Fabrication of Heterostructural FeNi3-Loaded Perovskite Catalysts by Rapid Plasma for Highly Efficient Photothermal Reverse Water Gas Shift Reaction.

Metal-semiconductor heterostructured catalysts have attracted great attention because of their unique interfacial characteristics and superior catalytic performance. Exsolution of nanoparticles is one of the effective and simple ways for in-situ growth of metal nanoparticles embedded in oxide surfaces and their favorable dispersion and stability. However, both high-temperature and a reducing atmosphere are required simultaneously in conventional exsolution, which is time-consuming and costly, and particles often agglomerate during the process. In this work, Ca0.9Ti0.8Ni0.1Fe0.1O3-δ (CTNF) is exposed to dielectric blocking discharge (DBD) plasma at room temperature to fabricate alloying FeNi3 nanoparticles from CTNF perovskite. FeNi3-CTNF has outstanding catalytic activity for photothermal reverse water gas shift reaction (RWGS). At 350 °C under full-spectrum irradiation, the carbon monoxide (CO) yield of FeNi3-CTNF (10.78 mmol g-1 h-1) is 11 times that of pure CaTiO3(CTO), and the CO selectivity is 98.9%. This superior catalytic activity is attributed to the narrow band gap, photogenerated electron migration to alloy particles, and abundant surface oxygen vacancies. The carbene pathway reaction is also investigated through in-situ Raman spectroscopy. The present work presents a straightforward method for the exsolution of nanoalloys in metal-semiconductor heterostructures for photothermal CO2 reduction.

Predictive and prognostic role of tumour-infiltrating lymphocytes in breast cancer patients with different molecular subtypes: a meta-analysis.

BACKGROUND: Whether tumour-infiltrating lymphocytes (TILs) play different roles in different molecular subtypes of breast cancer remains unknown. Additionally, their prognostic and predictive value in different molecular subtypes of breast cancer is still controversial. The aim of our meta-analysis was to assess the prognostic and predictive value of TILs in different molecular subtypes of breast cancer by summarizing all relevant studies performing multivariate analysis. METHODS: PubMed, Embase, EBSCO, ScienceDirect, the Cochrane Database and Web of Science were comprehensively searched (until March 2020). Hazard ratios (HRs), odds ratios (ORs) and their 95% confidence intervals (CIs) were used as effect measures to perform our meta-analysis. A random effect model was used. Stata software, version 15 (2017) (StataCorp, College Station, TX, USA) was used to perform the statistical analysis. RESULTS: Thirty-three studies including 18,170 eligible breast cancer patients were analysed. The meta-analysis showed that high TIL expression was significantly associated with increased pathological complete response (pCR) rates after neoadjuvant chemotherapy in patients with the HER2-enriched molecular subtype (OR = 1.137, 95% CI [1.061 ~ 1.218], p < 0.001) and triple-negative breast cancer (TNBC) subtype (OR = 1.120, 95% CI [1.061 ~ 1.182], p < 0.001). However, high TIL expression was not significantly associated with high pCR rates after neoadjuvant chemotherapy in patients with the luminal molecular subtype of breast cancer (OR = 1.154, 95% CI [0.789 ~ 1.690], p = 0.460). We carried out a meta-analysis on the HRs of overall survival (OS) and disease-free survival (DFS) to assess the prognostic value of TILs in breast cancer with different molecular subtypes more deeply. Our meta-analysis confirmed that high TILs were associated with significantly improved DFS in patients with the HER2-enriched molecular subtype [HR = 0.940, 95% CI (0.903 ~ 0.979), p = 0.003] and TNBC molecular subtype [HR = 0.907, 95% CI (0.862 ~ 0.954), p < 0.001]. However, high TILs were not associated with significantly better DFS in patients with the luminal molecular subtype of breast cancer [HR = 0.998, 95% CI (0.977 ~ 1.019), p = 0.840]. Furthermore, the results confirmed that high TILs were significantly related to better OS in patients with the HER2-enriched molecular subtype [HR = 0.910, 95% CI (0.866 ~ 0.957), p < 0.001] and TNBC molecular subtype [HR = 0.869, 95% CI (0.836 ~ 0.904), p < 0.001]. Conversely, the summarized results indicated that high TILs were significantly associated with poor OS in patients with the luminal molecular subtype of breast cancer [HR = 1.077, 95% CI (1.016 ~ 1.141), p = 0.012]. CONCLUSIONS: Our meta-analysis confirms that high TILs are associated with favourable survival and predicts pCR in breast cancer patients with the TNBC and HER2-enriched molecular subtypes.

Electroencephalogram and surface electromyogram fusion-based precise detection of lower limb voluntary movement using convolution neural network-long short-term memory model.

The electroencephalogram (EEG) and surface electromyogram (sEMG) fusion has been widely used in the detection of human movement intention for human-robot interaction, but the internal relationship of EEG and sEMG signals is not clear, so their fusion still has some shortcomings. A precise fusion method of EEG and sEMG using the CNN-LSTM model was investigated to detect lower limb voluntary movement in this study. At first, the EEG and sEMG signal processing of each stage was analyzed so that the response time difference between EEG and sEMG can be estimated to detect lower limb voluntary movement, and it can be calculated by the symbolic transfer entropy. Second, the data fusion and feature of EEG and sEMG were both used for obtaining a data matrix of the model, and a hybrid CNN-LSTM model was established for the EEG and sEMG-based decoding model of lower limb voluntary movement so that the estimated value of time difference was about 24 ∼ 26 ms, and the calculated value was between 25 and 45 ms. Finally, the offline experimental results showed that the accuracy of data fusion was significantly higher than feature fusion-based accuracy in 5-fold cross-validation, and the average accuracy of EEG and sEMG data fusion was more than 95%; the improved average accuracy for eliminating the response time difference between EEG and sEMG was about 0.7 ± 0.26% in data fusion. In the meantime, the online average accuracy of data fusion-based CNN-LSTM was more than 87% in all subjects. These results demonstrated that the time difference had an influence on the EEG and sEMG fusion to detect lower limb voluntary movement, and the proposed CNN-LSTM model can achieve high performance. This work provides a stable and reliable basis for human-robot interaction of the lower limb exoskeleton.

Rapid, facile synthesis of InSb twinning superlattice nanowires with a high-frequency photoconductivity response.

We present a self-seeded (with indium droplets) solution-liquid-solid (SLS) synthesis route for InSb nanowires (NWs) using commercially available precursors at a relatively low temperature of about 175 °C, which takes only 1 min upon the injection of reductant. Structural characterization reveals that the InSb nanowires are high quality and have twinning superlattice structures with periodically spaced twin planes along the growth direction of 〈111〉. Notably, we have measured an ultrafast conductivity lifetime in the NWs of just 9.1 ps utilizing time-resolved optical pump-terahertz probe (OPTP) spectroscopy, which may facilitate the development of high-frequency nanoscale integrated optoelectronic systems related to twinning superlattice structures.