Suppressing Formation of Zn─Mn─O Phases by In Situ Ti Decoration of MnO2 for Long Lifespan MnO2-Zn Battery.

Mildly-acidic MnO2-Zn batteries are considered as a promising alternative for large-scale energy storage systems for their low toxicity, high safety, and low cost. Though, the degradation of MnO2 with cycling still hinders the further development of the batteries. In this study, it is observed that the decrease in available capacity of MnO2 with charge and discharge is accompanied by a structural transformation with the emergence of Zn─Mn─O phases. An electrodeposition test indicates that the Zn─Mn─O phase is formed from a co-precipitation of Zn and Mn during the charge process. Further, the structural change of MnO2 is suppressed and its cycle stability is improved with the addition of TiOSO4 as a facile electrolyte additive. As a result, under a current of 1200 mA g-1, the MnO2 electrode still gives a capacity of 230 mAh g-1 for over 1500 cycles. Capacity retention is 75% after 10 000 cycles under a current rate of 4800 mA g-1. These findings provide fundamental insights on the degradation mechanism of MnO2 and a new strategy to improve the electrochemical performance of aqueous MnO2-Zn batteries.

Predicting the Mechanisms for H2O2 Activation and Phenol Oxidation Catalyzed by Modified Graphene-Based Systems Using Density Functional Theory.

The heterogeneous Fenton-like reaction on metal-free graphene-based catalysts attracts great attention. However, a systematic and comprehensive understanding of the mechanisms for H2O2 activation and pollutant oxidation is still lacking. In this study, the heterogeneous Fenton-like mechanisms on doped and oxygen-containing graphene are investigated using density functional theory. The H2O2 tends to form a surface oxygen and a water molecule on the doped graphene. For the oxygen-containing graphene-based systems, relative to the groups in the basal plane, the separated groups on the edge including hydroxyl, carbonyl, and carboxyl readily activate H2O2 to hydroxyls. However, when the groups are close to each other, more additional side reactions might occur upon H2O2 adsorption, which may inhibit catalyst retrieval. Phenol is selected as a model pollutant to study its oxidation reaction with the adsorbed oxygen formed from the dissociated H2O2. The thermodynamics of the reactions depends significantly on the co-adsorption strengths over different catalysts. Our work provides key fundamental insights into the catalytic performance of various modified graphene-based systems, which could guide the future design and applications of heterogeneous Fenton reactions.

Artificial Intelligence Assisted Topographic Mapping System for Endoscopic Submucosal Dissection Specimens.

Background: Endoscopic submucosal dissection (ESD), a minimally invasive surgery used to treat early gastrointestinal malignancies, has been widely embraced around the world. The gross reconstruction of ESD specimens can facilitate a more precise pathological diagnosis and allow endoscopists to explore lesions thoroughly. The traditional method of mapping is time-consuming and inaccurate. We aim to design a topographic mapping system via artificial intelligence to perform the job automatically. Methods: The topographic mapping system was built using computer vision techniques. We enrolled 23 ESD cases at the Peking Union Medical College Hospital from September to November 2019. The reconstruction maps were created for each case using both the traditional approach and the system. Results: Using the system, the time saved per case ranges from 34 to 3,336 s. Two approaches revealed no significant variations in the shape, size, or tumor area. Conclusion: We developed an AI-assisted system that would help pathologists complete the ESD topographic mapping process rapidly and accurately.

Evaluation of optical band gaps and dopant state energies in transition metal oxides using oxidation-state constrained density functional theory.

We report the use of oxidation-state constrained density functional theory (OS-CDFT) to calculate the optical band gaps of transition metal oxides and dopant state energies of different doped anatase. OS-CDFT was used to control electron transfer from the valence band maximum of the transition metal system to the conduction band minimum or to the dopant state in order to calculate the band gap or the dopant state energies respectively. The calculation of the dopant state energies also allows identification of the transition responsible for the reduced band gap of the doped system in ambiguous cases. We applied this approach to the band gap calculation in TiO2anatase and rutile, vanadium pentoxide (V2O5), chromium(III) oxide (Cr2O3), manganese(IV) oxide (MnO2), ferric oxide (Fe2O3), ferrous oxide (FeO) and cobalt(II) oxide (CoO). The dopant state energies calculations were carried out in the V-, Cr-, Mn-, and Fe-doped anatase.

Automatic deep learning-based colorectal adenoma detection system and its similarities with pathologists.

OBJECTIVES: The microscopic evaluation of slides has been gradually moving towards all digital in recent years, leading to the possibility for computer-aided diagnosis. It is worthwhile to know the similarities between deep learning models and pathologists before we put them into practical scenarios. The simple criteria of colorectal adenoma diagnosis make it to be a perfect testbed for this study. DESIGN: The deep learning model was trained by 177 accurately labelled training slides (156 with adenoma). The detailed labelling was performed on a self-developed annotation system based on iPad. We built the model based on DeepLab v2 with ResNet-34. The model performance was tested on 194 test slides and compared with five pathologists. Furthermore, the generalisation ability of the learning model was tested by extra 168 slides (111 with adenoma) collected from two other hospitals. RESULTS: The deep learning model achieved an area under the curve of 0.92 and obtained a slide-level accuracy of over 90% on slides from two other hospitals. The performance was on par with the performance of experienced pathologists, exceeding the average pathologist. By investigating the feature maps and cases misdiagnosed by the model, we found the concordance of thinking process in diagnosis between the deep learning model and pathologists. CONCLUSIONS: The deep learning model for colorectal adenoma diagnosis is quite similar to pathologists. It is on-par with pathologists' performance, makes similar mistakes and learns rational reasoning logics. Meanwhile, it obtains high accuracy on slides collected from different hospitals with significant staining configuration variations.

Clinically applicable histopathological diagnosis system for gastric cancer detection using deep learning.

The early detection and accurate histopathological diagnosis of gastric cancer increase the chances of successful treatment. The worldwide shortage of pathologists offers a unique opportunity for the use of artificial intelligence assistance systems to alleviate the workload and increase diagnostic accuracy. Here, we report a clinically applicable system developed at the Chinese PLA General Hospital, China, using a deep convolutional neural network trained with 2,123 pixel-level annotated H&E-stained whole slide images. The model achieves a sensitivity near 100% and an average specificity of 80.6% on a real-world test dataset with 3,212 whole slide images digitalized by three scanners. We show that the system could aid pathologists in improving diagnostic accuracy and preventing misdiagnoses. Moreover, we demonstrate that our system performs robustly with 1,582 whole slide images from two other medical centres. Our study suggests the feasibility and benefits of using histopathological artificial intelligence assistance systems in routine practice scenarios.

Automatic detection of squamous cell carcinoma metastasis in esophageal lymph nodes using semantic segmentation.

Esophageal squamous cell carcinoma (ESCC) is more prevalent than esophageal adenocarcinoma in Asia, especially in China, where more than half of ESCC cases occur worldwide. Many studies have reported that the automatic detection of lymph node metastasis using semantic segmentation shows good performance in breast cancer and other adenocarcinomas. However, the detection of squamous cell carcinoma metastasis in hematoxylin-eosin (H&E)-stained slides has never been reported. We collected a training set of 110 esophageal lymph node slides with metastasis and 132 lymph node slides without metastasis. An iPad-based annotation system was used to draw the contours of the cancer metastasis region. A DeepLab v3 model was trained to achieve the best fit with the training data. The learned model could estimate the probability of metastasis. To evaluate the effectiveness of the detection model of learned metastasis, we used another large cohort of clinical H&E-stained esophageal lymph node slides containing 795 esophageal lymph nodes from 154 esophageal cancer patients. The basic authenticity label for each slide was confirmed by experienced pathologists. After filtering isolated noise in the prediction, we obtained an accuracy of 94%. Furthermore, we applied the learned model to throat and lung lymph node squamous cell carcinoma metastases and achieved the following promising results: an accuracy of 96.7% in throat cancer and an accuracy of 90% in lung cancer. In this work, we organized an annotated dataset of H&E-stained esophageal lymph node and trained a deep neural network to detect lymph node metastasis in H&E-stained slides of squamous cell carcinoma automatically. Moreover, it is possible to use this model to detect lymph nodes metastasis in squamous cell carcinoma from other organs. This study directly demonstrates the potential for determining the localization of squamous cell carcinoma metastases in lymph node and assisting in pathological diagnosis.

Hydro-Assisted Self-Regenerating Brominated N-Alkylated Thiophene Diketopyrrolopyrrole Dye Nanofibers-A Sustainable Synthesis Route for Renewable Air Filter Materials.

With rising global concerns over the alarming levels of particulate pollution, a sustainable air quality management is the need of the hour. Air filtration research has gained momentum in recent years. However, the research perspective is still blinkered toward formulating new fiber systems for the energy-intensive electrospinning process to fabricate high quality factor air filters. A holistic approach on sustainable air filtration models is still lacking. The air filter model presented in this work uses a simple process involving water-induced self-organization and self-regeneration of nanofibers, and an easy recycling route after the filter life that not only facilitates reuse of the microfibrous scaffold holding the nanofibers but also allows renewal of nanofibers. Three generations of air filters are fabricated and tested, all having high particulate matter (PM)-adsorbing tendency, high filtration efficiency (>95%), and high Young's modulus (≈5 GPa). The renewable air filters offer a sustainable alternative to the present cost-intensive electrospun air filters.

Oxidation-State Constrained Density Functional Theory for the Study of Electron-Transfer Reactions.

We propose a new constrained density functional theory (CDFT) approach which directly controls the oxidation state of the target atoms. In this new approach called oxidation-state constrained density functional theory (OS-CDFT), the eigenvalues of the occupation matrix obtained from projecting the Kohn-Sham wave functions onto the valence orbitals are constrained to obtain the desired oxidation states. This approach is particularly useful to study electron transfer problems in transition metal-containing systems due to the multivalent nature of the transition metal ions. The calculation of the forces on the ions and of the coupling constant was implemented under the OS-CDFT scheme to allow efficient and accurate study of electron transfer reactions. We demonstrated the application of this method in the study of different electron transfer reactions including the aqueous ferrous-ferric self-exchange reaction, polaron hopping in the TiO2 anatase and bismuth vanadate, and photoexcited electron transfer in the sapphire.