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In this study, we present a microstructural and mechanical analysis of Ti–6Al–4V alloys processed by low-temperature multi-directional forging at 900 °C, achieving near-theoretical density (99.9%), with a tensile strength of 1070 MPa and elongation of 14.9%. The microstructure is defined by both continuous and discontinuous dynamic recrystallization occurring simultaneously, related to an increase in dislocation density. Significantly, this study reveals the interaction between crystallographic orientation and spheroidization during the forging process. Specifically, changes in crystallographic orientation include the asynchronous formation of new subgrains/grains, the disruption of the Burgers orientation relationship, and rotation around the axis, which not only increases the orientation differences between α lamellae but also enhances the spheroidization process. Moreover, our findings demonstrate that during deformation, the activation of basal and pyramidal slip systems is favored over prismatic slip, indicating that the forging process has enhanced slip activity. The average misorientation angle and the fraction of high-angle grain boundaries are quantified, providing a detailed characterization of the evolved microstructure. This study adopts a new perspective to examine the microstructural evolution during the multidirectional forging process in powder metallurgy, presenting findings that elucidate how multidirectional forging effectively controls.

To overcome the disadvantage of the Arrhenius constitutive (AC) equation in predicting complex nonlinear flow behaviors, this work employed an artificial neural network (ANN) to introduce a proportional coefficient reflecting changes in softening mechanisms under various hot deformation conditions, thereby enhancing the applicability of the AC model. The result demonstrates that, compared to the AC model, the ANN-AC model effectively compensates for errors resulting from different softening mechanisms during hot deformation, with RMSE and MAPE decreasing by 62.47% and 63.75%, respectively. Additionally, the microstructure aligns with the evolution of the power dissipation factor (η) predicted by the ANN-AC model, indicating the map's accuracy. Microstructure analysis shows that discontinuous dynamic recrystallization (DDRX) grains nucleate adjacent to the original grain boundaries at low temperatures and high strain rates, while continuous dynamic recrystallization (CDRX) grains primarily form within original grains at high temperatures and low strain rates. The results reveal the effect of grain orientation on DRX, with //CD (compression direction) oriented grains capable of activating multiple slip systems, indicating a propensity for the CDRX mechanism, whereas //CD oriented grains, with fewer slip systems, promote DDRX through coordinated deformation via grain boundary motion.

Multistage thermomechanical treatment experiments were conducted on Cu–Ni–Co–Si–Mg alloy strip prepared by the Heating Cooling Combined Mold (HCCM) horizontal continuous casting-cold rolling process. Effects of various heat treatments on the microstructure, mechanical properties, and electrical conductivity of the alloy were studied. The strengthening mechanisms and factors influencing the performance of each process has been analyzed. The tensile strength, yield strength, and electrical conductivity of Cu–Ni–Co–Si–Mg alloy can reach 894 MPa, 855 MPa, and 47.1 %IACS after a third-stage of thermomechanical treatment (TTMT). The cold rolling in first-stage of thermomechanical treatment (FTMT) not only caused work hardening of the alloy but also promoted the precipitation of solute atoms and the growth of second phase particles, limiting the increase in strength. Second-stage of thermomechanical treatment (STMT) effectively hindered the excessive growth of second phase particles in FTMT but shortened recrystallization process. The superior performance of Cu–Ni–Co–Si–Mg alloy after TTMT was attributed to dispersive precipitation of Ni, Co, and Si elements as (Ni, Co) 2Si phases in the copper matrix, strengthening the interaction between the precipitates and dislocations. A short process of HCCM horizontal continuous casting-cold rolling-thermomechanical treatment was developed to produce a high strength and highly electrically conductive Cu–Ni–Co–Si alloy strip used in lead frames.

Significant improvement of bending properties (approximately 64.7%) for WE43 magnesium alloy was realized by weakening the basal texture and raising the deformation temperature. The mechanisms for such improvement were investigated in detail by combining the experiment and finite element model. The results concluded that the coupling effects of weak basal texture and high deformation temperature was the key reason producing the above improvement of bending properties. Specially, the TD sample with weak basal texture was more conducive to activating basal slip than the RD sample, resulting in its higher strain hardenability and consequently more significant bending properties. Based on weak basal texture, raising the deformation temperature could further improve the bending properties. This improvement mechanism was attributed to the abundant activations of non-basal slip instead of basal slip, which could increase the independent slip systems and efficiently delay the development of the strong basal texture. Moreover, the occurrence of partial DRV and DRX relieved the stress concentrations by consuming the dislocations, further improving the bending properties. The activations of these deformation mechanisms matching the corresponding bending stress states caused the crystal rotation and the texture variety in the bending samples. Basal slip activation aligned the (0002) basal planes parallel to tensile stress in the outer region or perpendicular to compressive stress in the inner region. Additionally, prismatic slip activation induced crystal rotation around the c-axis, developing //LD texture in the outer region but ⊥LD texture in the inner region.

Polyepoxysuccinic acid (PESA) was modified by glycidyl (Gly) to obtain a phosphate-free and nitrogen-free polymer (Gly-PESA), and its scale and corrosion inhibition properties were studied.The effects of Gly-PESA concentration, calcium ion concentration and temperature to scale inhibition performance were studied by static scale inhibition method. The corrosion inhibition performance of Gly-PESA was studied by static weight-loss method. The morphology and structure of calcium scale and steel surface were characterized by spectroscopic and microscopic methods. The scale inhibition efficiency of Gly-PESA for CaCO3 and CaSO4 was 98.06% and 92.6%, respectively. Under the same experimental conditions, the scale inhibition effect of Gly-PESA was obviously better than that of PESA. Gly-PESA could effectively inhibit the growth of CaCO3 and CaSO4 crystals and cause their crystal lattice distortion or crystal dispersion. The corrosion inhibition efficiency of Gly-PESA for carbon steel was 78.17%. The adsorption of Gly-PESA on the surface of the steel test piece followed Langmuir adsorption isotherm, and formed a protective film, which increased the corrosion resistance of the steel test piece, thus effectively protecting the steel test piece. Therefore, Gly-PESA was a green scale and corrosion inhibitor, which solved the problem of environmental pollution.

Limitations on data volume and quality are key bottlenecks in using machine learning for property prediction and property-oriented composition design of non-oriented silicon steel. In response, this study employed a Gaussian Mixture Model (GMM) to generate virtual samples for enhancing the in-house experimental data, by which the generated virtual data well captured the distribution of the raw experimental data. As a result, compared with the model without data augmentation, the enhanced prediction model (composition → property) fitted by virtual samples improved its R2 value from 0.52 to 0.86. Based on this model, a multi-properties-oriented (yield strength σs, magnetic induction B50, and iron loss P10/400) composition prediction model (property → composition) was established to rapidly discover high-performance non-oriented silicon steel. Experimental characterization and theory calculation of the explored candidate alloys exhibited that their yield strength was above 750 MPa, which primarily results from solid solution strengthening (50 %) and precipitation strengthening (36 %). Moreover, the alloys possessed magnetic induction B50 of over 1.72 T and low core losses with P10/400 of 12.1 W/kg (0.2 mm) and 17.0 W/kg (0.35 mm). Further microstructural characterization exhibited that such satisfactory performance is associated with copper (Cu)-rich nanoprecipitates that dramatically improved the yield strength without deteriorating the magnetic properties.

Overstaffing production in underground coal mining is not convenient for daily management, and incomplete information of coal miners hinders the rescue process of firefighters during mine accidents. To address this safety sustainability issue, a novel face recognition method based on an improved multiscale neural network is proposed in this paper. A new depthwise separable (DS)-inception block is designed and a joint supervised loss function based on center loss theory is developed to construct a new multiscale model. The miniers can be recognized in the harsh underground environment during the life rescue. Experimental results show that the accuracy, recall and F1-score indexes of the proposed method for the miner face recognition in the underground mining environment are 97.26%, 94.17% and 95.42%, respectively. Transfer model with joint supervised loss can effectively improve the recognition accuracy by about 0.5–1.5%. In addition, the average recognition accuracy of the proposed face recognition method achieves to 91.34% and the miss detection rate is less than 5% in the dugout tunnel of coal mine.

For high-performance metal matrix composites, maintaining the uniform distribution of the small-sized reinforcement phase in the matrix is the key to fully exploiting the advantages of the reinforcement phase, but it is also a technical difficulty. In this work, a new type of reinforcement phase—SiO2 aerogel particles, was selected to be added into the Cu matrix. Utilizing the characteristics of easy dispersion of the nanoporous structure in its original state and then densification under a high temperature load, its uniform distribution in the Cu matrix can be achieved by a simple powder metallurgy method. The composites have excellent electrical conductivity and high strength, which is mainly attributed to the unique distribution of silica aerogel particles: larger particles are distributed on the grain boundary, and smaller particles are distributed in the grain, which can greatly refine the grain. Quantifying the contribution of various strengthening mechanisms to yield strength increases, the results revealed that the grain refinement strengthening mechanism plays a leading role. Moreover, this work provides a new strategy for preparing high-performance copper matrix composites.

A belt conveyor system is one of the essential equipment in coal mining. The damages to conveyor belts are hazardous because they would affect the stable operation of a belt conveyor system whilst impairing the coal mining efficiency. To address these problems, a novel conveyor belt damage detection method based on CenterNet is proposed in this paper. The fusion of feature-wise and response-wise knowledge distillation is proposed, which balances the performance and size of the proposed deep neural network. The Fused Channel-Spatial Attention is proposed to compress the latent feature maps efficiently, and the Kullback-Leibler divergence is introduced to minimize the distribution distance between student and teacher networks. Experimental results show that the proposed lightweight object detection model reaches 92.53% mAP and 65.8 FPS. The proposed belt damage detection system can detect conveyor belt damages efficiently and accurately, which indicates its high potential to deploy on end devices.

Cu-1.3 wt%Ni-1.0 wt%Co-0.9 wt%Si-0.1 wt%Mg alloy strip with 10 mm in thickness produced by heating-cooling combined mold (HCCM) horizontal continuous casting was cold rolled. Microstructure evolution and mechanical properties of the cold-rolled alloy strips with different reduction were studied, and the deformation mechanism was clarified. The results showed that the continuous casting strips had axial columnar grain with main orientations and straight small angle grain boundaries, the elongation after fracture was up to 25.0%, which can be directly processed by large deformation cold rolling, and the maximum cumulative cold deformation was up to 99%.When the reduction was 40% and 60%, the dislocation bands crossed each other and shear bands formed, and the alternating distribution of soft/hard oriented grains were conducive to improve the deformation coordination ability of micro-region under high strain. When the deformation exceeded 80%, deformation twins formed and the twins had strong interaction with the shear band and grain boundaries, resulting in strong shear and rotation of the local grains. The tensile strength and hardness increased from 464 MPa to 137HV of the as-cast strip to 629 MPa and 208HV with reduction 90%, respectively, while the elongation decreased from 25.0% to 4.1%. These results can lay a foundation for the development of compact method for producing high performance Cu–Ni–Co–Si alloy strips.

Summary: The multi-scale modeling of lithium-ion battery (LIB) is difficult and necessary due to its complexity. However, it is difficult to capture the aging behavior of batteries, and the coupling mechanism between multiple scales is still incomplete. In this paper, a simplified electrochemical model (SEM) and a kinetic Monte Carlo (KMC)-based solid electrolyte interphase (SEI) film growth model are used to study the multi-scale characteristics of LIBs. The single-particle SEM (SP-SEM) is described for macro scale, and a simple and self-consistent multi-particle SEM (MP-SEM) is developed. Then, the KMC-based SEI model is established for micro-scale molecular evolution. And, the two models are coupled to construct the full-cycle multi-scale model. After modeling, validation is performed by using a commercial 18650-type LIB. Finally, the effect of parameters on the SEI model is studied, including qualitative trend analysis and quantitative sensitivity analysis. The growth of SEI film with different particle sizes is studied by MP-SEM coupling simulation.

The use of environmentally friendly corrosion inhibitors (CIs) is an effective way to overcome the environmental problems caused by existing CIs that contain nitrogen or phosphorus components. Plant extracts, as a type of environmentally friendly CI, have attracted considerable attention. In this work, the corrosion inhibition properties of spinach extract (AESPE) on Q235 steel were studied in a hydrochloric acid medium. First, AESPE was extracted from spinach by an ethanol-acetone reflux method and its main structural and stability parameters in hydrochloric acid medium were determined by infrared spectroscopy (FTIR) and ultraviolet spectroscopy (UV–vis). Second, the corrosion inhibition properties of AESPE on Q235 steel in a 0.5 M hydrochloric acid solution were studied by weight loss (WTL), dynamic potential polarisation (PDP), linear polarisation (LPR), electrochemical impedance spectroscopy (EIS) and quantum chemical calculation methods. The results showed that AESPE could significantly slow the corrosion rate (Vcorr) of Q235 steel in 0.5 M hydrochloric acid medium, which was via a mixed corrosion inhibition mechanism at the cathode and anode. As the AESPE concentration increased, the cathode and anode currents decreased and the active corrosion sites were blocked; thus, the Vcorr decreased and the corrosion inhibition efficiency (E (%)) increased. Under the conditions of 303 K and 300 mg⋅L–1 AESPE, the E (%) of AESPE for Q235 steel was>90%. As the system temperature increased, the E% of AESPE for Q235 steel decreased but the extent of this decrease was small. This work shows that AESPE is an environmentally friendly CI with a good corrosion inhibition effect.

Cadmium (Cd) is one of the dominant metal pollutants present in the aquatic environment that affects ion homeostasis, oxidative stress (OS) and immune responses of aquatic organisms. Given the physicochemical similarities between Cd2+ and calcium (Ca2+) ions, their antagonism may facilitate the mitigation of Cd-induced toxicity. To better understand the role of Ca in protecting against Cd-induced toxicity in teleosts, juvenile grass carp were exposed to Cd (measured concentration 3 μg/L) and a gradient of Ca concentrations (measured concentration 1.5 mg/L, 2.5 mg/L, 3.0 mg/L, and 3.5 mg/L in the control (CTL) group, low calcium (LCA) group, medium calcium (MCA) group, and high calcium (HCA) group, respectively) for 30 days. Inductively coupled plasma mass spectrometry (ICP-MS) data analyses showed that simultaneous exposure to Ca impaired the accumulation of Cd in all tested tissues. Besides, Ca addition maintained the plasma ion (Na+, K+, Cl-) homeostasis, alleviated Cd-induced oxidative stress (OS), and regulated the activities and transcriptional levels of ATPase. Furthermore, transcriptional heatmap analysis demonstrated that several indicator genes for OS and calcium signaling pathway were found to be significantly modulated by Ca addition. This work delineates a protective effect of Ca against Cd-induced toxicity in grass carp, providing new insight into the possible solutions to Cd pollution issues in aquaculture industry.

In this work, in-situ TiB2/AlSi7Mg composites were successfully fabricated using TiB2 decorated AlSi7Mg alloy powder synthesized via salt-metal reaction and gas atomization, followed by powder metallurgy and hot extrusion. Microstructure and mechanical properties were investigated by optical microscopy (OM), X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscope (TEM) and tensile tests. The results show that the formation of fine grains and homogeneous subspherical silicon particles in as-extruded composites with high ductility (elongation of 21.7%) is achieved by perfect metallurgical bonding during severe plastic deformation. The covering effect of TiB2 aggregates around silicon particles as particle shells has hindered the dissolution of silicon in the composite. After T6 treatment, the ultimate strength, yield strength and elongation at fracture of the 2 wt% TiB2/AlSi7Mg composite reach up to 344 MPa, 298 MPa and 8.6%, respectively. The excellent mechanical properties are mainly attributed to grain refinement, silicon optimization and precipitation strengthening. This study provides a good solution for improving the strength and toughness of Al–Si alloy.

In this work, the development of the eco-friendly comprehensive scale and corrosion inhibitor based on green polyaspartic acid (PASP) was presented. In this view, PASPG was prepared by a ring-opening graft modification reaction of polysuccinimide (PSI) with glycidyl. In addition, the molecular structure and the thermal stability of PASPG were characterized by using three different methods (FTIR, 1H NMR, and TGA). PASPG’s scale inhibition efficiency and corrosion inhibition efficiency were also evaluated, respectively. More concretely, the scale inhibition efficiency of PASPG achieved 94.6 % and 95.1 % for CaCO3 and CaSO4, respectively. With the aid of the FTIR and SEM measurement techniques, it was found that PASPG could induce the irregular growth of the CaCO3 and CaSO4 morphology and destroy the formation of crystals. On the other hand, the higher corrosion efficiency of 85.17 % was achieved by PASPG in comparison with PASP (72.53 %). PASPG is a mixed inhibitor and the adsorption of PASPG on the Q235 steel surface followed the Langmuir mono-layer adsorption isotherm. The formation of a protective film on the surface of carbon steel was proved by PASPG’s adsorption, which increased the resistance to be eroded. Thus, the surface of carbon steel can be effectively protected. The present work provides a simple and effective pathway for the synthesis of high-efficiency green scale and corrosion inhibitor, by introducing a functional group into the PASP chains. The implementation of such type of chemical modification method may also be an effective strategy for improving the efficiency of other polymers green scale and corrosion inhibitors.

Summary: The accurate estimation of battery health conditions is a crucial challenge for development of battery management systems due to the degradation of cathode and anode materials. In this paper, a fusion of deep learning model and feature analysis methods is employed to approach accurate estimation for state of health (SOH) and remaining useful life (RUL). The differential thermal voltammetry (DTV) signal analysis is executed to pre-process the datasets from Oxford University. A deep learning model is constructed with LSTM network as the core, combined with Bayesian optimization and dropout technique. This work shows that the deep learning model could approach the SOH and RUL early estimation with the mean absolute error of RUL maintained around 0.5%. It is potential that this deep learning model, combined with DTV signal analysis methods, could approach early prediction and estimation of battery SOH and RUL, contributing to the development of the next-generation high-energy-density and highly safety commercial batteries.

With the fast and ever-growing development of multifunctional wearable intelligent electronic devices (WEDs), the ideal building block materials for the fabrication of WEDs should be naturally abundant with characteristics such as green and non-toxic, excellent biocompatibility, biodegradability, and sanitation property. Gelatin is denatured collagen from natural connective tissue that mimics components of the native extracellular matrix regarding its biomimetic primary structure and chemical composition. Recently, gelatin-based conductive hydrogels (GC-Gels) are considered promising materials for developing flexible wearable sensors due to their unique porous structure and excellent application performances. This review first provides a comprehensive overview of the preparation, composition, structure, and properties of gelatin, followed by the current development status and progress of GC-Gels as well as GC-Gels-based flexible wearable devices with different structures and functions. Finally, the possible challenges and suggestions for the future development of GC-Gels-based flexible wearable devices are provided. This review is expected to promote and inspire the emerging applications of gelatin, its derivatives, and even other natural raw materials as sustainable advanced materials for WEDs.

Natural leather with outstanding biomimetic properties is the main application of animal skin. However, the traditional tannery industry is facing serious issues of sustainability, such as low value addition and high environmental pollution. Upscaled functional leather (UFL) inherits all the natural properties of leather and combines it with other cross-domain functions, due to which it has gained considerable attention as advanced functional bio-based materials. This review briefly introduces the multi-hierarchical structure and preponderant properties of leather as a natural biomass. Further emphasis is on UFL, which includes electromagnetic (EM) wave absorbing leather, infrared absorbing leather, X-ray shielding leather, flexible conductive leather, flame-retardant leather, antibacterial leather, self-cleaning leather as well as water-, oil-, and soil-repellent leather. Furthermore, the fabrication methods of these UFLs, their merits and their respective applications are comprehensively discussed. Moreover, possible challenges and outlooks for the development of UFL are proposed. This review describes how UFL will expand the scope of applications and significantly improve the ontological value of natural leather. Significantly, UFL is expected to boost the sustainable development of conventional leather industry and the next-generation of advanced functional bio-based materials.

Neointimal hyperplasia after vascular injury is a representative complication of restenosis. Endoplasmic reticulum (ER) stress-induced unfolded protein response (UPR) is involved in the pathogenesis of vascular intimal hyperplasia. PARP16, a member of the poly(ADP-ribose) polymerases family, is correlated with the nuclear envelope and the ER. Here, we found that PERK and IRE1α are ADP-ribosylated by PARP16, and this might promote proliferation and migration of smooth muscle cells (SMCs) during the platelet-derived growth factor (PDGF)-BB stimulating. Using chromatin immunoprecipitation coupled with deep sequencing (ChIP-seq) analysis, PARP16 was identified as a novel target gene for histone H3 lysine 4 (H3K4) methyltransferase SMYD3, and SMYD3 could bind to the promoter of Parp16 and increased H3K4me3 level to activate its host gene's transcription, which causes UPR activation and SMC proliferation. Moreover, knockdown either of PARP16 or SMYD3 impeded the ER stress and SMC proliferation. On the contrary, overexpression of PARP16 induced ER stress and SMC proliferation and migration. In vivo depletion of PARP16 attenuated injury-induced neointimal hyperplasia by mediating UPR activation and neointimal SMC proliferation. This study identified SMYD3–PARP16 is a novel signal axis in regulating UPR and neointimal hyperplasia, and targeting this axis has implications in preventing neointimal hyperplasia related diseases.

Summary: With the aggravation of environmental pollution and energy crisis, lithium-ion batteries are widely regarded as promising. However, the current distribution in the parallel battery pack branches is highly heterogeneous. Charging strategies based on the models can be adopted to prevent side reactions that may lead to severe degradation or even thermal runaway under various ambient temperatures. In this study, a battery model for a single cell is established by coupling a single particle model with electrolyte, degradation model, and thermal model. Besides, considering the contact resistance and wire resistance, the circuit model of a battery pack is established. A charging strategy based on minimum Li plating overpotential control is then adopted, and the effectiveness under high C-rate and low temperature to reduce capacity loss is verified by simulation. This study provides a low-loss charging strategy that can reduce the safety risk of battery packs with better performance under various ambient temperatures.

Background: Over 57 million people in Bangladesh have been chronically exposed to arsenic-contaminated drinking water. They also face environmental exposure to elevated levels of cadmium (Cd), manganese (Mn), and lead (Pb), all of which have been previously observed in environmental and biological samples for this population. These metals have been linked to adverse neurocognitive outcomes in adults and children, though their effects on adolescents are not yet fully characterized. Additionally, previous studies have linked selenium (Se) to protective effects against the toxicity of these other metals. Objectives: To examine the associations between mixed metals exposure and cognitive function in Bangladeshi adolescents. Methods: The Metals, Arsenic, & Nutrition in Adolescents study (MANAs) is a cross-sectional study of 572 Bangladeshi adolescents aged 14–16 years, whose parents were enrolled in the Health Effects of Arsenic Longitudinal Study (HEALS). Biosamples were collected from these adolescents for measurement of whole blood metalloid/metal levels of As, Cd, Mn, Pb, and Se. Participants also completed an abbreviated version of The Cambridge Neuropsychological Test Automated Battery (CANTAB), a cognitive function test designed to measure performance across several aspects of executive function. Linear regression was used to examine associations for each metal while controlling for the other metals. Bayesian Kernel Machine Regression (BKMR) assessed the overall mixture effect in addition to confirming the effects of individual metal components observed via linear regression. Results: Linear regression revealed negative associations for Spatial Working Memory and both As and Mn (As B=−2.40, Mn B=−5.31, p

Summary: An intelligent battery management system is a crucial enabler for energy storage systems with high power output, increased safety and long lifetimes. With recent developments in cloud computing and the proliferation of big data, machine learning approaches have begun to deliver invaluable insights, which drives adaptive control of battery management systems (BMS) with improved performance. In this paper, a general framework utilizing an end-edge-cloud architecture for a cloud-based BMS is proposed, with the composition and function of each link described. Cloud-based BMS leverages from the Cyber Hierarchy and Interactional Network (CHAIN) framework to provide multi-scale insights, more advanced and efficient algorithms can be used to realize the state-of-X estimation, thermal management, cell balancing, fault diagnosis and other functions of traditional BMS system. The battery intelligent monitoring and management platform can visually present battery performance, store working-data to help in-depth understanding of the microscopic evolutionary law, and provide support for the development of control strategies. Currently, the cloud-based BMS requires more effects on the multi-scale integrated modeling methods and remote upgrading capability of the controller, these two aspects are very important for the precise management and online upgrade of the system. The utility of this approach is highlighted not only for automotive applications, but for any battery energy storage system, providing a holistic framework for future intelligent and connected battery management.

Sulfonyl drugs are widely used to treat various types of diseases, in which sultones have several desirable properties that have led to their increased use, including improving its simple and effective synthetic methods and/or potentially enhancing potency of drugs. This review focuses on the recent progress in typical synthesis of sultones and the latest developments in various therapeutic applications, including carbonic anhydrase inhibitory activity, HIV-I and HIV-II inhibitory activity, human cytomegalovirus inhibitory activity, and cholinesterase inhibitory activity and their structure–activity relationship. We hope that this review will encourage medicinal chemists to consider the potential benefits to incorporating sultone scaffold into future drug discovery.

Cathode electrolyte interphase (CEI) has obtained increasing attention due to the importance in sustaining full cell life. However, it has rarely been overviewed. Therefore, it is timely and necessary to write this mini-review on CEI. This mini-review will involve the formation mechanism and modification methods of CEI. The perspective on CEI is also given out. We expect that this mini-review will provide rational guidelines for the CEI design of highly-performance lithium-ion batteries (LIBs).

Background: Emerging evidence suggests that phthalate exposure may be associated with behavior problems in children and that these associations may be sex specific. Methods: In a follow up study of 411 inner-city minority mothers and their children, mono-n-butyl phthalate (MnBP), monobenzyl phthalate (MBzP), monoisobutyl phthalate (MiBP), monethyl phthalate (MEP) and four di-2-ethylhexyl phthalate metabolites (DEHP) were quantified in maternal urine samples collected during the third trimester and in child urine samples at ages 3 and 5 years. The Conners’ Parent Rating Scale-Revised: Long Form (CPRS) and Child Behavior Checklist (CBCL) were administered to the mothers to assess children’s behavior problems at 7 years of age. The analysis included children with available measures of CBCL, CPRS and phthalates measured in maternal urine. We performed both Quasi-Poisson regression and a mixture analysis using Weighted Quantile Sum(WQS) regression to assess the risk for CPRS scores and for internalizing and externalizing behaviors (from the CBCL) following intra-uterine exposure to the phthalate metabolites for boys and girls separately. Results: Among boys, increases in in anxious-shy behaviors were associated with prenatal exposure to MBzP (Mean Ratio [MR] = 1.20, 95%CI 1.05–1.36) and MiBP (Mean Ratio (MR) = 1.22, 95%CI 1.02–1.47). Among girls, increases in perfectionism were associated with MBzP (MR = 1.15, 95%CI 1.01–1.30). In both boys and girls, increases in psychosomatic problems were associated with MiBP (MR = 1.28, 95%CI 1.02–1.60), and MnBP (MR = 1.28, 95%CI 1.02–1.59), respectively. Among girls, decreased hyperactivity was associated with two DEHP metabolites, mono(2-ethyl-5-oxohexyl) phthalate (MR = 0.83, 95%CI 0.71–0.98) and mono(2-ethyl-5-hydroxyhexyl) phthalate (MR = 0.85, 95%CI 0.72–0.99). Using weighted Quantile Sum logistic regression, no associations were found between the Weighted Quantile Sum (WQS) of phthalate metabolites and CPRS scores or externalizing and internalizing behaviors. Nonetheless, when the analysis was performed separately for DEHP and non-DEHP metabolites significant associations were found between the WQS of DEHP metabolites and social problems in boys (OR = 2.15, 95%CI 1.13–4.06, p-value = 0.02) anxious-shy problems in girls (OR = 2.19, 95%CI 1.15–4.16, p = 0.02), and emotional lability problems in all children (OR = 0.61, 95%CI 0.38–0.97, p = 0.04). MEHP and MEOHP were the most highly weighted DEHP metabolites in WQS mixture. The analysis performed with CBCL scale corroborated these associations. Conclusion: Concentration of non-DEHP metabolites was associated with anxious-shy behaviors among boys. DEHP phthalate metabolites were associated with decreased hyperactivity and impulsivity among girls on CPRS scores. These findings lend further support to the adverse associations between prenatal phthalate exposure and childhood outcomes, and clearly suggest that such associations are sex and mixture specific.

Effective management of lithium-ion batteries is a key enabler for a low carbon future, with applications including electric vehicles and grid scale energy storage. The lifetime of these devices depends greatly on the materials used, the system design and the operating conditions. This complexity has therefore made real-world control of battery systems challenging. However, with the recent advances in understanding battery degradation, modelling tools and diagnostics, there is an opportunity to fuse this knowledge with emerging machine learning techniques towards creating a battery digital twin. In this cyber-physical system, there is a close interaction between a physical and digital embodiment of a battery, which enables smarter control and longer lifetime. This perspectives paper thus presents the state-of-the-art in battery modelling, in-vehicle diagnostic tools, data driven modelling approaches, and how these elements can be combined in a framework for creating a battery digital twin. The challenges, emerging techniques and perspective comments provided here, will enable scientists and engineers from industry and academia with a framework towards more intelligent and interconnected battery management in the future.

Introduction: Phthalates are a group of high production chemicals, generally used as plasticizers and odor enhancers. Phthalates cross the blood-placenta barrier and are associated with deficits in cognitive functions and behavior problems in offspring. We previously reported sex-specific associations with motor function when phthalates are considered singly. Because exposure to phthalates usually occurs as mixtures, here we assess these associations between a mixture of phthalates and motor function at age 11 years. Methods: Data come from the prospective cohort study of mothers and offspring who participated in the Columbia Center for Children’s Environmental Health birth cohort (CCCEH). Seven phthalate metabolites were measured in maternal spot urine obtained during the third trimester and motor function was evaluated using the short form of the Bruininks-Oseretsky Test of Motor Proficiency, 2nd edition (BOT-2) at the age of 11 years. We used Weighted Quartile Sum (WQS) regression models to examine the effect of phthalate metabolites in males and females separately. The models were adjusted for child age in months, child BMI, maternal race (African-American vs. Dominican), prenatal alcohol consumption, maternal demoralization score, HOME score, and urine specific gravity. In a secondary analysis we used linear regression models to examine the association between the sum of molar concentrations of both DEHP and non-DEHP metabolites, and outcomes of gross and fine motor functions. Results: 209 mother-child pairs were eligible for this analysis. A significant decrease in fine-motor functions was observed among females, but not among males, following exposure to high levels of weighted quartile sum of seven phthalate metabolites (Covariates-adjusted coefficient estimate B = −2.7, 95% Confidence Interval [CI] −4.64 to −0.75, p = 0.01 for females [n = 116] and B = −1.63, 95%CI −3.94 to 0.69, p = 0.16 for males [n = 93]). The most highly weighted phthalate metabolites, associated with fine-motor functions among females, were MBP, MBZP, and MIBP, all non-DEHP phthalates.No significant associations were found between the weighted quartile sum of seven phthalate metabolites and gross-motor functions at the age of 11 years for males (B = −0.81, 95%CI −1.17 to 1.96, p = 0.23).With the molar sum of four non-DEHP phthalates as main predictor of linear regression models, we found significant decrease in gross and fine motor functions among females prenatally exposed to non-DEHP phthalates B = −0.98, 95%CI −1.98 to 0.03, p = 0.05 and B = −0.85, 95%CI −1.49 to −0.20, p = 0.01, respectively). Conclusions: Phthalate exposure during pregnancy was associated with decreased motor functions among 11-year-old girls. Keywords: Phthalates, Motor functions

Variant pharmacological activities of Zanthoxylum bungeanum were determined before. The aim of this study was to assess whether Z. bungeanum could regulate lipid metabolism. The cholesterol overloading HepG2 cells induced by sterols were used as in vitro model to study lipid-lowering activities of the n-butanol (BuOH) fraction isolated from Z. bungeanum (ZBBu). Male apolipoprotein E knockout (apoE-KO) mice with high fat diet were used as in vivo model. We firstly demonstrated ZBBu had effects on reversed lipid accumulation, decreased apoB and enhanced apoA1 secretion. It increased the amount of low density lipoprotein receptor (LDLR) protein, also significantly inhibited the expression of SREBP-1 and SREBP-2's target molecule (hydroxy methylglutaryl coenzyme A reductase, HMGCR), which might be active in stimulation of RCT. And the expression of genes involved in RCT, such as CYP27A1, LXR-α, ABCG1, was promoted by ZBBu. Furthermore, ZBBu could reduce serum TC, TG levels in apoE-KO mice. Our study indicated that ZBBu could regulate the lipid metabolism through increasing the amount of low density lipoprotein receptor (LDLR) and inducing the expression of genes involved in RCT.