Dense Layer Research Articles

Plant Disease Detection Using Machine Learning

In this project, we have developed a plant disease detection model using a machine learning approach on a dataset named “Plant Disease Expert” which is available on Kaggle. The dataset contains 199,611 images across 58 subdirectories. The splitting of the dataset in training, validation and testing is done in ratio 90:5:5. The data preprocessing included resizing the images, normalizing pixel values and applying augmentation techniques. We have used different libraries throughout the project such as NumPy, Pandas, Operating system, Time, Matplotlib, OpenCV, Shutil, TensorFlow, Keras, Seaborn, etc. EfficientNetB3 is used as the base model. EfficientNetB3 is a convolutional neural network architecture developed by Google Brain researchers [1]. It is known for its efficiency and effectiveness in image classification tasks. Additional custom layers such as max pooling, batch normalization, dense layers, and activation functions are used to optimize the performance. The adamax optimizer and categorical cross-entropy were used during training. The model was trained for 10 epochs with a batch size of 20 and a learning rate of 0.01. For evaluation, classification report, accuracy, precision, recall, F1-score and support were used. The model achieved an accuracy of 98.76% [1].

Enhancing sustainability and power generation from sewage‐driven air‐cathode microbial fuel cells through innovative anti‐biofouling spacer and biomass‐derived anode

AbstractBackgroundRecently, the concept of the membrane‐less microbial fuel cell (MFC) has gained traction to avoid the high internal resistance that is created upon utilizing conventional membranes. Nevertheless, an overlooked problem arises from the ingress of oxygen from the cathode side into the anolyte solution, fostering the formation of biofilms by aerobic microorganisms on the cathode surface. This biofilm layer poses a formidable impediment, leading to cell disconnection. Moreover, low surface area of conventional anodes is another important issue behind the low power density generation. In this research, a novel approach to circumvent biofilm formation and achieve stable and high‐power‐density output from MFCs by harnessing a commercial antibacterial spacer is introduced.ResultsAir‐cathode, sewage‐driven MFCs showed continuous power generation without the need for external microorganisms. Conversely, the absence of the innovative membrane resulted in a catastrophic power breakdown after 125 h of operation due to the formation of a dense biofilm layer on the cathode. Through the utilization of the proposed membrane strategy, stable power density output of 100 ± 8, 135 ± 11 and 142 ± 10 mW m−2 with carbon cloth, carbon paper and carbon felt anodes, respectively, was achieved. Moreover, a novel anode is introduced from graphitization of grape tree branches. The proposed anode could increase the generated power to 516 ± 17 mW m−2 from the sewage‐driven air‐cathode MFC, more than three times compared to the best conventional anode, carbon felt.ConclusionThis study provides significant solutions for sustainability, low‐performance and high‐cost problems of microbial fuel cells. © 2024 Society of Chemical Industry (SCI).

Preparation of Chromium Carbide-Strengthened Ni-W Coatings by Electrodeposition and the Corresponding Anticorrosion Behavior in CO2-Saturated NaCl Solution

In this study, we have prepared Ni-W/Cr3C2 composite electrodeposits through the diffuse reinforcement mechanism of chromium carbide nanoparticles. These electrodeposits exhibit excellent corrosion resistance and are designed to effectively protect the structural materials of pipe columns under the harsh CO2 corrosion environments faced by carbon sequestration and utilization technologies. A systematic investigation was conducted into the micro-morphology, profile undulation, phase composition, and surface chemical state of the electrodeposits. Furthermore, the corrosion-resistant behavior of Ni-W/Cr3C2 composite electrodeposits was investigated in simulated corrosive environments (i.e., brine solution containing 3.5 wt% NaCl and CO2-saturated brine). We then evaluated their corrosion response under the aforementioned conditions using electrochemical methods. In addition, the EIS of electrodeposited coatings revealed that the dense oxide film layer formed on the surface of the deposits, which acted as a primary barrier against corrosion, playing a pivotal role in maintaining the overall corrosion resistance of the material.

Inertial Measurement Unit Self-Calibration by Quantization-Aware and Memory-Parsimonious Neural Networks

This paper introduces a methodology to compensate inertial Micro-Electro-Mechanical System (IMU-MEMS) time-varying calibration loss, induced by stress and aging. The approach relies on a periodic assessment of the sensor through specific stimuli, producing outputs which are compared with the response of a high-precision sensor, used as ground truth. At any re-calibration iteration, differences with respect to the ground truth are approximated by quantization-aware trained tiny neural networks, allowing calibration-loss compensations. Due to the unavailability of aging IMU-MEMS datasets, a synthetic dataset has been produced, taking into account aging effects with both linear and nonlinear calibration loss. Also, field-collected data in conditions of thermal stress have been used. A model relying on Dense and 1D Convolution layers was devised and compensated for an average of 1.97 g and a variance of 1.07 g2, with only 903 represented with 16 bit parameters. The proposed model can be executed on an intelligent signal processing inertial sensor in 126.4 ms. This work represents a step forward toward in-sensor machine learning computing through integrating the computing capabilities into the sensor package that hosts the accelerometer and gyroscope sensing elements.

Novel Aryl Phosphate for Improving Fire Safety and Mechanical Properties of Epoxy Resins

Epoxy resins (EPs) are highly flammable, and traditional flame retardant modifications often lead to a significant reduction in their mechanical performance, limiting their applications in aerospace and electrical and electronic fields. In this study, a novel flame retardant, bis(4-(((diphenylphosphoryl)oxy)methyl)phenyl)phenyl phosphate (DMP), was successfully prepared and introduced into the EP matrix. When the addition of DMP was 9 wt%, the EP/9 wt% DMP thermosets passed the UL-94 V-0 rating, and their LOI was increased from 24.5% of EP to 35.0%. With the introduction of DMP, the phosphoric acid compounds from the decomposition of DMP promoted the dehydration and charring of the EP matrix, and the compact, dense char layer effectively exerted the shielding effect in the condensed phase. Meanwhile, the produced phosphorus-containing radicals played a quenching effect in the gas phase. As a result, the peak heat release rate (PHRR) and total heat release (THR) of EP/9 wt% DMP were reduced by 68.9% and 18.1% compared to pure EP. In addition, the polyaromatic structure of DMP had good compatibility with the EP matrix, and the tensile strength, flexural strength and impact strength of EP/9 wt% DMP were enhanced by 116.38%, 17.84% and 59.11% in comparison with that of pure EP. This study is valuable for expanding the application of flame-retardant EP/DMP thermosets in emerging fields.

Interfacial microstructure and corrosion behavior of ODS FeCrAl alloy in oxygen-saturated lead-bismuth eutectic at 450 °C

The interfacial microstructure and corrosion behavior of oxide dispersion strengthening (ODS) FeCrAl alloy with 20 wt% Cr (e.g. 20Cr ODS FeCrAl alloy) exposed to static oxygen-saturated lead-bismuth eutectic (LBE) at 450 °C for 20 h, 40 h, 60 h, 80 h and 100 h as exposure time respectively have been investigated. The results show that multilayer corrosion films of 20Cr ODS FeCrAl alloy can exist at the corrosion interface. Due to formation of protective scales, the primary corrosion mechanism of 20Cr ODS FeCrAl alloy in LBE is oxidation rather than dissolution and penetration of liquid LBE. Specially, the spinel-shaped nano-sized dense external layer without any microcracks is obviously observed. Moreover, the dense and compact scale with micron-meters, i.e. Al-rich Fe-Cr spinel, generates at interface as the middle layer to effectively defend inward diffusion of oxygen and penetration of LBE. Meanwhile, a reinforced and continuous internal layer mixed with Cr2O3 scale and the ferrite inner oxidation zone (IOZ) existing in the form of the interlocking interface between the layers and the substrate can further promote the adhesion between corrosion layers and the substrate of 20Cr ODS FeCrAl.

General Approach to Hydrolysis Resistive Aluminum Nitride and Its High-Performance Thermal Interface Materials.

Aluminum nitride (AlN), noted for its excellent thermal conductivity and exceptional electrical insulation, presents a promising alternative to traditional ceramic particles in thermal interface materials (TIMs). However, its broader adoption in practical applications is limited by performance degradation due to the vulnerability of its crystal structure to ubiquitous moisture. This study introduces a dual solution, utilizing a mechanochemical method to design a dense outer layer of Galinstan liquid metal (LM) that simultaneously enhances AlN's resistance to hydrolysis and improves its thermal performance in TIM applications. The high surface free energy of the LM layer imparts hydrophobic properties to the AlN surface and, combined with outer metal oxides, forms a dual-layer protective barrier that prevents water penetration, significantly enhancing the TIM's long-term stability in high-humidity conditions. Additionally, the LM layer at the interface improves the thixotropic properties of the TIM and enhances interfacial heat transport through the bridging effect of the LM, resulting in improved rheological mobility and thermal conductivity of the composite material. This win-win surface modification strategy opens opportunities for the practical and durable application of AlN in widespread electronic thermal management.

A seed-epidermis-feature-recognition-based lightweight peanut seed selection method for embedded systems

High-quality seeds improve the germination rate. Therefore, seed selection before sowing peanuts is crucial. Current peanut seed selection methods include color sorters and sieving machines, which are efficient but lack accuracy due to their reliance on a single indicator. Manual selection is inefficient and subject to subjective influences. Although machine vision and deep learning have performed well in crops such as corn and pepper, most existing research is based on PC or MATLAB platforms, which are not portable and are prone to interference, making them unsuitable for field applications. This study developed a lightweight model for recognizing peanut seed epidermal features. The model was based on deep learning and model quantization techniques. The transfer learning method was used to use four pre-trained models, EfficientNet_b0, EfficientNetv2-b0, MobileNet_v2_35_224, and NasNet_Mobile, as feature extraction layers, the input layer was added before the feature extraction layer, and the dropout and dense layers were added after the feature extraction layer to construct a classifier. The peanut seed selection network(PSSNet) models were constructed and named PSSNet-E, PSSNet-E2, PSSNet-M, and PSSNet-N, respectively, and trained on the Huayu 22 peanut seed dataset constructed in this study. The constructed models were compressed using model quantization technology, and four quantized models were obtained, namely PSSNet-Ef, PSSNet-E2f, PSSNet-Mf, and PSSNet-Nf. Finally, PSSNet-Mf, which had the best model evaluation, was selected as the peanut selection model for this study and deployed on a prototype for testing. Compared with the unquantized model, the size of the quantized model was reduced by two-thirds and the running speed was increased by 37%. A total of 400 Huayu 22 peanut seeds were selected as test samples, and 5 selection tests were conducted.The results showed that the average accuracy was 95.3%, which met the requirements of peanut selection at the production site.

Applying minirhizotrons to observe spatiotemporal variations in rooting depth and distribution in agroecosystems to improve the performance of hydrological models

AbstractTo understand and explain soil moisture dynamics, the role of the vegetation is crucial. Hydrological processes in agricultural soils are strongly affected by rooting depth and root distribution. We present a new approach to monitor and model root dynamics and its influence on soil moisture using minirhizotrons combined with phenological observations. Field setups consist of a portable root scanner and acrylic glass tubes installed in the soil at the start of the growing season. 360° scans of soil and roots are taken regularly at different depths in the tubes. Root traits are identified automatically for each soil layer and complemented by observations of aboveground plant phenology. Results from minirhizotron data collected at an agrometeorological observatory in Germany show for both cereal and rapeseed (Brassica napus L.) crops a higher root density in deeper soil layers and a lower density near the soil surface when compared to literature data. An opposite picture emerged for maize (Zea mays L.) and potato (Solanum tuberosum L.), whereas vertical root distribution in sugar beet (Beta vulgaris subsp. vulgaris) had a different seasonal course than expected from literature. Applying the new root distribution data in calculations of the soil water balance resulted in differences of more than 3% in absolute volumetric soil water content. A comparison with in situ measurements of volumetric soil moisture at 20‐ and 50‐cm depth revealed a significant improvement of the model results due to the new parameterization. Thus, we argue that minirhizotrons constitute a useful supplement to hydrological observatories and can help understand and predict soil moisture dynamics in the critical zone.

Experimental investigation of circumnutation-inspired penetration in sand

Abstract Probes that penetrate soil are used in fields such as geotechnical engineering, agriculture, and ecology to classify soils and characterize their properties in situ. Conventional tools like the Cone Penetration Test (CPT) often face challenges due to the lack of reaction force needed to penetrate stiff or dense soil layers, necessitating larger drill rigs. This paper investigates more efficient means of penetrating soil taking inspiration from a plant-root motion known as circumnutation. Experimental penetration tests on sands are performed with circumnutation-inspired (CI) probes that advance at a constant vertical velocity (v) while simultaneously rotating at a constant angular velocity (ω). These probes have tips have a given bent angle (α) and bent length (L1). The variation of the mobilized vertical force (Fz), torque (Tz), and the mechanical work components with the relative tangential to vertical velocity (\omega R/v) is investigated along with the effects of probe geometry, v, and soil relative density (DR). The results show that the soil penetration resistance does not vary with v, but it increases as \alpha, L1, and DR are increased. Fz decays exponentially with increasing ωR/v, Tz initially increases and then plateaus, while total work (WT) shows little magnitude changes initially but later increases monotonically. The mechanisms governing these trends are identified as changes in the probe projected areas and mobilized normal stresses due to differences in probe geometry and the effects of ωR/v on the resultant force direction and soil disturbance. The results show that CI penetration within a specific range of ωR/v leads to small increases in WT (i.e., ≦25%), yet mobilizes Fz magnitudes that are 50 to 80% lower than that mobilized during non-rotational penetration. This indicates that CI penetration can be adopted for in situ characterization or sensor placement with smaller vertical forces, allowing for use of lighter rigs.

Deciphering the Cell Surface Sugar-Coating via Biochemical Pathways.

Cell surface components, specifically glycans, play a significant role in several biological functions like cell structure, crosstalk between cells, and eventual target recognition of the cells for therapeutics. The dense layer of glycans, i. e., glycocalyx, could differ in taxon, species, and cell type. Glycans are coupled with lipids and proteins to form glycolipids, glycoproteins, proteoglycans, and glycosylphosphatidylinositol-anchored proteins, making their study challenging. However, understanding glycosylation at the cellular level is vital for fundamental research and advancing glycan-targeted therapy. Among different pathways, metabolic glycan labelling uses the natural metabolic processes of the cell to introduce abiotic functionality into glycan residues. The Bertozzi group pioneered metabolic oligosaccharide engineering using glycan salvage pathways to convert monosaccharides with unnatural modifications. This eventually results in the probe becoming part of the complex cellular glycan structures via click chemistry using copper. On the other hand, the boronic acid-based probe can recognise carbohydrates in a single step without any chemical modification of the surface. This review discusses the significance of glycans as biomarkers for different diseases and the necessity to evaluate them in situ within the physiological environment. The review also discusses the prospect of this field and its potential applications.

Analysis of Physiological Oxygen Concentrations in Different Abdominal Fat Layers by Body Mass Index.

Physiological oxygen concentration in adipose tissue is closely linked to metabolic disorders such as chronic inflammation and insulin resistance. However, the nature of the variation in the oxygen levels of adipose tissue with body mass index (BMI) and depths of abdominal fat remain unclear. Therefore, this study aimed to elucidate the patterns of oxygen concentration in adipose tissue layers according to BMI. In this study, patients undergoing abdominal fat removal surgery were divided into the normal-weight (NW) or overweight-obese (OW) groups based on their BMI. Oxygen concentrations in abdominal superficial (sSAT) and deep subcutaneous adipose tissue (dSAT) were measured. The oxygen consumption rate, mean cell area, and capillary density in both tissue layers were compared between the two groups. Furthermore, the interaction between these three variables, BMI, and adipose tissue oxygen concentration, was analyzed using linear regression. A total of 42 patients were recruited in this study and we observed that oxygen concentration in the sSAT was significantly lower than in the dSAT, irrespective of BMI. In terms of the oxygen concentration in the dSAT, OW's was significantly lower than that of NW's. Linear regression analysis revealed a significant correlation between dSAT oxygen concentration and BMI, mean adipocyte area, and vascular density. Individuals who are obese have significantly lower oxygen levels in the deep abdominal adipose tissue, and this is influenced by BMI, adipocyte area, and capillary density. This journal requires that authors assign a level of evidence to each submission to which Evidence-Based Medicine rankings are applicable. This excludes Review Articles, Book Reviews, and manuscripts that concern Basic Science, Animal Studies, Cadaver Studies, and Experimental Studies. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Relationship between plaque burden and plaque vulnerability in patients with acute coronary syndromes versus chronic coronary syndrome

Abstract Background The relationship between plaque burden and plaque vulnerability based on clinical presentations has not been fully investigated. Objective The aim of this study was to compare the relationship between plaque burden and plaque vulnerability in patients with acute coronary syndromes (ACS) versus chronic coronary syndrome (CCS). Methods This is an observational, single-center cohort study. Patients who underwent both coronary computed tomography angiography (CTA) and optical coherence tomography (OCT) before coronary intervention were enrolled. All OCT and CTA images were analyzed at the core laboratory. All plaques were detected in culprit vessels using CTA, and total plaque volume (TPV) and OCT features were assessed at the corresponding sites. A layered plaque was defined as a plaque with one or more layers of different optical densities and a clear demarcation from underlying components. The layer arc was measured at 1-mm intervals. The layer index, which is equivalent to layer volume, was defined as mean layer arc × layer length. All plaques were divided into three groups according to the tertile range of TPV (low TPV: <96.5 mm3, moderate TPV: 96.5 – 164.7 mm3, high TPV: ≥164.8 mm3). The effect modification of clinical presentations (CCS or ACS) on the association between TPV and OCT features was investigated. Results A total of 990 plaques were imaged by OCT in 419 patients: 445 plaques in 190 (45.3%) patients who presented with ACS and 545 in 229 (54.7%) with CCS. The prevalence of thin-cap fibroatheroma (TCFA) was higher as plaque volume increased in both groups (low vs. moderate vs. high TPV group: 18.4% vs. 37.8% vs. 52.3% in ACS; 16.2% vs. 19.9% vs. 30.6% in CCS; interaction P=0.066). However, the effect modification of clinical presentations on the associations of TPV with macrophage, lipid arc, and layer index was statistically significant. Macrophage was more prevalent in plaques with greater TPV in patients who presented with ACS but not in those who presented with CCS (low vs. moderate vs. high TPV group: macrophage 57.4% vs. 71.8% vs. 82.4% in ACS; 63.4% vs. 67.8% vs. 66.7% in CCS; interaction P=0.004). Lipid arc was more strongly associated with TPV in patients who presented with ACS than in those with CCS (low vs. moderate vs. high TPV group: 116 [76-157] vs. 166 [115-210] vs. 175 [140-221] in ACS; 118 [80-154] vs. 143 [102-187] vs. 150 [102-199] in CCS; interaction P=0.028). Conversely, layer index was more weakly associated with TPV in patients who presented with ACS than in those who presented with CCS (low vs. moderate vs. high TPV group: 0 [0-0] vs. 0 [0-365] vs. 0 [0-903] in ACS; 0 [0-756] vs. 235 [0-1266] vs. 552 [0-1704] in CCS; interaction P=0.046) (Figure 1). Conclusions Greater plaque burden was closely related to the level of plaque vulnerability in ACS, whereas this relationship was less obvious in CCS.Figure 1

Prediction of cancer therapy related cardiac dysfunction by using a machine learning approach with cardiac magnetic resonance images

Abstract Background There is inconsistent data on the value of baseline and early cardiac function assessment on identifying patients at risk of cancer therapy related cardiac dysfunction (CTRCD). Whether machine learning (ML) approaches applied to cardiac MRI (CMR) images may help risk stratify patients is unclear. Purpose In women with HER2+ breast cancer receiving sequential anthracyclines and trastuzumab we sought to (i) assess if a ML approach applied to pre-cancer therapy and early post anthracycline CMR short axis (SAX) cines can help identify patients at risk for future CTRCD and (ii) whether this approach is superior to using clinical and manual quantitative CMR data. Methods Women with HER2+ breast cancer from the EMBRACE-MRI study underwent 5 CMR studies (pre-cancer therapy, post anthracyclines, 3, 6 and 12 months during trastuzumab). CTRCD was defined using the CREC criteria by end of treatment. Three models were created: 1) clinical data (demographics, cardiovascular risk factors and medications); 2) clinical and manual quantitative CMR data (right and left ventricular volumes and LVEF); and 3) ML model based on SAX cine CMR images only. We focused on the pre-cancer therapy CMR and 1st visit post-anthracycline CMR. All images underwent standardised formatting. A 3D convolutional neural networks model architecture was used. The model consisted of multiple convolutional layers followed by max pooling and global average pooling layers. The output of the convolutional layers were then fed into a dense layer of 128 nodes incorporating an L2 activity regularizer and a subsequent dropout layer. To train and tune the model, the process was iterated 50 times. In each iteration, the patient participant dataset was shuffled, and a random 80% of patients were selected for training and tuning. The tuned model was then applied to the remaining 20% of patients in each iteration, and this entire cycle of shuffling, training, and testing was repeated 50 times with replacement. Area Under the Curve (AUC), accuracy, sensitivity, and specificity were calculated on the test set and averaged across folds to determine each model’s performance. Results 135 women were included (mean age 51.1±9.2 years) mean doxorubicin equivalent was dose 204.9±12.5mg/m2. Cardiovascular risk factors were prevalent: 33 (24%) had a smoking history; 21 (15%) had hypertension. 37 (27%) developed CTRCD. The ML model using baseline CMR cine data only had a higher AUC (0.84 95% CI [0.83-0.86]) for the detection of CTRCD compared to clinical data (0.62 95% CI [0.59-0.65]) and clinical and CMR data (0.62 95% CI [0.60-0.65]). This further improved with the addition of post-anthracycline CMR data (Table 1). Conclusion A ML model examining early cardiac MRI data with short axis cine CMR images performed better than those using clinical or manual quantitative CMR data for the prediction of CTRCD. Further studies including external validation are needed.Table 1 Mean and 95% CIs

Unraveling the adsorption dynamics of asphaltene molecules on silica surfaces

Understanding the adsorption behavior of heavy oil components on reservoir solids is crucial for improving oil recovery, yet the molecular mechanism remains unclear. This study used molecular dynamics simulations to explore the adsorption kinetics and thermodynamics of asphaltene molecules on silica surfaces. The adsorption process was divided into three stages: free, adsorption, and equilibrium. In the adsorption stage, asphaltenes must pass through two dense hydration layers and adhere to the silica surface in a flat configuration. Carboxyl groups increase asphaltene hydrophilicity, raising interaction energy with water molecules and hindering adsorption. In addition, two distinct hydration layers of water molecules on the silica surface. The first hydration layer, with a peak density of 2000 kg m−3, was located around 0.6 nm from the surface, driven by hydrogen bonding between Si-OH groups and water molecules. The second layer, found at 1.44–1.80 nm, had a lower density of 1200 kg m−3, formed through hydrogen bonding between water molecules. This study aims to enhance the understanding of the physicochemical mechanisms governing oil droplet adsorption on silica surfaces, potentially informing the design of improved oil recovery strategies.

Mitigating the Kinetic Hysteresis of Co-Free Ni-Rich Cathodes via Gradient Penetration of Nonmagnetic Silicon.

Co-free Ni-rich layered oxides are considered a promising cathode material for next-generation Li-ion batteries due to their cost-effectiveness and high capacity. However, they still suffer from the practical challenges of low discharge capacity and poor rate capability due to the hysteresis of Li-ion diffusion kinetics. Herein, based on the regulation of the lattice magnetic frustration, the Li/Ni intermixing defects as the primary origin of kinetic hysteresis are radically addressed via the doping of the nonmagnetic Si element. Meanwhile, by adopting gradient penetration doping, a robust Si-O surface structure with reversible lattice oxygen evolution and low lattice strain is constructed on Co-free Ni-rich cathodes to suppress the formation of surface dense barrier layer. With the remarkably enhanced Li-ion diffusion kinetics in atomic and electrode particle scales, the as-obtained cathodes (LiNixMn1-xSi0.01O2, 0.6≤x≤0.9) achieve superior performance in discharge capacity, rate capability, and durability. This work highlights the coupling effect of magnetic structure and interfacial chemicals on Li-ion transport properties, and the concept will inspire more researchers to conduct an intensive study.

Preparation of high strength and rigidity carbon layer on SiO material surface by dynamic CVD method using gas carbon source C2H2

The main issue in the application of silicon-based negative electrode materials is the inevitable volume expansion, leading to negative electrode material fracture, which severely impacts the performance of batteries. This study employed Chemical Vapor Deposition (CVD) (C2H2@SiO), solid-phase coating method (Pitch@SiO), and liquid-phase coating method (RGFQ@SiO) to coat the surface of SiO materials with a dense amorphous carbon structure. Material property analysis revealed that C2H2@SiO has a relatively small specific surface area (1.8 m2 g−1) and surface carbon increment (2.4 %). It exhibited high reversible specific capacity (1563.4 mAh g−1), high initial Coulombic efficiency (81.45 %), and low volume expansion rate (9.3 %). Mechanical performance testing indicated that the surface carbon layer Young's modulus of C2H2@SiO was the highest (35 GPa), suggesting that using the CVD method to obtain a dense carbon layer can enhance the structural strength of the material. Based on the electrochemical-mechanical coupling theory simulation analysis of the stress during the charge-discharge process of electrode materials, the evolution of concentration and stress field during lithium insertion process was obtained, showing that the coating can further improve the electrochemical and mechanical performance of the negative electrode materials effectively. Additionally, the negative electrode sheets fabricated using sample C2H2@SiO were assembled into 18650 cylindrical batteries, exhibiting excellent cycling performance in 1000 cycles at 25 °C with a capacity retention rate of 85.7 %, along with good rate capability and high/low-temperature performance. The conclusions of this study provide certain guidance for the design and optimization of negative electrode materials for battery electrodes.

Covalent organic framework membranes with vertically aligned nanorods for efficient separation of rare metal ions

Covalent organic frameworks (COFs) have emerged as promising platforms for membrane separations, while remaining challenging for separating ions in a fast and selective way. Here, we propose a concept of COF membranes with vertically aligned nanorods for efficient separation of rare metal ions. A quaternary ammonium-functionalized monomer is rationally designed to synthesize COF layers on porous substrates via interfacial synthesis. The COF layers possess an asymmetric structure, in which the upper part displays vertically aligned nanorods, while the lower part exhibits an ultrathin dense layer. The vertically aligned nanorods enlarge contact areas to harvest water and monovalent ions, and the ultrathin dense layer enables both high permeability and selectivity. The resulting membranes exhibit exceptional separation performances, for instance, a Cs+ permeation rate of 0.33 mol m−2 h−1, close to the value in porous substrates, and selectivities with Cs+/La3+ up to 75.9 and 69.8 in single and binary systems, highlighting the great potentials in the separation of rare metal ions.

Mechanical and high-temperature steam oxidation properties of Cr coatings deposited via high-power impulse magnetron sputtering

Applying protective coatings to Zr alloy cladding surfaces is one of the better methods to design fuel tolerant materials. In this study, the surface of a Zr-4 alloy was coated with Cr using high-power impulse magnetron sputtering. Furthermore, the mechanisms by which bias voltages affect the mechanical characteristics, resistance to high-temperature steam oxidation, and coating structure were elucidated. The coating exhibits a strong (200) weave structure with coarse grains at a bias voltage of -100 V. With increasing bias, the energy of deposited particles increases, grains continue to grow, (200) preferential growth orientation disappears, and the coating exhibits a (110) crystal orientation. The growth structure of the coating first shows a tendency to be dense and then loose. For the Cr coating with a (200) crystal orientation, a dense oxide layer is preferentially formed after oxidation, which can effectively block the internal diffusion of O. With increasing oxidation time, coarse Cr grains can effectively block the external diffusion of Zr. Furthermore, the Cr coating exhibiting a (110) crystal orientation was severely oxidized after oxidation, resulting in the formation of cracks at the film base; this accelerated the outward diffusion of Zr.

Mitigating the Kinetic Hysteresis of Co‐Free Ni‐Rich Cathodes via Gradient Penetration of Nonmagnetic Silicon

AbstractCo‐free Ni‐rich layered oxides are considered a promising cathode material for next‐generation Li‐ion batteries due to their cost‐effectiveness and high capacity. However, they still suffer from the practical challenges of low discharge capacity and poor rate capability due to the hysteresis of Li‐ion diffusion kinetics. Herein, based on the regulation of the lattice magnetic frustration, the Li/Ni intermixing defects as the primary origin of kinetic hysteresis are radically addressed via the doping of the nonmagnetic Si element. Meanwhile, by adopting gradient penetration doping, a robust Si−O surface structure with reversible lattice oxygen evolution and low lattice strain is constructed on Co‐free Ni‐rich cathodes to suppress the formation of surface dense barrier layer. With the remarkably enhanced Li‐ion diffusion kinetics in atomic and electrode particle scales, the as‐obtained cathodes (LiNixMn1−xSi0.01O2, 0.6≤x≤0.9) achieve superior performance in discharge capacity, rate capability, and durability. This work highlights the coupling effect of magnetic structure and interfacial chemicals on Li‐ion transport properties, and the concept will inspire more researchers to conduct an intensive study.