In situ electrochemical synthesis of graphene-poly(arginine) composite for p-nitrophenol monitoring

Para-Nitrophenol (p-nitrophenol) is a common industrial pollutant occurring widely in water bodies, yet actual monitoring methods are limited. Herein we proposed a fully electrochemically in situ synthesized graphenepolyarginine composite functionalized screen printed electrode, as a novel p-nitrophenol sensing platform. The electrode was characterized by morphologic, spectrometric and electrochemical techniques. p-nitrophenol in both pure aqueous solution and real water samples was tested. Results show a detection limit as low as the nanomolar level, and display a linear response and high selectivity in the range of 0.5-1250 mu M. Molecular simulation reveals a detailed synergy between graphene and poly-arginine. The preferable orientation of nitrophenol molecules on the graphene interface in the presence of poly-arginine induces H- and ionic binding. This sensor is an ideal prototype for p-nitrophenol quantification in real waters

Machine learning models combining computed tomography semantic features and selected clinical variables for accurate prediction of the pathological grade of bladder cancer

ObjectiveThe purpose of this research was to develop a radiomics model that combines several clinical features for preoperative prediction of the pathological grade of bladder cancer (BCa) using non-enhanced computed tomography (NE-CT) scanning images.Materials and methodsThe computed tomography (CT), clinical, and pathological data of 105 BCa patients attending our hospital between January 2017 and August 2022 were retrospectively evaluated. The study cohort comprised 44 low-grade BCa and 61 high-grade BCa patients. The subjects were randomly divided into training (n = 73) and validation (n = 32) cohorts at a ratio of 7:3. Radiomic features were extracted from NE-CT images. A total of 15 representative features were screened using the least absolute shrinkage and selection operator (LASSO) algorithm. Based on these characteristics, six models for predicting BCa pathological grade, including support vector machine (SVM), k-nearest neighbor (KNN), gradient boosting decision tree (GBDT), logical regression (LR), random forest (RF), and extreme gradient boosting (XGBOOST) were constructed. The model combining radiomics score and clinical factors was further constructed. The predictive performance of the models was evaluated based on the area under the receiver operating characteristic (ROC) curve, DeLong test, and decision curve analysis (DCA).ResultsThe selected clinical factors for the model included age and tumor size. LASSO regression analysis identified 15 features most linked to BCa grade, which were included in the machine learning model. The SVM analysis revealed that the highest AUC of the model was 0.842. A nomogram combining the radiomics signature and selected clinical variables showed accurate prediction of the pathological grade of BCa preoperatively. The AUC of the training cohort was 0.919, whereas that of the validation cohort was 0.854. The clinical value of the combined radiomics nomogram was validated using calibration curve and DCA.ConclusionMachine learning models combining CT semantic features and the selected clinical variables can accurately predict the pathological grade of BCa, offering a non-invasive and accurate approach for predicting the pathological grade of BCa preoperatively

Air Stable PbSe Colloidal Quantum Dot Heterojunction Solar Cells: Ligand-Dependent Exciton Dissociation, Recombination, Photovoltaic Property, and Stability

We fabricated the long-term air stable PbSe colloidal quantum dots (CQDs) based planar heterojunction solar cells (FTO/TiO₂/PbSe/Au) with relatively larger active area (0.25 cm²) using tetrabutylammonium iodide (TBAI, I^–) as ligand in solid state ligand-exchange process. For the first time, we have achieved the whole preparation process of the device in the ambient atmosphere from PbSe CQDs collection to PbSe colloidal quantum dot solar cells (CQDSCs) fabrication, then storage and in their following measurements. Especially, TBAI-treated PbSe CQDSCs exhibited a power conversion efficiency (PCE) of 3.53% under AM 1.5 G in air, and also a remarkable long-term stability (more than 90 days) of their storage in ambient atmosphere has been identified. By contrast, 1,2-ethanedithiol (EDT), 3-mercaptopropionic acid (MPA) and cetyltrimethylammonium bromide (CTAB, Br^–) treated PbSe CQDSCs were further studied. The ligand-dependent exciton dissociation, recombination, energy level shift, and air stability of PbSe CQDs treated with these different ligands were systematically investigated. It was noted that TBAI-treated PbSe CQDSCs exhibited suppressed recombination, faster charge transfer rate, and longer carrier lifetimes, which resulted in a higher PCE and long-term air stability