Hierarchically nanostructured porous TiO2(B) with superior photocatalytic CO2 reduction activity

Hierarchically nanostructured, porous TiO2(B) microspheres were synthesized by a microwave-assisted solvothermal method combined with subsequent heat treatment in air. The materials were carefully characterized by scanning and transmission electron microscopy, X-ray diffraction, CO2 adsorption, and a range of spectroscopies, including Raman, infrared, X-ray photoelectron and UV-Vis spectroscopy. The hierarchical TiO2 (B) particles are constructed by ultrathin nanosheets and possess large specific surface area, which provided many active sites for CO2 adsorption as well as CO2 conversion. The TiO2 (B) nanostructures exhibited marked photocatalytic activity for CO2 reduction to methane and methanol. Anatase TiO2 and P25 were used as the reference photocatalysts. Transient photocurrent measurement also proved the higher photoactivity of TiO2(B) than that of anatase TiO2. In-situ infrared spectrum was measured to identify the intermediates and deduce the conversion process of CO2 under illumination over TiO2(B) photocatalyst

A direct Z-scheme g-C3N4/SnS2 photocatalyst with superior visible-light CO2 reduction performance

Highlights - SnS2 quantum dots anchored in situ on g-C3N4 by a simple one-step hydrothermal method. - The internal electric field between g-C3N4 and SnS2 was confirmed. - Internal-electric-field-induced direct Z-scheme g-C3N4/SnS2 charge transfer for enhanced photocatalytic CO2 reduction. - In situ FTIR analysis further proved the suggested photocatalytic mechanism. Abstract Photocatalytic reduction of CO2 to solar fuels is an ideal approach to simultaneously solve the global warming and energy crisis issues. Constructing a direct Z-scheme heterojunction is an effective way to overcome the drawbacks of single-component or conventional heterogeneous photocatalysts for photocatalytic CO2 reduction. Here, a novel type of direct Z-scheme g-C3N4/SnS2 heterojunction was constructed by depositing SnS2 quantum dots onto the g-C3N4 surface in situ via a simple one-step hydrothermal method. l-Cysteine not only acted as the sulfur source, but also grafted ammine groups onto g-C3N4 in the hydrothermal process, which greatly enhanced the CO2 uptake of the composite. XPS analysis and density functional theory (DFT) calculation show that electron transfer occurred from g-C3N4 to SnS2, resulting in the formation of interfacial internal electric fields (IEF) between the two semiconductors at equilibrium. As a result, Z-scheme charge transfer took place under photoexcitation, with the electrons in SnS2 combining with the holes in g-C3N4, which improved the extraction and utilization of photoinduced electron in g-C3N4. The g-C3N4/SnS2 hybrid shows superior photocatalytic CO2 reduction as compared with individual g-C3N4 and SnS2, which should be attributed to the IEF-induced direct Z-scheme as well as improved CO2 adsorption capacity. In situ FTIR spectra illustrate that HCOOH appeared as an intermediate during the CO2 conversion, which can only be generated by g-C3N4 according to the energy level of the photoinduced electrons, further confirming the Z-scheme configuration for the g-C3N4/SnS2 system

A direct Z-scheme g-C3N4/SnS2 photocatalyst with superior visible-light CO2 reduction performance

Highlights - SnS2 quantum dots anchored in situ on g-C3N4 by a simple one-step hydrothermal method. - The internal electric field between g-C3N4 and SnS2 was confirmed. - Internal-electric-field-induced direct Z-scheme g-C3N4/SnS2 charge transfer for enhanced photocatalytic CO2 reduction. - In situ FTIR analysis further proved the suggested photocatalytic mechanism. Abstract Photocatalytic reduction of CO2 to solar fuels is an ideal approach to simultaneously solve the global warming and energy crisis issues. Constructing a direct Z-scheme heterojunction is an effective way to overcome the drawbacks of single-component or conventional heterogeneous photocatalysts for photocatalytic CO2 reduction. Here, a novel type of direct Z-scheme g-C3N4/SnS2 heterojunction was constructed by depositing SnS2 quantum dots onto the g-C3N4 surface in situ via a simple one-step hydrothermal method. l-Cysteine not only acted as the sulfur source, but also grafted ammine groups onto g-C3N4 in the hydrothermal process, which greatly enhanced the CO2 uptake of the composite. XPS analysis and density functional theory (DFT) calculation show that electron transfer occurred from g-C3N4 to SnS2, resulting in the formation of interfacial internal electric fields (IEF) between the two semiconductors at equilibrium. As a result, Z-scheme charge transfer took place under photoexcitation, with the electrons in SnS2 combining with the holes in g-C3N4, which improved the extraction and utilization of photoinduced electron in g-C3N4. The g-C3N4/SnS2 hybrid shows superior photocatalytic CO2 reduction as compared with individual g-C3N4 and SnS2, which should be attributed to the IEF-induced direct Z-scheme as well as improved CO2 adsorption capacity. In situ FTIR spectra illustrate that HCOOH appeared as an intermediate during the CO2 conversion, which can only be generated by g-C3N4 according to the energy level of the photoinduced electrons, further confirming the Z-scheme configuration for the g-C3N4/SnS2 system

Deep Learning-Based Classification of Cancer Cell in Leptomeningeal Metastasis on Cytomorphologic Features of Cerebrospinal Fluid

Background: It is a critical challenge to diagnose leptomeningeal metastasis (LM), given its technical difficulty and the lack of typical symptoms. The existing gold standard of diagnosing LM is to use positive cerebrospinal fluid (CSF) cytology, which consumes significantly more time to classify cells under a microscope.Objective: This study aims to establish a deep learning model to classify cancer cells in CSF, thus facilitating doctors to achieve an accurate and fast diagnosis of LM in an early stage.Method: The cerebrospinal fluid laboratory of Xijing Hospital provides 53,255 cells from 90 LM patients in the research. We used two deep convolutional neural networks (CNN) models to classify cells in the CSF. A five-way cell classification model (CNN1) consists of lymphocytes, monocytes, neutrophils, erythrocytes, and cancer cells. A four-way cancer cell classification model (CNN2) consists of lung cancer cells, gastric cancer cells, breast cancer cells, and pancreatic cancer cells. Here, the CNN models were constructed by Resnet-inception-V2. We evaluated the performance of the proposed models on two external datasets and compared them with the results from 42 doctors of various levels of experience in the human-machine tests. Furthermore, we develop a computer-aided diagnosis (CAD) software to generate cytology diagnosis reports in the research rapidly.Results: With respect to the validation set, the mean average precision (mAP) of CNN1 is over 95% and that of CNN2 is close to 80%. Hence, the proposed deep learning model effectively classifies cells in CSF to facilitate the screening of cancer cells. In the human-machine tests, the accuracy of CNN1 is similar to the results from experts, with higher accuracy than doctors in other levels. Moreover, the overall accuracy of CNN2 is 10% higher than that of experts, with a time consumption of only one-third of that consumed by an expert. Using the CAD software saves 90% working time of cytologists.Conclusion: A deep learning method has been developed to assist the LM diagnosis with high accuracy and low time consumption effectively. Thanks to labeled data and step-by-step training, our proposed method can successfully classify cancer cells in the CSF to assist LM diagnosis early. In addition, this unique research can predict cancer’s primary source of LM, which relies on cytomorphologic features without immunohistochemistry. Our results show that deep learning can be widely used in medical images to classify cerebrospinal fluid cells. For complex cancer classification tasks, the accuracy of the proposed method is significantly higher than that of specialist doctors, and its performance is better than that of junior doctors and interns. The application of CNNs and CAD software may ultimately aid in expediting the diagnosis and overcoming the shortage of experienced cytologists, thereby facilitating earlier treatment and improving the prognosis of LM