Surface-Phosphided Metal Oxide Microspheres as Catalytic Host of Sulfur to Enhance the Performance of Lithium-Sulfur Batteries.

Lithium-sulfur (Li-S) batteries are one of the most promising high-energy density secondary batteries due to their high theoretical energy density of 2600 Wh kg-1. However, the sluggish kinetics and severe "shuttle effect" of polysulfides are the well-known barriers that hinder their practical applications. A carefully designed catalytic host of sulfur may be an effective strategy that not only accelerates the conversion of polysulfides but also limit their dissolution to mitigate the "shuttle effect." Herein, in situ surface-phosphided Ni0.96Co0.03Mn0.01O (p-NCMO) oxide microspheres are prepared via gas-phase phosphidation as a catalytic host of sulfur. The as-prepared unique heterostructured microspheres, with enriched surface-coated metal phosphide, exhibit superior synergistic effect of catalytic conversion and absorption of the otherwise soluble intermediate polysulfides. Correspondingly, the sulfur cathode exhibits excellent electrochemical performance, including a high initial discharge capacity (1162 mAh gs-1 at 0.1C), long cycling stability (491 mAh gs-1 after 1000 cycles at 1C), and excellent rate performance (565 mAh gs-1 at 5C). Importantly, the newly prepared sulfur cathode shows a high areal capacity of 4.0 mAh cm-2 and long cycle stability under harsh conditions (high sulfur loading of 5.3 mg cm-2 and lean electrolyte/sulfur ratio of 5.8 µL mg-1). This work proposes an effective strategy to develop the catalytic hosts of sulfur for achieving high-performance Li-S batteries via surface phosphidation.

Molecular Dynamics Simulation on the Charge Transport Properties in a Salt-in-Ionic Liquid Electrolyte.

A clear picture of charge transport properties in salt-in-ionic liquid electrolyte (SILE) is indispensable for the applications in lithium-ion batteries. In this study, we applied molecular dynamics (MD) simulations on a typical SILE system, composed of lithium bis(fluorosulfonyl)imide (LiFSI) with a molar fraction of 0.3 doped in 1-ethyl-3-methylimidazolium bis(fluorosulfonyl)imide (EMIMFSI). Based on the MD simulations, we calculated conductivity spectra from 108 Hz to 1014 Hz, charge current correlation functions, and charge mean square displacements, based on the center-of-mass (COM) velocities of the ions. The conductivity spectra show a bimodal feature between 1012 Hz and 1013 Hz, attributed to the interionic vibrations of the EMIM+-FSI- and Li+-FSI- contact ion pairs, respectively. Structural relaxation is observed between 109 Hz and 1012 Hz, and a flat plateau below 109 Hz, attributed to the direct current (DC) conductivity. For this SILE composed of three constituent ions, i.e., Li+, EMIM+, and FSI-, the above transport properties are further partitioned to the contributions of the individual constituent ions, including self, distinct contribution of the same constituent ions, and also the cross correlation between them. Detailed analyses on the individual contributions reveal strongly correlated motions in this complex ionic system.

A Catalytic Electrolyte Additive Modulating Molecular Orbital Energy Levels of Lithium Polysulfides for High-Performance Lithium-Sulfur Batteries.

Lithium-sulfur (Li-S) batteries have ultrahigh theoretical specific capacity, but the practical application is hindered by the severe shuttle effect and the sluggish redox kinetics of the intermediate lithium polysulfides (LiPSs). Effectively enhancing the conversion kinetics of LiPSs is essential for addressing these issues. Herein, the redox kinetics of LiPSs are effectively improved by introducing 6-azauracil (6-AU) molecules to the organic electrolyte to modulate the molecular orbital energy level of LiPSs. The 6-AU as a soluble catalyst can form complexes with LiPSs via Li-O bonds. These complexes are liable to transform because of the elevated HOMO and the reduced LUMO energy levels as compared to the dissociative LiPSs, resulting in small energy gaps (Egap) and exhibiting stronger redox activity. Benefiting from the rapid conversion kinetics, the shuttling effect of LiPSs is alleviated to a great extent, so that sulfur utilization is improved and the lithium electrode is protected. In addition, the introduction of 6-AU modulates the deposition behavior of Li2S and eases the coverage of the cathode surface by the insulating Li2S layer. The Li-S battery containing 6-AU provides superior capacity retention of 853 mAh g-1 after 150 cycles at 0.2 C and shows remarkable high-rate performance and retains a specific discharge capacity of 855 mAh g-1 at 5 C. This study accelerates the kinetics of Li-S batteries by tuning the HOMO and LUMO energy levels of LiPSs, which opens an avenue for designing functional electrolyte additives.

Combining Vis-NIR and NIR Spectral Imaging Techniques with Data Fusion for Rapid and Nondestructive Multi-Quality Detection of Cherry Tomatoes.

Firmness, soluble solid content (SSC) and titratable acidity (TA) are characteristic substances for evaluating the quality of cherry tomatoes. In this paper, a hyper spectral imaging (HSI) system using visible/near-infrared (Vis-NIR) and near-infrared (NIR) was proposed to detect the key qualities of cherry tomatoes. The effects of individual spectral information and fused spectral information in the detection of different qualities were compared for firmness, SSC and TA of cherry tomatoes. Data layer fusion combined with multiple machine learning methods including principal component regression (PCR), partial least squares regression (PLSR), support vector regression (SVR) and back propagation neural network (BP) is used for model training. The results show that for firmness, SSC and TA, the determination coefficient R2 of the multi-quality prediction model established by Vis-NIR spectra is higher than that of NIR spectra. The R2 of the best model obtained by SSC and TA fusion band is greater than 0.9, and that of the best model obtained by the firmness fusion band is greater than 0.85. It is better to use the spectral bands after information fusion for nondestructive quality detection of cherry tomatoes. This study shows that hyperspectral imaging technology can be used for the nondestructive detection of multiple qualities of cherry tomatoes, and the method based on the fusion of two spectra has a better prediction effect for the rapid detection of multiple qualities of cherry tomatoes compared with a single spectrum. This study can provide certain technical support for the rapid nondestructive detection of multiple qualities in other melons and fruits.

Theory of sigma bond resonance in flat boron materials.

In chemistry, theory of aromaticity or π bond resonance plays a central role in intuitively understanding the stability and properties of organic molecules. Here we present an analogue theory for σ bond resonance in flat boron materials, which allows us to determine the distribution of two-center two-electron and three-center two-electron bonds without quantum calculations. Based on this theory, three rules are proposed to draw the Kekulé-like bonding configurations for flat boron materials and to explore their properties intuitively. As an application of the theory, a simple explanation of why neutral borophene with ~1/9 hole has the highest stability and the effect of charge doping on borophene's optimal hole concentration is provided with the assumption of σ and π orbital occupation balance. Like the aromaticity theory for carbon materials, this theory greatly deepens our understanding on boron materials and paves the way for the rational design of various boron-based materials.

Cotton leaf segmentation with composite backbone architecture combining convolution and attention.

Plant leaf segmentation, especially leaf edge accurate recognition, is the data support for automatically measuring plant phenotypic parameters. However, adjusting the backbone in the current cutting-edge segmentation model for cotton leaf segmentation applications requires various trial and error costs (e.g., expert experience and computing costs). Thus, a simple and effective semantic segmentation architecture (our model) based on the composite backbone was proposed, considering the computational requirements of the mainstream Transformer backbone integrating attention mechanism. The composite backbone was composed of CoAtNet and Xception. CoAtNet integrated the attention mechanism of the Transformers into the convolution operation. The experimental results showed that our model outperformed the benchmark segmentation models PSPNet, DANet, CPNet, and DeepLab v3+ on the cotton leaf dataset, especially on the leaf edge segmentation (MIoU: 0.940, BIoU: 0.608). The composite backbone of our model integrated the convolution of the convolutional neural networks and the attention of the Transformers, which alleviated the computing power requirements of the Transformers under excellent performance. Our model reduces the trial and error cost of adjusting the segmentation model architecture for specific agricultural applications and provides a potential scheme for high-throughput phenotypic feature detection of plants.

Spectral Preprocessing Combined with Deep Transfer Learning to Evaluate Chlorophyll Content in Cotton Leaves.

Rapid determination of chlorophyll content is significant for evaluating cotton's nutritional and physiological status. Hyperspectral technology equipped with multivariate analysis methods has been widely used for chlorophyll content detection. However, the model developed on one batch or variety cannot produce the same effect for another due to variations, such as samples and measurement conditions. Considering that it is costly to establish models for each batch or variety, the feasibility of using spectral preprocessing combined with deep transfer learning for model transfer was explored. Seven different spectral preprocessing methods were discussed, and a self-designed convolutional neural network (CNN) was developed to build models and conduct transfer tasks by fine-tuning. The approach combined first-derivative (FD) and standard normal variate transformation (SNV) was chosen as the best pretreatment. For the dataset of the target domain, fine-tuned CNN based on spectra processed by FD + SNV outperformed conventional partial least squares (PLS) and squares-support vector machine regression (SVR). Although the performance of fine-tuned CNN with a smaller dataset was slightly lower, it was still better than conventional models and achieved satisfactory results. Ensemble preprocessing combined with deep transfer learning could be an effective approach to estimate the chlorophyll content between different cotton varieties, offering a new possibility for evaluating the nutritional status of cotton in the field.

A Unified Approach to Derive Atomic Partial Charges and Polarizabilities of Ionic Liquids.

We propose a unified approach to fit simultaneously a set of atomic partial charges and polarizabilities of the polarizable model against the ab initio electrostatic potential (ESP) and polarizability. The polarizable model is represented with interactive atomic dipoles with distance-dependent attenuation. For the polarizable model employed in this study, the internal electric field on the polarization sites is fully turned on, and thus allows self-induced dipoles, which persist even for an isolated molecule/ion. By such treatment, the contribution of ESP stems not only from the partial charges but also from the self-induced dipoles, and the atomic partial charges and polarizabilities can be fitted simultaneously against ESP in a unified manner. The fitting with 1-ethyl-3-methylimidazolium (EMIM+) and nitrate (NO3-), a prototypical organic cation and inorganic anion, respectively, that can form ionic liquid, demonstrates that allowance of the self-induced dipoles gives much better fitness. Moreover, test on the total dipole of an EMIM+/NO3- ion pair shows that the agreement with the ab initio dipole is also much improved for the polarizable model, which highlights the importance of the polarization effects of ionic liquids.

Detection of Pesticide Residue Level in Grape Using Hyperspectral Imaging with Machine Learning.

Rapid and accurate detection of pesticide residue levels can help to prevent the harm of pesticide residue. This study used visible/near-infrared (Vis-NIR) (376-1044 nm) and near-infrared (NIR) (915-1699 nm) hyperspectral imaging systems (HISs) to detect the level of pesticide residues. Three different varieties of grapes were sprayed with four levels of pesticides. Logistic regression (LR), support vector machine (SVM), random forest (RF), convolutional neural network (CNN), and residual neural network (ResNet) models were used to build classification models for pesticide residue levels. The saliency maps of CNN and ResNet were conducted to visualize the contribution of wavelengths. Overall, the results of NIR spectra performed better than those of Vis-NIR spectra. For Vis-NIR spectra, the best model was ResNet, with the accuracy of over 93%. For NIR spectra, LR was the best, with the accuracy of over 97%, but SVM, CNN, and ResNet also showed closed and fine results. The saliency map of CNN and ResNet presented similar and closed ranges of crucial wavelengths. Overall results indicated deep learning performed better than conventional machine learning. The study showed that the use of hyperspectral imaging technology combined with machine learning can effectively detect the level of pesticide residues in grapes.

The fluctuating standard potential of lithium in organic solvents.

The standard potential of a lithium metal electrode versus the standard hydrogen electrode was calculated by constructing the thermodynamic cycle in a hypothetical electrochemical cell with a dual-phase electrolyte. It is demonstrated that the standard potential of the lithium metal electrode can fluctuate over 0.5 V in different organic solvents, and is correlated to the modified donor number by the entropy of fusion of the solvents.

Application of Near-Infrared Spectroscopy and Hyperspectral Imaging Combined with Machine Learning Algorithms for Quality Inspection of Grape: A Review.

Grape is a fruit rich in various vitamins, and grape quality is increasingly highly concerned with by consumers. Traditional quality inspection methods are time-consuming, laborious and destructive. Near-infrared spectroscopy (NIRS) and hyperspectral imaging (HSI) are rapid, non-destructive and accurate techniques for quality inspection and safety assessment of agricultural products, which have great potential in recent years. The review summarized the applications and achievements of NIRS and HSI for the quality inspection of grapes for the last ten years. The review introduces basic principles, signal mode, data acquisition, analysis and processing of NIRS and HSI data. Qualitative and quantitative analysis were involved and compared, respectively, based on spectral features, image features and fusion data. The advantages, disadvantages and development trends of NIRS and HSI techniques in grape quality and safety inspection are summarized and discussed. The successful application of NIRS and HSI in grape quality inspection shows that many fruit inspection tasks could be assisted with NIRS and HSI.

Application of Hyperspectral Imaging for Maturity and Soluble Solids Content Determination of Strawberry With Deep Learning Approaches.

Maturity degree and quality evaluation are important for strawberry harvest, trade, and consumption. Deep learning has been an efficient artificial intelligence tool for food and agro-products. Hyperspectral imaging coupled with deep learning was applied to determine the maturity degree and soluble solids content (SSC) of strawberries with four maturity degrees. Hyperspectral image of each strawberry was obtained and preprocessed, and the spectra were extracted from the images. One-dimension residual neural network (1D ResNet) and three-dimension (3D) ResNet were built using 1D spectra and 3D hyperspectral image as inputs for maturity degree evaluation. Good performances were obtained for maturity identification, with the classification accuracy over 84% for both 1D ResNet and 3D ResNet. The corresponding saliency maps showed that the pigments related wavelengths and image regions contributed more to the maturity identification. For SSC determination, 1D ResNet model was also built, with the determination of coefficient (R 2) over 0.55 of the training, validation, and testing sets. The saliency maps of 1D ResNet for the SSC determination were also explored. The overall results showed that deep learning could be used to identify strawberry maturity degree and determine SSC. More efforts were needed to explore the use of 3D deep learning methods for the SSC determination. The close results of 1D ResNet and 3D ResNet for classification indicated that more samples might be used to improve the performances of 3D ResNet. The results in this study would help to develop 1D and 3D deep learning models for fruit quality inspection and other researches using hyperspectral imaging, providing efficient analysis approaches of fruit quality inspection using hyperspectral imaging.

Grafting and Depositing Lithium Polysulfides on Cathodes for Cycling Stability of Lithium-Sulfur Batteries.

Based on dissolution/deposition chemistry, together with multielectron redox reactions, lithium-sulfur (Li-S) batteries have been demonstrated as a promising energy storage system. However, the diffusion of soluble lithium polysulfide intermediates (LiPSs) to bulk electrolyte results in the fast capacity fade of a Li-S cell. How to confine the LiPSs within the cathode while retaining high reversible capacity remains a huge challenge. In this work, N-bromophthalimide, an organic molecule with an aromatic heterocyclic ring and a reactive halogen bond, is introduced as an electrolyte additive to conquer the excessive dissolution and diffusion of LiPSs by in situ formation of an organopolysulfide deposition layer. This electrochemically active layer not only maintains the internal sulfur conversion but also prevents LiPSs from diffusing into the electrolyte bulk, thereby improving the cycling and rate performance of Li-S batteries. This study provides a feasible strategy for regulating the reaction region and path for high-performance Li-S batteries.

Binary structure and dynamics of the hydrogen bonds in the hydration shells of ions.

Ion-specific effects of cations (Li+, Na+, K+, Mg2+, Ca2+) and anions (F-, Cl-) on the hydrogen bond structure and dynamics of the coordination waters in the hydration shells have been studied using molecular dynamics simulations. Our simulations indicate that the hydrogen bonds between the first and second hydration shell waters show binary structural and dynamic properties. The hydrogen bond with a first shell water as the donor (HD) is strengthened, while those with a first shell water as the acceptor (HA) are weakened. For a hydrated anion, this binary effect reverses, but is less significant. This ion-specific binary effect correlates with the size and the valence of the ion, and is more significant for the strong kosmotropic ions of high charge density.

Combining Multi-Dimensional Convolutional Neural Network (CNN) With Visualization Method for Detection of Aphis gossypii Glover Infection in Cotton Leaves Using Hyperspectral Imaging.

Cotton is a significant economic crop. It is vulnerable to aphids (Aphis gossypii Glovers) during the growth period. Rapid and early detection has become an important means to deal with aphids in cotton. In this study, the visible/near-infrared (Vis/NIR) hyperspectral imaging system (376-1044 nm) and machine learning methods were used to identify aphid infection in cotton leaves. Both tall and short cotton plants (Lumianyan 24) were inoculated with aphids, and the corresponding plants without aphids were used as control. The hyperspectral images (HSIs) were acquired five times at an interval of 5 days. The healthy and infected leaves were used to establish the datasets, with each leaf as a sample. The spectra and RGB images of each cotton leaf were extracted from the hyperspectral images for one-dimensional (1D) and two-dimensional (2D) analysis. The hyperspectral images of each leaf were used for three-dimensional (3D) analysis. Convolutional Neural Networks (CNNs) were used for identification and compared with conventional machine learning methods. For the extracted spectra, 1D CNN had a fine classification performance, and the classification accuracy could reach 98%. For RGB images, 2D CNN had a better classification performance. For HSIs, 3D CNN performed moderately and performed better than 2D CNN. On the whole, CNN performed relatively better than conventional machine learning methods. In the process of 1D, 2D, and 3D CNN visualization, the important wavelength ranges were analyzed in 1D and 3D CNN visualization, and the importance of wavelength ranges and spatial regions were analyzed in 2D and 3D CNN visualization. The overall results in this study illustrated the feasibility of using hyperspectral imaging combined with multi-dimensional CNN to detect aphid infection in cotton leaves, providing a new alternative for pest infection detection in plants.

From a bulk to nanoconfined water chain: bridge water at the pore of the (6,6) carbon nanotube.

Hydrophobic porous materials with nano-pores are critical in many processes such as water desalination and biological membrane transportation. Herein, we performed molecular dynamics (MD) simulations on a prototypical hydrophobic nanochannel consisting of a (6,6) carbon nanotube (CNT) of 4.12 Å in radius and 13.72 Å in length immersed in water. The simulation shows that there are two major filling numbers of water N = 5 and N = 6, with the former being the most stable one. The confined waters form a single-file water chain with two hydrogen bonds per water. An extending water chain is formed for N = 5, with a bridge water near the pore of the CNT linking the water confined inside the CNT and hydration layer around the pore of the CNT. The bridge water can be considered as intermediate water characterized by three hydrogen bonds that distinguish from the confined water and bulk water. On the other hand, the hydration layer is depleted from the pore when N = 6. The analyses of the correlation of the bond order for the adjacent hydrogen bond pair of the hydration layer around the pore of the CNT does not show apparent difference from that of bulk water, though the former is slightly ordered. van Hove analysis of the bridge water shows that it tends to move inside the CNT when N < 5, in order to maintain the chemical equilibrium between the confined water and bulk water. This study highlights the unique structure of water around the hydrophobic pore of a sub-nanometer nanochannel.

High Volumetric Energy Density Sulfur Cathode with Heavy and Catalytic Metal Oxide Host for Lithium-Sulfur Battery.

For high-energy lithium-sulfur batteries, the poor volumetric energy density is a bottleneck as compared with lithium-ion batteries, due to the low density of both the sulfur active material and sulfur host. Herein, in order to enhance the volumetric energy density of sulfur cathode, a universal approach is proposed to fabricate a compact sulfur cathode with dense materials as sulfur host, instead of the old-fashioned lightweight carbon nanomaterials. Based on this strategy, heavy lanthanum strontium manganese oxide (La0.8Sr0.2MnO3), with a high theoretical density of up to 6.5 g cm-3, is introduced as sulfur host. Meanwhile, the La0.8Sr0.2MnO3 host also acts as an efficient electrocatalyst to accelerate the diffusion, adsorption, and redox dynamics of lithium polysulfides in the charge-discharge processes. As a result, such S/La0.8Sr0.2MnO3 cathode presents high gravimetric/volumetric capacity and outstanding cycling stability. Moreover, an ultra-high volumetric energy density of 2727 Wh L-1 -cathode is achieved based on the densification effect with higher density (1.69 g cm-3), which is competitive to the Ni-rich oxide cathode (1800-2160 Wh L-1) of lithium-ion batteries. The current study opens up a path for constructing high volumetric capacity sulfur cathode with heavy and catalytic host toward practical applications of lithium-sulfur batteries.

Proton Propensity and Orientation of Imidazolium Cation at Liquid Imidazole-Vacuum Interface: A Molecular Dynamics Simulation.

Imidazole has gained attention as an alternative to anhydrous proton conductor in high-temperature proton exchange membrane fuel cells. A detailed investigation of proton propensity and the orientation of the imidazolium cation at the liquid-vacuum interface is important for understanding the interfacial properties of imidazole-based proton-conductive materials. Here, we perform all-atom molecular dynamics simulation on a slab model of the liquid imidazole-vacuum interface. Proton transportation process between the imidazolium cation and neutral imidazole molecules is described by the multistate empirical valence bond model of imidazole developed previously. The imidazolium cation shows a tendency to stay in the bulk region rather than at the outermost surface, and the NN vectors and norm vectors of both the imidazolium cation and imidazole molecules are more probable to be perpendicular to the surface normal vector at the interface than in the bulk. The orientation of the hydrogen bond cluster shows the same tendency as the NN vectors, which indicates that proton transportation along the direction of the surface normal vector is hindered. The instantaneous surface analyses show that the fluctuation is depressed when the imidazolium cation is near the outermost surface, which makes it less favorable for the cation appearing at the interface.

Hydrogen Bond Dynamics in the Solvation Shell on Proton Transfer in Aqueous Solution.

Molecular dynamics (MD) simulations based on the multistate empirical valence bond model have been performed to study the proton transfer (PT) process in aqueous solution. This study focuses on the details of the hydrogen bond (HB) dynamics in the solvation shells of an excess proton accompanied by PT events. The HB dynamics analyses show that the three water molecules in the first solvation shell of hydronium (H3O+) tend to break their accepted HB to maintain a distorted Eigen (H9O4+) configuration. The results from MD simulations show that the cleavage and formation of accepted HBs on the water ligands in the first solvation shell of the proton acceptor water molecule and donor water molecule are crucial to drive the PT. Moreover, the water-donated and -accepted HBs around the H3O+ solvation shells are inequivalent, induced by the excess proton. Coupled with the PT, the donated HBs are enhanced on the proton acceptor side, while, in contrast, the accepted HBs are weakened on the same side.

Application and interpretation of deep learning methods for the geographical origin identification of Radix Glycyrrhizae using hyperspectral imaging.

Radix Glycyrrhizae is used as a functional food and traditional medicine. The geographical origin of Radix Glycyrrhizae is a determinant factor influencing the chemical and physical properties as well as its medicinal and health effects. The visible/near-infrared (Vis/NIR) (376-1044 nm) and near-infrared (NIR) hyperspectral imaging (915-1699 nm) were used to identify the geographical origin of Radix Glycyrrhizae. Convolutional neural network (CNN) and recurrent neural network (RNN) models in deep learning methods were built using extracted spectra, with logistic regression (LR) and support vector machine (SVM) models as comparisons. For both spectral ranges, the deep learning methods, LR and SVM all exhibited good results. The classification accuracy was over 90% for the calibration, validation, and prediction sets by the LR, CNN, and RNN models. Slight differences in classification performances existed between the two spectral ranges. Further, interpretation of the CNN model was conducted to identify the important wavelengths, and the wavelengths with high contribution rates that affected the discriminant analysis were consistent with the spectral differences. Thus, the overall results illustrate that hyperspectral imaging with deep learning methods can be used to identify the geographical origin of Radix Glycyrrhizae, which provides a new basis for related research.