Solid-phase Synthesis of Visible-light-driven BiVO4 Photocatalyst and Photocatalytic Reduction of Aqueous Cr(VI)

This communication reports a pioneering study on the synthesis of BiVO4 and photocatalytic reduction of Cr(VI)-polluted wastewaters. Monoclinic phase BiVO4 micron-crystals with adjustable morphology were synthesized via a solid-phase route. The structures, morphology, optical properties of the BiVO4 micron-crystals were characterized by X-ray diffraction, field emission scanning electron microscopy, UV-vis diffuse reflectance spectra, Fourier transform infrared spectroscopy spectra, and photocurrent measurements. Besides, their photocatalytic properties were tested for the reduction of aqueous Cr(VI) under visible light (l > 420 nm) irradiation. The photocatalytic tests showed that the photocatalytic activities of BiVO4 powders in aqueous Cr(VI) depended on the dark adsorption amount for Cr(VI) and number of photogenerated carriers. BiVO4-(c) exhibited the highest photocatalytic reduction efficiency that attributed to highest separation and transfer efficiency of photogenerated electrons and holes. Besides, effects of photocatalytic experiment parameters (including dosage of photocatalyst and coexistent anions and cations) on the Cr(VI) removal rate by BiVO4-(c) were also investigated, and •OH play an important role in the BiVO4 photocatalytic reduction Cr(VI). Copyright © 2019 BCREC Group. All rights reserved

Electrochemical performance of different carbon fuels on a hybrid direct carbon fuel cell

The authors acknowledge the financial support of the Royal Society of Edinburgh for a RSE BP Hutton Prize in Energy Innovation and EPSRC Platform grant, EP/K015540/1.In this work, three processed carbon fuels including activated carbon, carbon black and graphite have been employed to investigate influence of the chemical and physical properties of carbon on the HDCFC performance in different anode atmospheres at 650–800 °C. The results reveal that the electrochemical activity is strongly dependent on crystalline structure, thermal stability and textural properties of carbon fuels. The activated carbon samples demonstrate a better performance with a peak power density of 326 mW cm−2 in CO2 at 750 °C, compared to 147 and 59 mW cm−2 with carbon black and graphite samples, respectively. Compared to the ohmic resistance, the polarization resistance plays a more dominated role in the cell performance. When replacing N2 by CO2 purge gas, the power density is the strongly temperature dependent due to the Boudouard reaction.PostprintPeer reviewe

Retroperitoneal schwannoma mimicking a metastatic lymph node of renal clear cell carcinoma: a case report

Schwannomas are usually benign tumors typically found in the head, neck, and extremities, with approximately 3% originating in the retroperitoneum. In this case, a young male presented with incidental masses in the left kidney and retroperitoneum. Abdominal pelvic enhanced computerized tomography (CT) revealed a tumor apparently originating from the left kidney, along with a retroperitoneal mass suspected to be a metastatic lymph node. Subsequently, a radical nephrectomy of the left kidney and retroperitoneal mass resection was performed. Pathological examination confirmed the left kidney mass as renal clear cell carcinoma and the retroperitoneal mass as schwannoma. The patient recovered uneventfully and was discharged from the hospital. A 6-month postoperative follow-up showed no evidence of recurrence. Preoperative diagnosis of schwannomas concurrent with other concurrent malignancies in rare sites, such as the retroperitoneum, is challenging due to their rare and non-specific radiological features. Although retroperitoneal schwannomas are rare, they should be considered in the differential diagnosis during CT examinations for renal cancer. Additionally, the advantages of a multidisciplinary team approach should be utilized in tumor management

Cauchy non-convex sparse feature selection method for the high-dimensional small-sample problem in motor imagery EEG decoding

IntroductionThe time, frequency, and space information of electroencephalogram (EEG) signals is crucial for motor imagery decoding. However, these temporal-frequency-spatial features are high-dimensional small-sample data, which poses significant challenges for motor imagery decoding. Sparse regularization is an effective method for addressing this issue. However, the most commonly employed sparse regularization models in motor imagery decoding, such as the least absolute shrinkage and selection operator (LASSO), is a biased estimation method and leads to the loss of target feature information.MethodsIn this paper, we propose a non-convex sparse regularization model that employs the Cauchy function. By designing a proximal gradient algorithm, our proposed model achieves closer-to-unbiased estimation than existing sparse models. Therefore, it can learn more accurate, discriminative, and effective feature information. Additionally, the proposed method can perform feature selection and classification simultaneously, without requiring additional classifiers.ResultsWe conducted experiments on two publicly available motor imagery EEG datasets. The proposed method achieved an average classification accuracy of 82.98% and 64.45% in subject-dependent and subject-independent decoding assessment methods, respectively.ConclusionThe experimental results show that the proposed method can significantly improve the performance of motor imagery decoding, with better classification performance than existing feature selection and deep learning methods. Furthermore, the proposed model shows better generalization capability, with parameter consistency over different datasets and robust classification across different training sample sizes. Compared with existing sparse regularization methods, the proposed method converges faster, and with shorter model training time