Immense Applications Research Articles

Single-layer MoSeN – a synthetic Janus two-dimensional transition-metal compound grown by plasma-assisted molecular beam epitaxy

Abstract Two-dimensional (2D) nanomaterials hold immense application potentials such as in high-performance nano-electronics, and asymmetric 2D structures with inherent electric dipoles will extend the application promises. Yet synthesizing asymmetric 2D structures remains challenging. Herein, we report the first synthesis of single-layer (SL) hexagonal (H-) phase polar Janus MoSeN via nitrogen-plasma-assisted molecular beam epitaxy. This is a significant achievement given the incommensurate valence between Mo, Se, and N, and the inherent strain from the Janus architecture. Using an array of compositional and structural characterization methods, we establish the atomic configurations of the synthesized MoSeN SL, confirming that they are 2D Janus transition-metal chalcogen-nitrides rather than alloys. By employing density functional theory calculations and transport measurements, we explore the structural feasibility and offer insights into its electronic properties, demonstrating its metallic behavior with ohmic contact characteristics. Piezoresponse force microscopy measurements reveal vertical piezoelectricity and ferroelectric potentials from the Janus MoSeN SL. Therefore, it exhibits great potential for applications in, e.g. piezoelectric and ferroelectric devices, sensing technologies, and optoelectronic devices. This work not only addresses existing challenges in 2D nanomaterial research but also opens new avenues for the development of advanced functional materials.

Human amniotic epithelial stem cells, a potential therapeutic approach for diabetes and its related complications.

The escalating diabetes prevalence has heightened interest in innovative therapeutic strategies for this disease and its complications. Human amniotic epithelial stem cells (HAESCs), originate from the innermost layer of the placenta closest to the fetus and express stem cell markers in the amniotic membrane's umbilical cord attachment area, which have garnered significant attention. This article critically examines emerging research advancements and potential application values of hAESCs in treating diabetes and its complications. Initially, we will discuss the characteristics, origin, and advantages of hAESCs in differentiating into insulin-secreting cells. Subsequently, we will focus on the potential applications of hAESCs in treating diabetes complications such as diabetic retinopathy, diabetic nephropathy, and diabetic neuropathy, etc. We will scrutinize the progress of relevant clinical studies and trials involving hAESC therapy. In conclusion, as an emerging diabetes treatment method, hAESCs exhibit immense potential and application value. Despite numerous challenges in practical application, we are confident that with scientific advancement and technological progress, hAESCs will play a pivotal role in treating diabetes and its related complications.

Comparative analysis of the human microbiome from four different regions of China and machine learning-based geographical inference.

The human microbiome, the community of microorganisms that reside on and inside the human body, is critically important for health and disease. However, it is influenced by various factors and may vary among individuals residing in distinct geographic regions. In this study, 220 samples, consisting of sterile swabs from palmar skin and oral and nasal cavities were collected from Chinese Han individuals living in Shanghai, Chifeng, Kunming, and Urumqi, representing the geographic regions of east, northeast, southwest, and northwest China. The full-length 16S rRNA gene of the microbiota in each sample was sequenced using the PacBio single-molecule real-time sequencing platform, followed by clustering the sequences into operational taxonomic units (OTUs). The analysis revealed significant differences in microbial communities among the four regions. Cutibacterium was the most abundant bacterium in palmar samples from Shanghai and Kunming, Psychrobacter in Chifeng samples, and Psychrobacillus in Urumqi samples. Additionally, Streptococcus and Staphylococcus were the dominant bacteria in the oral and nasal cavities. Individuals from the four regions could be distinguished and predicted based on a model constructed using the random forest algorithm, with the predictive effect of palmar microbiota being better than that of oral and nasal cavities. The prediction accuracy using hypervariable regions (V3-V4 and V4-V5) was comparable with that of using the entire 16S rRNA. Overall, our study highlights the distinctiveness of the human microbiome in individuals living in these four regions. Furthermore, the microbiome can serve as a biomarker for geographic origin inference, which has immense application value in forensic science.IMPORTANCEMicrobial communities in human hosts play a significant role in health and disease, varying in species, quantity, and composition due to factors such as gender, ethnicity, health status, lifestyle, and living environment. The characteristics of microbial composition at various body sites of individuals from different regions remain largely unexplored. This study utilized single-molecule real-time sequencing technology to detect the entire 16S rRNA gene of bacteria residing in the palmar skin, oral, and nasal cavities of Han individuals from four regions in China. The composition and structure of the bacteria at these three body sites were well characterized and found to differ regionally. The results elucidate the differences in bacterial communities colonizing these body sites across different regions and reveal the influence of geographical factors on human bacteria. These findings not only contribute to a deeper understanding of the diversity and geographical distribution of human bacteria but also enrich the microbiome data of the Asian population for further studies.

Synthesis of reusable hierarchical Pore PVDF-MIL-101(Cr) foam for Solid phase extraction of fluoroquinolones from water and its adsorption behavior for anionic and cationic dyes

In this study, a novel hierarchical pore MIL-101(Cr) foam (HPF-MIL-101) was designed and prepared using the sacrificial template method with NaCl as the sacrificial template. This method involved grinding, heating, and washing the NaCl template to produce HPF-MIL-101, with PVDF as the binder and MIL-101(Cr) as the adsorbent. This preparation process is both straightforward and cost-effective, avoiding the use or generation of any organic reagents, thereby offering an environmentally sustainable approach for producing metal-organic framework (MOF) composites. The prepared HPF-MIL-101 exhibited excellent adsorption capabilities for both anionic dye (methyl orange, MO) and cationic dye (methylene blue, MB). The adsorption process followed a pseudo-second-order kinetic model and Friedrich isotherm model, indicating a multilayer adsorption. This is further supported by the Weber-Morris intraparticle diffusion model, which divided the adsorption process into three stages. Furthermore, the adsorption process was consistent with the Freundlich isotherm model, with a correlation coefficient (r) greater than 0.96. HPF-MIL-101 can also be used as an adsorbent for solid phase extraction (SPE). Therefore, an SPE method combined with high-performance liquid chromatography (HPLC) was developed using HPF-MIL-101 as the adsorbent to analyze five fluoroquinolones (FQs) in water samples. This analytical method showed good linearity in the range of 30–2000 ng·mL−1, with excellent linear correlation coefficient (r = 0.9991–0.9999), reasonable extraction recoveries ranging from 80.39 to 112.7 % (RSD ≤ 7.9 %), and low limits of detection (8–30 ng·mL−1). Overall, the results indicated that HPF-MIL-101 not only had a simple, environment-friendly, and pollution-free preparation process but also can be reused for enrichment and detection of trace FQs in water. Thus, HPF-MIL-101 exhibits immense application potential in environmental pollutant removal and also provides a valuable reference for the preparation and application of other MOF composites.

Classification and microbes involved in Plastic biodegradation: A review

Plastic is composed of synthetic and semi-synthetic compounds with immense applications. While Plastics are dangerous and harm the environment, their practice is causing their ingesting to grow. Outdated methods of plastic degradation through physical and chemical means like landfill, incineration, and recycling are expensive and perilous and release hazardous chemical substances to the environment. Thus, proper approaches are required to overcome this trick. Biodegradation through microbes on the other hand offers a hopeful solution. Presently, numerous categories of bacteria and other microbial species degrade plastic materials. Several steps are required in plastic biodegradation like biodeterioration, depolymerization, assimilation and mineralization. These processes will result in secretion of enzymes that degrade plastics and converts it into gasses and biomass along other biproducts.

Study on Intelligent Bionic Superhydrophobic Material and its Oil-Water Separation Mechanism.

Marine oil spills and improper disposal of daily oil usage have posed significant threat to ecological environments and human health due to rapid industrial development. In this study, an environmentally friendly, simple process and high-performance Fe3O4@SiO2@Polymethyl methacrylate (PMMA)-based smart bionic superhydrophobic oil-absorbing material was developed for effectively collecting and removing oil pollutants from water. By studying the effects of Fe3O4 particle size, polydimethylsiloxane (PDMS) concentration, and heating time on the superhydrophobicity of the materials, the directional regulation of superhydrophobicity and oil-water separation performance of Fe3O4@SiO2@PMMA@PDMS materials was realized. The results showed that the material exhibited optimal performance when the Fe3O4 particle size combination was 20/500 nm/1 μm, the mass ratio of PDMS to Fe3O4@SiO2@PMMA was 7 : 1, and it was heated at 350°C for 1 minute. The coating achieved an apparent contact angle (APCA) of 158.7° and a rolling angle as low as 4.9°. This coating not only remained superhydrophobic after a 21 m abrasion test and 288 h immersion in acid, alkali, salt, and high-temperature solutions, but also efficiently separated oil-water mixtures and water-in-oil emulsions, and the separation efficiency for oil-water mixtures of trichloromethane, dichloromethane and bromomethane was over 99.78 %, and that for water-in-oil emulsions was over 98.34 %. Furthermore, the superhydrophobic magnetic polyurethane (SFPU) sponge prepared using Fe3O4@SiO2@PMMA not only exhibited excellent oil-absorbing capacity (11-28 g/g), but also realized precise oil absorption at multiple sites by magnetic conduction. In the actual oily wastewater test, the oil-water separation efficiency of the sponge reached 90.58 % and the oil absorption capacity reached 17.03 g/g. This efficient oil-water separation performance as well as oil adsorption capacity comes from the fact that the nonpolar molecules (e. g., -CH3) generated by the hydrolysis of PDMS can produce van der Waals adsorption with oil substances, while the excellent micro-nanostructure of the coating surface greatly increases the contact area between oil droplets and the coating, which can make them adsorb or pass through quickly. This multifunctional coating and sponge had immense application potential in fields like offshore oil spill treatment, organic pollution control in water bodies.

Electrochemical transformation of 2D materials to their quantum dots

The term two-dimensional (2D) material refers to a single layer of monatomic units or molecules that reveals distinct electrical and optical properties and has received much attention recently due to its immense application potential. For example, graphene as a monolayer has captured intense efforts during the past decade, and other 2D materials like transition metal dichalcogenides, phosphorene, borophene, bismuthene, and stanene have also evolved for various applications such as nanoelectronics, hydrogen storage, supercapacitors, and solar cells. More recently, their heterostructures including Janus layers have also emerged with several exceptional electronic properties. Although there are several ways of synthesizing quantum dots of these exciting materials, electrochemical methods are especially relevant for preparing 2D materials (often in a size-controlled manner) from suitable precursors. More importantly, hetero-atom doping could also be carried out at room temperature when these materials are prepared by applying electric field without any major change in the morphology or size distribution after doping. With this disposition, we summarize the essential experimental methodology and a few mechanistic insights for the electrochemical synthesis of quantum dots from different 2D materials. This topic has not been discussed unambiguously in the past, lacking the proper motivation to emphasize the importance of controlling the electric field, substrate electrodes, precursors, and the role of counter ions during the synthesis. In this review, we concisely discuss the synthesis of such 0D materials by electrochemical methods, the mechanism, advantages, and limitations in comparison with other methods, along with the benefits ensued for a few selected applications. The genre of this category of work has always been intriguing despite the fact that only a few other groups are involved in the synthetic methodology, making the topic an everchanging field of exciting applications ranging from flexible power sources for wearable electronics to green electrocatalysts for sustainability and nano-sensors for biological applications.

Synergistic modification of Ti/PbO2 electrodes with metal ions (Nd, Al, Bi) and STAB surfactant for electrocatalytic treatment of desulfurization wastewater

In this study, PbO2 electrodes are modified by co-doping with different metallic elements (Nd, Al, Bi) and stearyl trimethyl ammonium bromide (STAB) using the electrodeposition method. The modified electrodes, designated as Ti/PbO2-Nd-STAB, Ti/PbO2-Al-STAB, and Ti/PbO2-Bi-STAB, are then applied in the electrochemical degradation of desulfurization wastewater. The surface morphology, crystal structure, and elemental valence of the prepared electrodes are analyzed using scanning electron microscopy (SEM), X-ray diffraction (XRD), and X-ray photoelectron spectroscopy (XPS). Their electrochemical performance and stability are assessed by linear sweep voltammetry (LSV), cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS), and accelerated life testing (ALT). The results reveal that modification with metallic elements and STAB significantly enhances the physicochemical and electrocatalytic performance of the Ti/PbO2 electrode. Among the modified electrodes, the Ti/PbO2-Nd-STAB electrode exhibits the most compact and homogeneous surface morphology, the highest oxygen evolution potential (OEP, 2.33 V vs. SCE), the lowest chlorine evolution potential (CEP, 1.44 V vs. SCE), the largest electrochemically active surface area, the lowest charge transfer resistance, the highest electrochemical stability, and a prolonged accelerated life (65 h, which is 17 h longer than that of the unmodified electrode). Further, the optimal electrochemical oxidation process parameters for desulfurization wastewater are determined as follows: current density = 50 mA/cm2, temperature = 35 ℃, electrode spacing = 3.0 cm, and stirring rate = 300 rpm. After 300 min of electrolysis, the Ti/PbO2-Nd-STAB electrode achieves a Cl− removal rate of 86.9 % and a chemical oxygen demand (COD) removal rate of 71.2 %. After 10 cycles, the Cl− and COD removal rates decrease by 2.8 % and 1.8 %, respectively. Besides, Tafel curve analysis indicates that the chlorine evolution reaction follows the Volmer-Tafel mechanism, with the recombination of adsorbed Cl− ions on the surface oxidation film being the rate-determining step. Overall, the Ti/PbO2-Nd-STAB anode exhibits immense application potential in the treatment of desulfurization wastewater.

Cleaved Amplified Polymorphic Sequence Markers in Horticultural Crops: Current Status and Future Perspectives

DNA markers have broad applications, including marker-assisted selection (MAS) for breeding new cultivars. Currently, single nucleotide polymorphisms (SNPs) have become a preferred choice of markers for molecular geneticists and breeders. They offer many advantages, such as high abundance and coverage in the genome, codominant inheritance, locus specificity, and flexibility for high-throughput genotyping/detection formats, and they are relatively inexpensive. The availability of reference genome sequences enables precise identification of candidate genes and SNPs associated with a trait of interest through quantitative trait loci (QTL) mapping and genome-wide association studies (GWAS). Such SNPs can be converted into markers for their application in MAS in crop breeding programs. Cleaved amplified polymorphic sequence (CAPS) markers amplify short genomic sequences around the polymorphic endonuclease restriction site. This review provides insight into the recent advancements made in the development and application of CAPS markers in several horticultural plants. We discussed many new tools that aid faster and more accurate design of CAPS markers from the whole genome resequencing (WGRS) data. The developed CAPS markers offer immense application in germplasm screening and field trials, genomic loci mapping, identifying candidate genes, and MAS of important horticultural traits such as disease resistance, fruit quality and morphology, and genetic purity.

Bio-inspired Design and Performance Evaluation of Lawnmower Cutter Blade

This study presents a bio-inspired design approach for the engineering applications of agricultural implements, with practical implications for the field. The CAD modeling of bio-inspired serrated lawnmower cutter blades was followed by structural and fatigue analysis with a comparative evaluation of the performance of conventional plain cutter blades. This method involved the bionic transformation of a biological model of the serrated leg of the grasshopper, traced using MATLAB edge detection and image processing software. The solid modeling CAD software for the Bionic Blade lawnmower cutter blade converts this biological model into an engineering model. After designing the geometric shape of the bionic tooth profile's serrated cutting edge, the boundary conditions are evaluated for numerical simulation based on the machine specifications of the conventional lawnmower cutter. After analyzing the power of the motor, the rotational speed, and the torque, the static structural and fatigue analysis was carried out in numerical simulation software to synthesize the performance of the bionic lawnmower cutter blade for engineering analysis of deformation, shear stress, and fatigue life. The bionic design of the blade and conventional plain blades were manufactured using an Amada CO2 laser cutting machine and sheet metal bending processes for Stainless Steel (SS 304) material. The experimental validation of software simulation was carried out by field trials using the Bionic design of a lawnmower cutter blade to evaluate the performance improvement in cutting time and yield of grass cut in kg for 60 to 100 meters distance cutting. After comparing the performance of the plain blade and the design of the bionic blade, there was a reduction in abrasive wear, a 12.87% improvement in the cutting time, and a 14.8% increase in the yield of grass compared to the conventional blade. This performance improvement was attributed to the unique bionic design of serrated cutting-edge and ground clearance. The bio-inspired design approach has immense practical applications in designing agricultural implements, as confirmed by this performance evaluation of this case study of bio-inspired lawnmower cutter blades.

Entropy generation in Casson nanofluid flow over a lubricated wall in the presence of induced magnetic field and activation energy

ABSTRACT The research on entropy generation in a mechanical system is quite imperative owing to its immense applications in the production of advanced cooling devices and medical appliances. The basic need on this research is how to optimize the irreversibilities within the systems and channelize that energy towards the benefit. This study elucidates the entropy generation analysis of a Casson nanofluid near a stagnation point over a lubricated vertical wall. A thin film of shear-thinning liquid with power law index of 0.5 is employed on the wall to ensure the lubrication. The effect of induced magnetic field and the chemical reaction stimulated by the activation energy is also incorporated within the flow. The non-linear PDEs of the substantive problem along with the compatible interfacial and boundary conditions are reverted into ODEs with the aid of suitable similarity transformation, and then cracked numerically using the sixth-order Runge Kutta method assisted with Nachtsheim–Swigert shooting practice. One of the significant findings reveals that the fluid friction irreversibilities in the entropy generation are diminished with the magnetic parameter and the Casson parameter, whereas the thermal irreversibility predominates over the total entropy generation for the escalating values of the activation energy parameter.

Development of optimized ensemble machine learning-based prediction models for wire electrical discharge machining processes

This paper proposes development of optimized heterogeneous ensemble models for prediction of responses based on given sets of input parameters for wire electrical discharge machining (WEDM) processes, which have found immense applications in many of the present-day manufacturing industries because of their ability to generate complicated 2D and 3D profiles on hard-to-machine engineering materials. These ensembles are developed combining predictions of the three base models, i.e. random forest, support vector machine and ridge regression. These three base models are first framed utilizing the training datasets, providing predictions for all the responses under consideration. Based on these predictions, two optimization problems are formulated for each of the responses, while minimizing root mean squared error and mean absolute error, for subsequent development of two optimized ensembles whose predictions are the weighted sum of the predictions of the base models. The prediction performance of all the five models is ascertained through nine statistical metrics, after which a cumulative quality loss-based multi-response signal-to-noise (MRSN) ratio for each model is computed, for each of the responses, where a higher MRSN ratio indicates greater accuracy in prediction. This study is conducted using two experimental datasets of WEDM process. Overall, the optimized ensemble models having higher MRSN ratios than the base models are indicated to deliver better prediction accuracy.

Step-by-Step Tuning of Tribological and Anticorrosion Performance of Zinc Phosphate Conversion Coatings through Effective Integration of Spherical P-Doped MoS2 Nanoparticles.

Surface coatings with enhanced mechanical stability, improved tribological performance, and superior anticorrosion performance find immense application in various industrial sectors. Herein, we report the development of multifunctional composite zinc phosphate coatings by the effective integration of a structurally and morphologically tuned P-doped MoS2 nanoparticle additive (3P-MoS2) into the zinc phosphate matrix to offer attractive characteristics suitable for industrial applications. The integration of spherical nanoparticles as additive leads to the formation of homogeneous and compact coatings with a densely packed crystalline microstructure having enhanced microhardness, distinctive leaf-like morphology, and comparatively smooth topographical features. The attractive lubricity of the additive (3P-MoS2), coupled with its spherical morphology, facilitates a transition from sliding to rolling friction and contributes significantly toward the performance enhancement of the tuned composition of the composite zinc phosphate coating (0.3-PMS). Thus, the tuned 0.3-PMS coating delivers the lowest specific wear rate (1.334 × 10-5 mm3/Nm) and coefficient of friction (0.114) that significantly outperform bare-zinc phosphate coating. Further, the electrochemical corrosion study results indicate the improvement in corrosion resistance behavior of the composite zinc phosphate coatings with reduced corrosion current density (icorr) and charge transfer resistance (Rct) values, as compared to the bare-zinc phosphate coating. The effect of passivation in conjunction with the barrier protection characteristics of the composite coatings induced by the optimal composition of the integrated additive nanoparticles (3P-MoS2) can efficiently prevent the infiltration of corrosive ions and thereby significantly reduce the rate of corrosion.

Design and Synthesis of Sulfur-Containing Tetradentate Ligands for Ir-Catalyzed Asymmetric Hydrogenation of Ketones.

Developing highly active and enantioselective ligands for the asymmetric hydrogenation of ketones has consistently attracted significant attention from scientists. A series of novel tetradentate sulfur-containing ligands, termed as f-thiophamidol, were successfully designed and synthesized which exhibited excellent performance in the asymmetric hydrogenation of simple ketones (up to 99% yield, 99% ee) and α-substituted β-keto sulfonamides (up to 99% yield, 99% ee, 99:1 dr). The subsequent successful gram-scale experiments with high TON demonstrated the immense potential application value of this system in synthesizing drug molecules.

Signal processing for miniature mass spectrometer based on LSTM-EEMD feature digging

Miniature mass spectrometers exhibit immense application potential in on-site detection due to their small size and low cost. However, their detection accuracy is severely affected by factors such as sample pre-processing and environmental conditions. In this study, we propose a data processing method based on long short-term memory-ensemble empirical mode decomposition (LSTM-EEMD) to improve the quality of on-site detection data from miniature mass spectrometers. The EEMD method can clearly decompose the different physical feature components in the small-scale spectrometer signals, while the LSTM method can adaptively learn the internal feature relationships of the signals. Thus, by combining the two, the parameters for the EEMD signal reconstruction can be optimized in an adaptive manner, obtaining the optimized coefficients. Compared to the previous EEMD feature enhancement approach, the LSTM-EEMD method not only significantly improves the coefficient of determination (R2) and relative standard deviation (RSD) of the data, enhancing the linear range, but also achieves fully adaptive processing throughout the workflow, greatly boosting the efficiency. By leveraging a miniature mass spectrometer, data for N-acetyl-l-aspartic acid (NAA), 2-Hydroxyglutarate (2-HG), and γ-Aminobutyric acid (GABA) in actual blood samples have been obtained. The experimental results demonstrate that the LSTM-EEMD method can markedly enhance the accuracy and usability of the biological sample data in practical testing, providing new perspectives and possibilities for research and applications in the relevant domain.

A novel diglycosidase for the transformation of naringin to naringenin and neohesperidose

A novel fungal diglycosidase that transforms naringin into naringenin and neohesperidose, a rare biotransformation, has been purified to homogeneity using a simple procedure involving precipitation of the enzyme from the culture filtrate of the fungal strain using 80 % saturation of ammonium sulphate, dissolving the precipitate in minimum volume of the buffer and dialysing that against the buffer. The purified enzyme gives single protein bands of molecular mass 64.6 kDa in SDS-PAGE analysis. The purity of the enzyme has been further confirmed by the appearance of single protein band in native page analysis. Using naringin as the substrate, the diglycosidase has Km and kcat values of 0.20 m mol L−1 and 0.66 s−1, respectively, at pH 4.0 and 313 K. The specific activity of the purified enzyme using naringin as the natural substrate is 1.018 katal/kg. The diglycosidase also transforms rutin into quercetin and rutinose but has no effect on hesperidin. The feasibilities of preparing neohesperidose from naringin and rutinose from rutin on milligram scales using the pure enzyme have been demonstrated. These results open the way for developing an enzymatic process for preparation of neohesperidose from naringin. The reported diglycosidase has immense future applications in food and pharmaceutical industries.

Advances in Microbial Exopolysaccharides: Present and Future Applications.

Microbial exopolysaccharides (EPSs) are receiving growing interest today, owing to their diversity in chemical structure and source, multiple functions, and immense potential applications in many food and non-food industries. Their health-promoting benefits for humans deserve particular attention because of their various biological activities and physiological functions. The aim of this paper is to provide a comprehensive review of microbial EPSs, covering (1) their chemical and biochemical diversity, including composition, biosynthesis, and bacterial sources belonging mainly to lactic acid bacteria (LAB) or probiotics; (2) their technological and analytical aspects, especially their production mode and characterization; (3) their biological and physiological aspects based on their activities and functions; and (4) their current and future uses in medical and pharmaceutical fields, particularly for their prebiotic, anticancer, and immunobiotic properties, as well as their applications in other industrial and agricultural sectors.

Power dependent tunable optical nonlinearity in Iron oxide/Borophene core-shell nanoparticles under ultrashort laser excitation

Two-dimensional (2D) nanomaterials find immense applications in ultrafast photonics and optical switching systems due to their fascinating nonlinear optical (NLO) characteristics. This work presents a systematic investigation of laser power-dependent nonlinear optical characteristics of borophene nanosheets in a core-shell nanostructure. For this purpose, we have synthesized Borophene encapsulated Iron oxide (Fe3O4) core-shell nanoparticles (Fe3O4/Borophene CSNPs). The CSNPs were synthesized by mixing and annealing sodium borohydride (NaBH4) as a boron precursor with iron oxide nanoparticles (Fe3O4 NPs) in a controlled environment using chemical vapor deposition technique (CVD) followed by characterization using XRD, FTIR Spectroscopy, UV–vis absorption spectroscopy, TEM, SEM, and EDX techniques. Magnetic properties were analyzed by a vibrating sample magnetometer (VSM) which shows that the saturation magnetization of Fe3O4 NPs increases after being encapsulated in borophene nanosheets. The role of borophene in modifying the NLO characteristics of Fe3O4/Borophene CSNPs was studied using a power-dependent Z-scan technique utilizing ultrashort (femtosecond) laser pulses at a wavelength of 800 nm in both open and close aperture configuration. It was found that borophene-coated Fe3O4 NPs reveal tunable NLO properties that resemble the NLO characteristics of borophene nanosheets with enhanced features. This provides insights into an interaction between materials in NLO phenomena and illustrates the distinctive performance of the core-shell nanostructure. The tunable NLO properties of Fe3O4/Borophene CSNPs offer an innovative approach for developing optoelectronics and ultrafast nonlinear photonic devices.

Room-temperature self-healing polyurethane with superior mechanical properties based on supramolecular structure for flame retardant application

Self-healing polymers, with their ability to extend service life and conserve resources, have garnered significant attention across various fields due to their immense potential applications. However, most repairable polymers typically require high temperatures for repair, and their relatively weak mechanical properties limit their practical use. Moreover, the flammability of polymers not only poses safety hazards during use but also fails to meet specific requirements for flame retardancy in certain fields. Addressing these issues, this study successfully designed a reactive self-extinguishing room-temperature self-healing polyurethane (PU) by introducing hyperbranched catechol (DBPA) onto the PU main chain. The prepared self-healing PU elastomer (DPU) exhibited high tensile strength (∼51.2 MPa), outstanding toughness (∼119.0 MJ/m3), recyclability, and good flame retardancy. Through intermolecular and intramolecular multilevel hydrogen bonding, the prepared DPU0.6 achieved room-temperature self-healing with an efficiency of approximately 71.2 %. The results showed that the addition of trace amounts of DBPA (0.6 wt%) significantly enhanced the flame retardancy and thermal stability of DPU. Compared to DPU0, the heat release rate (HRR) of DPU0.6 decreased by approximately 13 %, the total heat release (THR) reduced by 18 %, and the time to ignition (TTI) was extended by 7 s (reduced by ∼ 21 %). This study not only provides insights into the synthesis and design of novel self-healing PU but also further expands the application scope of self-healing PU.

Deep learning permits imaging of multiple structures with the same fluorophores

Fluorescence microscopy, which employs fluorescent tags to label and observe cellular structures and their dynamics, is a powerful tool for life sciences. However, due to the spectral overlap between different dyes, a limited number of structures can be separately labeled and imaged for live-cell applications. In addition, the conventional sequential channel imaging procedure is quite time consuming, as it needs to switch either different lasers or filters. Here, we propose a novel double-structure network (DBSN) that consists of multiple connected models, which can extract six distinct subcellular structures from three raw images with only two separate fluorescent labels. DBSN combines the intensity-balance model to compensate for uneven fluorescent labels for different structures and the structure-separation model to extract multiple different structures with the same fluorescent labels. Therefore, DBSN breaks the bottleneck of the existing technologies and holds immense potential applications in the field of cell biology.