General Model for Predicting Response of Gas-Sensitive Materials to Target Gas Based on Machine Learning.

Gas sensors play a crucial role in various industries and applications. In recent years, there has been an increasing demand for gas sensors in society. However, the current method for screening gas-sensitive materials is time-, energy-, and cost-consuming. Consequently, an imperative exists to enhance the screening efficiency. In this study, we proposed a collaborative screening strategy through integration of density functional theory and machine learning. Taking zinc oxide (ZnO) as an example, the responsiveness of ZnO to the target gas was determined quickly on the basis of the changes in the electronic state and structure before and after gas adsorption. In this work, the adsorption energy and electronic and structural characteristics of ZnO after adsorbing 24 kinds of gases were calculated. These computed features served as the basis for training a machine learning model. Subsequently, various machine learning and evaluation algorithms were utilized to train the fast screening model. The importance of feature values was evaluated by the AdaBoost, Random Forest, and Extra Trees models. Specifically, charge transfer was assigned importance values of 0.160, 0.127, and 0.122, respectively, ranking as the highest among the 11 features. Following closely was the d-band center, which was presumed to exert influence on electrical conductivity and, consequently, adsorption properties. With 5-fold cross-validation using the Extra Tree accuracy, the 24-sample data set achieved an accuracy of 88%. The 72-sample data set achieved an accuracy of 78% using multilayer perceptron after 5-fold cross-validation, with both data sets exhibiting low standard deviations. This verified the accuracy and reliability of the strategy, showcasing its potential for rapidly screening a material's responsiveness to the target gas.

Two-step reconstruction framework of fluorescence molecular tomography based on energy statistical probability.

Fluorescence molecular tomography (FMT), as a promising technique for early tumor detection, can non-invasively visualize the distribution of fluorescent marker probe three-dimensionally. However, FMT reconstruction is a severely ill-posed problem, which remains an obstacle to wider application of FMT. In this paper, a two-step reconstruction framework was proposed for FMT based on the energy statistical probability. First, the tissue structural information obtained from computed tomography (CT) is employed to associate the tissue optical parameters for rough solution in the global region. Then, according to the global-region reconstruction results, the probability that the target belongs to each region can be calculated. The region with the highest probability is delineated as region of interest to realize accurate and fast source reconstruction. Numerical simulations and in vivo experiments were carried out to evaluate the effectiveness of the proposed framework. The encouraging results demonstrate the significant effectiveness and potential of our method for practical FMT applications.

Dynamic fluorescence molecular tomography metabolic parameters solution based on problem decomposition and prior refactor.

Dynamic fluorescence molecular tomography (DFMT), as a noninvasive optical imaging method, can quantify metabolic parameters of living animal organs and assist in the diagnosis of metabolic diseases. However, existing DFMT methods do not have a high capacity to reconstruct abnormal metabolic regions, and require additional prior information and complicated solution methods. This paper introduces a problem decomposition and prior refactor (PDPR) method. The PDPR decomposes the metabolic parameters into two kinds of problems depending on their temporal coupling, which are solved using regularization and parameter fitting. Moreover, PDPR introduces the idea of divide-and-conquer to refactor prior information to ensure discrimination between metabolic abnormal regions and normal tissues. Experimental results show that PDPR is capable of separating abnormal metabolic regions of the liver and has the potential to quantify metabolic parameters and diagnose liver metabolic diseases in small animals.

Laser-Ironing Induced Capping Layer on Co-ZIF-L Promoting In Situ Surface Modification to High-Spin Oxide-Carbon Hybrids on the "Real Catalyst" for High OER Activity and Stability.

Enhancing electrocatalytic performance through structural and compositional engineering attracts considerable attention. However, most materials only function as pre-catalysts and convert into "real catalysts" during electrochemical reactions. Such transition involves dramatic structural and compositional changes and disrupts their designed properties. Herein, for the first time, a laser-ironing (LI) approach capable of in-situ forming a laser-ironing capping layer (LICL) on the Co-ZIF-L flakes is developed. During the oxygen evolution reaction (OER) process, the LICL sustains the leaf-like morphology and promotes the formation of OER-active Co3 O4 nanoclusters with the highest activity and stability. In contrast, the pristine and conventional heat-treated Co-ZIF-Ls both collapse and transform to less active CoOOH. The density functional theory (DFT) calculations pinpoint the importance of the high spin (HS) states of Co ions and the narrowed band gap in Co3 O4 nanoclusters. They enhance the OER activity by promoting spin-selected electron transport, effectively lowering the energy barrier and realizing a spontaneous O2 -releasing step that is the potential determining step (pds) in CoOOH. This study presents an innovative approach for modulating both structural and compositional evolutions of electrocatalysts during the reaction, sustaining stability with high performance during dynamic electrochemical reactions, and providing new pathways for facile and high-precision surface microstructure control.

Structure-fused deep 3D hierarchical network: A bioluminescence tomography scheme for different imaging objects.

Monte Carlo eXtreme (MCX) method has a unique advantage for deep neural network based bioluminescence tomography (BLT) reconstruction. However, this method ignores the distribution of sources energy and relies on the determined tissue structure. In this paper, a deep 3D hierarchical reconstruction network for BLT was proposed where the inputs were divided into two parts -- bioluminescence image (BLI) and anatomy of the imaged object by CT. Firstly, a parallel encoder is used to feature the original BLI & CT slices and integrate their features to distinguish the different tissue structure of imaging objects; Secondly, GRU is used to fit the spatial information of different slices and convert it into 3D features; Finally, the 3D features are decoded to the spacial and energy information of source by a symmetrical decoding structure. Our research suggested that this method can effectively compute the radiation intensity and the spatial distribution of the source for different imaging object.

Liver injury monitoring using dynamic fluorescence molecular tomography based on a time-energy difference strategy.

Dynamic fluorescence molecular tomography (DFMT) is a promising molecular imaging technique that offers the potential to monitor fast kinetic behaviors within small animals in three dimensions. Early monitoring of liver disease requires the ability to distinguish and analyze normal and injured liver tissues. However, the inherent ill-posed nature of the problem and energy signal interference between the normal and injured liver regions limit the practical application of liver injury monitoring. In this study, we propose a novel strategy based on time and energy, leveraging the temporal correlation in fluorescence molecular imaging (FMI) sequences and the metabolic differences between normal and injured liver tissue. Additionally, considering fluorescence signal distribution disparity between the injured and normal regions, we designed a universal Golden Ratio Primal-Dual Algorithm (GRPDA) to reconstruct both the normal and injured liver regions. Numerical simulation and in vivo experiment results demonstrate that the proposed strategy can effectively avoid signal interference between liver and liver injury energy and lead to significant improvements in morphology recovery and positioning accuracy compared to existing approaches. Our research presents a new perspective on distinguishing normal and injured liver tissues for early liver injury monitoring.

An adaptive parameter selection strategy based on maximizing the probability of data for robust fluorescence molecular tomography reconstruction.

To alleviate the ill-posed of the inverse problem in fluorescent molecular tomography (FMT), many regularization methods based on L2 or L1 norm have been proposed. Whereas, the quality of regularization parameters affects the performance of the reconstruction algorithm. Some classical parameter selection strategies usually need initialization of parameter range and high computing costs, which is not universal in the practical application of FMT. In this paper, an universally applicable adaptive parameter selection method based on maximizing the probability of data (MPD) strategy was proposed. This strategy used maximum a posteriori (MAP) estimation and maximum likelihood (ML) estimation to establish a regularization parameters model. The stable optimal regularization parameters can be determined by multiple iterative estimates. Numerical simulations and in vivo experiments show that MPD strategy can obtain stable regularization parameters for both regularization algorithms based on L2 or L1 norm and achieve good reconstruction performance.

Multi-target reconstruction strategy based on blind source separation of surface measurement signals in FMT.

Fluorescence molecular tomography (FMT) is a promising molecular imaging technique for tumor detection in the early stage. High-precision multi-target reconstructions are necessary for quantitative analysis in practical FMT applications. The existing reconstruction methods perform well in retrieving a single fluorescent target but may fail in reconstructing a multi-target, which remains an obstacle to the wider application of FMT. In this paper, a novel multi-target reconstruction strategy based on blind source separation (BSS) of surface measurement signals was proposed, which transformed the multi-target reconstruction problem into multiple single-target reconstruction problems. Firstly, by multiple points excitation, multiple groups of superimposed measurement signals conforming to the conditions of BSS were constructed. Secondly, an efficient nonnegative least-correlated component analysis with iterative volume maximization (nLCA-IVM) algorithm was applied to construct the separation matrix, and the superimposed measurement signals were separated into the measurements of each target. Thirdly, the least squares fitting method was combined with BSS to determine the number of fluorophores indirectly. Lastly, each target was reconstructed based on the extracted surface measurement signals. Numerical simulations and in vivo experiments proved that it has the ability of multi-target resolution for FMT. The encouraging results demonstrate the significant effectiveness and potential of our method for practical FMT applications.

Pr6O11: Temperature-Dependent Oxygen Vacancy Regulation and Catalytic Performance for Lithium-Oxygen Batteries.

Many challenges still exist in lithium-oxygen batteries (LOBs), particularly exploring an efficient catalyst to optimize the reaction pathway and regulate the Li2O2 nucleation. Pr6O11 has a unique 4f electronic structure and the highest oxygen ion mobility among rare earth oxides, exhibiting superior electronic, optical, and chemical properties. These unique properties might endow it with advanced catalytic activities for LOBs. This work reports two crystal forms of Pr6O11 as novel catalysts and regulates the oxygen vacancy (Vo) concentrations by feasible calcination. Thermogravimetric analysis, X-ray diffraction, and X-ray photoelectron spectroscopy (XPS) confirm the conversion from commercial Pr6O11 to cubic fluorite Pr6O11 and Vo-rich Pr6O11. Photographs, high-resolution transmission electron microscopy, selected area electron diffraction, XPS, and electron paramagnetic resonance robustly demonstrate the temperature-dependent evolution of Vo. Ex situ XPS, scanning electron microscopy, and electrochemical techniques are used to study the catalytic mechanism and electrochemical reversibility. It is found that an appropriate Vo concentration can boost O2 adsorption/desorption, accelerate electron transport, and reduce the reaction energy barrier. Vo-rich Pr6O11 optimizes the reaction pathway by offering an intermediate Li2-xO2 (with metalloid conductivity) and adjusting Li2O2 into vertically staggered nanoflakes, effectively avoiding the suffocation of the catalytic surface and presenting excellent capacity, cycling stability, and rate performance.

Biological and biocompatible characteristics of fullerenols nanomaterials for tissue engineering.

Fullerenes, as hydrophobic molecules, are limited in biomedical function due to their very low solubility. But taking C60(OH)ₓ as an example, the properties of fullerenols were analyzed. It was found that fullerenols had good stability, water solubility, good biocompatibility and low cytotoxicity by adding a hydroxyl group to carbon atoms. In the biomedical field, it has been found that fullerene C60 can be used as a powerful free radical scavenger, with antioxidant activity, with antibacterial and inhibitory effects on cancer cells. Fullerenols inherit the good properties of fullerenes, and are better used in cancer treatment, including loading drug therapy and directly as an anticancer drug. In addition, fullerenols are also used in the repair of myocardial injury, the treatment of myocardial infarction and neuroprotection. With the development of tissue engineering technology, the preparation of nerve scaffolds which can improve ischemia, hypoxia and oxidative stress after nerve injury has become a research hotspot. The electron absorption and reduction characteristics of fullerenols in biomedical research bring new ideas for the treatment of oxidative stress in the repair of peripheral nerve defects. It seems that the research on fullerenols loaded neural scaffold has great prospects.

Fully Automated Bone Age Assessment on Large-Scale Hand X-Ray Dataset.

Bone age assessment (BAA) is an essential topic in the clinical practice of evaluating the biological maturity of children. Because the manual method is time-consuming and prone to observer variability, it is attractive to develop computer-aided and automated methods for BAA. In this paper, we present a fully automatic BAA method. To eliminate noise in a raw X-ray image, we start with using U-Net to precisely segment hand mask image from a raw X-ray image. Even though U-Net can perform the segmentation with high precision, it needs a bigger annotated dataset. To alleviate the annotation burden, we propose to use deep active learning (AL) to select unlabeled data samples with sufficient information intentionally. These samples are given to Oracle for annotation. After that, they are then used for subsequential training. In the beginning, only 300 data are manually annotated and then the improved U-Net within the AL framework can robustly segment all the 12611 images in RSNA dataset. The AL segmentation model achieved a Dice score at 0.95 in the annotated testing set. To optimize the learning process, we employ six off-the-shell deep Convolutional Neural Networks (CNNs) with pretrained weights on ImageNet. We use them to extract features of preprocessed hand images with a transfer learning technique. In the end, a variety of ensemble regression algorithms are applied to perform BAA. Besides, we choose a specific CNN to extract features and explain why we select that CNN. Experimental results show that the proposed approach achieved discrepancy between manual and predicted bone age of about 6.96 and 7.35 months for male and female cohorts, respectively, on the RSNA dataset. These accuracies are comparable to state-of-the-art performance.

Passive Millimeter Wave Imaging System Based on Helical Scanning.

A simple and fast single channel passive millimeter wave (PMMW) imaging system for public security check is presented in this paper. It distinguishes itself with traditional ones by an innovative scanning mechanism. Indoor experiments against human body with or without concealed items in clothes show that imaging could be completed in 3 s with angular resolution of about 0.7°. In addition, its field of view (FOV) is adjustable according to the size of actual target.

Dual-band Circular Polarizer Based on Simultaneous Anisotropy and Chirality in Planar Metamaterial.

Metamaterial of dual-square array is proposed to design a dual-band circular polarizer. The novel design of asymmetric unit cell and layout of duplicate arrays significantly enhances the coupling between electric and magnetic fields. Simulation and measurement results show that the polarizer presents wide angle circular dichroism and circular birefringence. Moreover, the polarization conversion of the proposed metamaterial changes with frequency, incident angle, and polarization of incident waves. The fundamental mechanism behind is concluded to be the angle-dependent chirality and dispersion of our novel design.