor computing is required. The target of this study is to extend the discussions on HCI by examining the relationships of users' compound attitudes (i.e., affective attitudes, cognitive attitude), engagement and electronic word of mouth (eWOM) behaviors in the context of mobile sensor computing. A conceptual model is developed, based on which, 313 valid questionnaires are collected. The research discusses the level of impact on the eWOM of mobile sensor computing by considering user-technology issues, including the compound attitude and engagement, which can bring valuable discussions on the HCI of mobile sensor computing in further study. Besides, we find that user engagement plays a mediating role between the user's compound attitudes and eWOM. The research result can also help the mobile sensor computing industry to develop effective strategies and build strong consumer user-product (brand) relationships.