Facile Fabrication of Sandwich Structured WO₃ Nanoplate Arrays for Efficient Photoelectrochemical Water Splitting

Herein, sandwich structured tungsten trioxide (WO₃) nanoplate arrays were first synthesized for photoelectrochemical (PEC) water splitting via a facile hydrothermal method followed by an annealing treatment. It was demonstrated that the annealing temperature played an important role in determining the morphology and crystal phase of the WO₃ film. Only when the hydrothermally prepared precursor was annealed at 500 °C could the sandwich structured WO₃ nanoplates be achieved, probably due to the crystalline phase transition and increased thermal stress during the annealing process. The sandwich structured WO₃ photoanode exhibited a photocurrent density of 1.88 mA cm^–2 and an incident photon-to-current conversion efficiency (IPCE) as high as 65% at 400 nm in neutral Na₂SO₄ solution under AM 1.5G illumination. To our knowledge, this value is one of the best PEC performances for WO₃ photoanodes. Meanwhile, simultaneous hydrogen and oxygen evolution was demonstrated for the PEC water splitting. It was concluded that the high PEC performance should be attributed to the large electrochemically active surface area and active monoclinic phase. The present study can provide guidance to develop highly efficient nanostructured photoelectrodes with the favorable morphology

All-Solid-State C₃N₄/Ni_<i>x</i>P/Red Phosphorus Z‑Scheme Heterostructure for Wide-Spectrum Photocatalytic Pure Water Splitting

Z-scheme photocatalysts encouraged by natural photosynthesis have received increasing attention for pure water splitting. However, there have been only a few instances of effective Z-scheme nanosystems utilizing nonmetal photocatalysts for both water reduction and oxidation. In this study, we used carbon nitride (CN), metallic NixP, and crystalline red phosphorus (RP) to build a solid-state Z-scheme photocatalytic system, which worked as reduction sites, an electron mediator, and oxidation sites, respectively. The light absorption capability up to ∼600 nm enabled the photocatalyst to realize water splitting under broad-spectrum illumination. Detailed analysis suggested that the photocatalytic hydrogen production rate was apparently enhanced on account of effective spatial separation of light-induced charges owing to the intimate contact between the NixP mediator and photocatalyst components as well as the suitable energy band alignment. Meanwhile, hydrogen peroxide instead of oxygen was generated from water oxidation, which can solve the separation and safety issues of the synchronized production of hydrogen and oxygen and thus facilitated the feasible application of photocatalytic hydrogen production

Synthesis and Photoelectrochemical Properties of (Cu₂Sn)_<i>x</i>Zn_{3(1–<i>x</i>)}S₃ Nanocrystal Films

This work provides new routes for developing efficient photoelectrodes for photoelectrochemical (PEC) water splitting using a low-cost electrophoretic film preparation method. A series of (Cu₂Sn)_<i>x</i>Zn_{3(1–<i>x</i>)}S₃ (0 ≤ <i>x</i> ≤ 0.75) quaternary nanocrystals (NCs) with tunable optical band gaps are synthesized. Morphologies including particles, rods, and wires are obtained by tuning the composition of the NCs. (Cu₂Sn)_0.75Zn_0.75S₃ (Cu₂ZnSnS₄) has a pure kesterite structure, but an increase in the Zn content results in a kesterite–wurtzite polytypism. (Cu₂Sn)_<i>x</i>Zn_{3(1–<i>x</i>)}S₃ films are fabricated from their colloidal solutions via electrophoretic deposition, and the PEC properties of these films with p-type character have been examined under water-splitting conditions. It is shown that the photocurrent varies as a function of film thickness as well as chemical composition. The produced (Cu₂Sn)_0.45Zn_1.65S₃ (<i>x</i> = 0.45) film has the highest photocurrent, and the incident photon to current conversion efficiency is improved compared with previously reported results of Cu₂ZnSnS₄ photocathodes

Table_2_Predicting histologic grades for pancreatic neuroendocrine tumors by radiologic image-based artificial intelligence: a systematic review and meta-analysis.docx

BackgroundAccurate detection of the histological grade of pancreatic neuroendocrine tumors (PNETs) is important for patients’ prognoses and treatment. Here, we investigated the performance of radiological image-based artificial intelligence (AI) models in predicting histological grades using meta-analysis.MethodA systematic literature search was performed for studies published before September 2023. Study characteristics and diagnostic measures were extracted. Estimates were pooled using random-effects meta-analysis. Evaluation of risk of bias was performed by the QUADAS-2 tool.ResultsA total of 26 studies were included, 20 of which met the meta-analysis criteria. We found that the AI-based models had high area under the curve (AUC) values and showed moderate predictive value. The pooled distinguishing abilities between different grades of PNETs were 0.89 [0.84-0.90]. By performing subgroup analysis, we found that the radiomics feature-only models had a predictive value of 0.90 [0.87-0.92] with I2 = 89.91%, while the pooled AUC value of the combined group was 0.81 [0.77-0.84] with I2 = 41.54%. The validation group had a pooled AUC of 0.84 [0.81-0.87] without heterogenicity, whereas the validation-free group had high heterogenicity (I2 = 91.65%, P=0.000). The machine learning group had a pooled AUC of 0.83 [0.80-0.86] with I2 = 82.28%.ConclusionAI can be considered as a potential tool to detect histological PNETs grades. Sample diversity, lack of external validation, imaging modalities, inconsistent radiomics feature extraction across platforms, different modeling algorithms and software choices were sources of heterogeneity. Standardized imaging, transparent statistical methodologies for feature selection and model development are still needed in the future to achieve the transformation of radiomics results into clinical applications.Systematic Review Registrationhttps://www.crd.york.ac.uk/prospero/, identifier CRD42022341852.</p

Table_1_Predicting histologic grades for pancreatic neuroendocrine tumors by radiologic image-based artificial intelligence: a systematic review and meta-analysis.docx

BackgroundAccurate detection of the histological grade of pancreatic neuroendocrine tumors (PNETs) is important for patients’ prognoses and treatment. Here, we investigated the performance of radiological image-based artificial intelligence (AI) models in predicting histological grades using meta-analysis.MethodA systematic literature search was performed for studies published before September 2023. Study characteristics and diagnostic measures were extracted. Estimates were pooled using random-effects meta-analysis. Evaluation of risk of bias was performed by the QUADAS-2 tool.ResultsA total of 26 studies were included, 20 of which met the meta-analysis criteria. We found that the AI-based models had high area under the curve (AUC) values and showed moderate predictive value. The pooled distinguishing abilities between different grades of PNETs were 0.89 [0.84-0.90]. By performing subgroup analysis, we found that the radiomics feature-only models had a predictive value of 0.90 [0.87-0.92] with I2 = 89.91%, while the pooled AUC value of the combined group was 0.81 [0.77-0.84] with I2 = 41.54%. The validation group had a pooled AUC of 0.84 [0.81-0.87] without heterogenicity, whereas the validation-free group had high heterogenicity (I2 = 91.65%, P=0.000). The machine learning group had a pooled AUC of 0.83 [0.80-0.86] with I2 = 82.28%.ConclusionAI can be considered as a potential tool to detect histological PNETs grades. Sample diversity, lack of external validation, imaging modalities, inconsistent radiomics feature extraction across platforms, different modeling algorithms and software choices were sources of heterogeneity. Standardized imaging, transparent statistical methodologies for feature selection and model development are still needed in the future to achieve the transformation of radiomics results into clinical applications.Systematic Review Registrationhttps://www.crd.york.ac.uk/prospero/, identifier CRD42022341852.</p

Image_2_Predicting histologic grades for pancreatic neuroendocrine tumors by radiologic image-based artificial intelligence: a systematic review and meta-analysis.tif

BackgroundAccurate detection of the histological grade of pancreatic neuroendocrine tumors (PNETs) is important for patients’ prognoses and treatment. Here, we investigated the performance of radiological image-based artificial intelligence (AI) models in predicting histological grades using meta-analysis.MethodA systematic literature search was performed for studies published before September 2023. Study characteristics and diagnostic measures were extracted. Estimates were pooled using random-effects meta-analysis. Evaluation of risk of bias was performed by the QUADAS-2 tool.ResultsA total of 26 studies were included, 20 of which met the meta-analysis criteria. We found that the AI-based models had high area under the curve (AUC) values and showed moderate predictive value. The pooled distinguishing abilities between different grades of PNETs were 0.89 [0.84-0.90]. By performing subgroup analysis, we found that the radiomics feature-only models had a predictive value of 0.90 [0.87-0.92] with I2 = 89.91%, while the pooled AUC value of the combined group was 0.81 [0.77-0.84] with I2 = 41.54%. The validation group had a pooled AUC of 0.84 [0.81-0.87] without heterogenicity, whereas the validation-free group had high heterogenicity (I2 = 91.65%, P=0.000). The machine learning group had a pooled AUC of 0.83 [0.80-0.86] with I2 = 82.28%.ConclusionAI can be considered as a potential tool to detect histological PNETs grades. Sample diversity, lack of external validation, imaging modalities, inconsistent radiomics feature extraction across platforms, different modeling algorithms and software choices were sources of heterogeneity. Standardized imaging, transparent statistical methodologies for feature selection and model development are still needed in the future to achieve the transformation of radiomics results into clinical applications.Systematic Review Registrationhttps://www.crd.york.ac.uk/prospero/, identifier CRD42022341852.</p

Image_1.PDF

<p>Mitogen-activated protein kinase (MAPK) pathways are ubiquitous and evolutionarily conserved signal transduction modules directing cellular respond to a diverse array of stimuli, in the eukaryotic organisms. In this study, PlMAPK10 was identified to encode a MAPK in Peronophythora litchii, the oomycete pathogen causing litchi downy blight disease. PlMAPK10, containing a specific and highly conserved dual phosphorylation lip sequence SEY (Serine-Glutamic-Tyrosine), represents a novel group of MAPKs as previously reported. Transcriptional profiling showed that PlMAPK10 expression was up-regulated in zoospore and cyst stages. To elucidate its function, the PlMAPK10 gene was silenced by stable transformation. PlMAPK10 silence did not impair oospore production, sporangium germination, zoospore encyst, or cyst germination but hindered hyphal growth, sporulation, pathogenicity, likely due to altering laccase activity. Over all, our results indicated that a MAPK encoded by PlMAPK10 gene in P. litchii is important for pathogenic development.</p

Image_1_Predicting histologic grades for pancreatic neuroendocrine tumors by radiologic image-based artificial intelligence: a systematic review and meta-analysis.png

BackgroundAccurate detection of the histological grade of pancreatic neuroendocrine tumors (PNETs) is important for patients’ prognoses and treatment. Here, we investigated the performance of radiological image-based artificial intelligence (AI) models in predicting histological grades using meta-analysis.MethodA systematic literature search was performed for studies published before September 2023. Study characteristics and diagnostic measures were extracted. Estimates were pooled using random-effects meta-analysis. Evaluation of risk of bias was performed by the QUADAS-2 tool.ResultsA total of 26 studies were included, 20 of which met the meta-analysis criteria. We found that the AI-based models had high area under the curve (AUC) values and showed moderate predictive value. The pooled distinguishing abilities between different grades of PNETs were 0.89 [0.84-0.90]. By performing subgroup analysis, we found that the radiomics feature-only models had a predictive value of 0.90 [0.87-0.92] with I2 = 89.91%, while the pooled AUC value of the combined group was 0.81 [0.77-0.84] with I2 = 41.54%. The validation group had a pooled AUC of 0.84 [0.81-0.87] without heterogenicity, whereas the validation-free group had high heterogenicity (I2 = 91.65%, P=0.000). The machine learning group had a pooled AUC of 0.83 [0.80-0.86] with I2 = 82.28%.ConclusionAI can be considered as a potential tool to detect histological PNETs grades. Sample diversity, lack of external validation, imaging modalities, inconsistent radiomics feature extraction across platforms, different modeling algorithms and software choices were sources of heterogeneity. Standardized imaging, transparent statistical methodologies for feature selection and model development are still needed in the future to achieve the transformation of radiomics results into clinical applications.Systematic Review Registrationhttps://www.crd.york.ac.uk/prospero/, identifier CRD42022341852.</p

Thinning Segregated Graphene Layers on High Carbon Solubility Substrates of Rhodium Foils by Tuning the Quenching Process

We report the synthesis of large-scale uniform graphene films on high carbon solubility substrates of Rh foils for the first time using an ambient-pressure chemical vapor deposition method. We find that, by increasing the cooling rate in the growth process, the thickness of graphene can be tuned from multilayer to monolayer, resulting from the different segregation amount of carbon atoms from bulk to surface. The growth feature was characterized with scanning electron microscopy, Raman spectra, transmission electron microscopy, and scanning tunneling microscopy. We also find that bilayer or few-layer graphene prefers to stack deviating from the Bernal stacking geometry, with the formation of versatile moiré patterns. On the basis of these results, we put forward a segregation growth mechanism for graphene growth on Rh foils. Of particular importance, we propose that this randomly stacked few-layer graphene can be a model system for exploring some fantastic physical properties such as van Hove singularities

Direct Chemical Vapor Deposition-Derived Graphene Glasses Targeting Wide Ranged Applications

Direct growth of graphene on traditional glasses is of great importance for various daily life applications. We report herein the catalyst-free atmospheric-pressure chemical vapor deposition approach to directly synthesizing large-area, uniform graphene films on solid glasses. The optical transparency and sheet resistance of such kinds of graphene glasses can be readily adjusted together with the experimentally tunable layer thickness of graphene. More significantly, these graphene glasses find a broad range of real applications by enabling the low-cost construction of heating devices, transparent electrodes, photocatalytic plates, and smart windows. With a practical scalability, the present work will stimulate various applications of transparent, electrically and thermally conductive graphene glasses in real-life scenarios