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Mechanically flexible optoelectronic devices and systems can enable a much broader range of applications than what their rigid counterparts can do, especially for novel bio-integrated optoelectronic systems, flexible consumer electronics and wearable sensors. Inorganic semiconductor could be a good candidate for the flexible PD when it can keep its high performance under the bending condition. Here, we demonstrate a III–V material-based flexible photodetector operating wavelength from 640 to 1700 nm with the high detectivity of 5.18 × 10 11  cm‧Hz 1/2 /W and fast response speed @1550 nm by using a simply top-to-down fabrication process. The optoelectrical performances are stable as the PDs are exposed to bending cycles with a radius of 15 mm up to 1000 times. Furthermore, the mechanical failure mode of the PD is also investigated, which suggests that the cracking and delamination failure mode are dominant in bending up and bending down direction, respectively. Such a flexible III–V material-based PD and design with stable and high performance could be a promising strategy for the application of the flexible broad spectrum detection.

Two-dimensional (2D) semiconductors are promising in channel length scaling of field-effect transistors (FETs) due to their excellent gate electrostatics. However, scaling of their contact length still remains a significant challenge because of the sharply raised contact resistance and the deteriorated metal conductivity at nanoscale. Here, we construct a 1D semimetal-2D semiconductor contact by employing single-walled carbon nanotube electrodes, which can push the contact length into the sub-2 nm region. Such 1D–2D heterostructures exhibit smaller van der Waals gaps than the 2D–2D ones, while the Schottky barrier height can be effectively tuned via gate potential to achieve Ohmic contact. We propose a longitudinal transmission line model for analyzing the potential and current distribution of devices in short contact limit, and use it to extract the 1D–2D contact resistivity which is as low as 10 −6 Ω·cm 2 for the ultra-short contacts. We further demonstrate that the semimetal nanotubes with gate-tunable work function could form good contacts to various 2D semiconductors including MoS 2 , WS 2 and WSe 2 . The study on 1D semimetal contact provides a basis for further miniaturization of nanoelectronics in the future. 2D semiconductors are attracting increasing attention as potentially scalable channels for future transistors, but the scaling of their contact length remains challenging. Here, the authors report the realization of 1D semimetal-2D semiconductor contacts based on individual carbon nanotubes with contact length down to 2 nm.

Low-dimensional semimetal–semiconductor (Sm-S) van der Waals (vdW) heterostructures have shown their potentials in nanoelectronics and nano-optoelectronics recently. It is an important scientific issue to study the interfacial charge transfer as well as the corresponding Fermi-level shift in Sm-S systems. Here we investigated the gate-tunable contact-induced Fermi-level shift (CIFS) behavior in a semimetal single-walled carbon nanotube (SWCNT) that formed a heterojunction with a transition-metal dichalcogenide (TMD) flake. A resistivity comparison methodology and a Fermi-level catch-up model have been developed to measure and analyze the CIFS, whose value is determined by the resistivity difference between the naked SWCNT segment and the segment in contact with the TMD. Moreover, the relative Fermi-level positions of SWCNT and two-dimensional (2D) semiconductors can be efficiently reflected by the gate-tunable resistivity difference. The work function change of the semimetal, as a result of CIFS, will naturally introduce a modified form of the Schottky–Mott rule, so that a modified Schottky barrier height can be obtained for the Sm-S junction. The methodology and physical model should be useful for low-dimensional reconfigurable nanodevices based on Sm-S building blocks.

Hybrid organic-inorganic metal halide perovskites are emerging materials in photovoltaics, whose bandgap is one of the most crucial parameters governing their light harvesting performance. Here we present the temperature and photocarrier density dependence of the bandgap in two phase-stabilized perovskite thin films (MA0.3FA0.7PbI3 and MA0.3FA0.7Pb0.5Sn0.5I3) using photoluminescence and absorption spectroscopy. Contrasting bandgap shifts with temperature are observed between the two perovskites. Using X-ray diffraction and in situ high-pressure photoluminescence spectroscopy, we show that thermal expansion plays only a minor role in the large bandgap blueshift, which is attributed to the enhanced structural stability of our samples. Our first-principles calculations further demonstrate the significant impact of thermally induced lattice distortions on the bandgap widening. We propose that the anomalous trends are caused by the competition between static and dynamic distortions. Additionally, both the bandgap renormalization and band filling effects are directly observed for the first time in fluence-dependent photoluminescence measurements and are employed to estimate the exciton effective mass. Our results provide new insights into the basic understanding of thermal and charge-accumulation effects on the band structure of hybrid perovskite thin films.

This study proposes a multimodal collaborative control method based on an improved whale optimization algorithm and iterative learning to address the issues of insufficient multimodal fusion and poor adaptability to dynamic environments in smart home human-machine collaborative control. Firstly, by introducing a dynamic learning gain mechanism to optimize the iterative learning control algorithm, the convergence speed and tracking accuracy of the system can be improved; Secondly, a feature level and decision level fusion strategy is adopted to achieve effective fusion of speech and gesture modalities; Finally, a complete smart home human-machine collaborative control system architecture is constructed. 1) In terms of control accuracy, the research method achieves average control accuracy of 0.9212 and 0.9053 in single-device and multi-device scenarios, respectively, significantly better than particle swarm optimization genetic algorithm (0.8751) and grey wolf optimization backpropagation network (0.8346). 2) In terms of error indicators, the maximum mean absolute error (0.167) and root mean square error (0.196) are reduced by more than 50% compared to particle swarm optimization genetic algorithm (0.373/0.338) and grey wolf optimization backpropagation network (0.337/0.324). 3) In terms of system performance, the accuracy recall curve area (0.9758) is improved by 5.45%-13.68% compared to the comparison methods, the system resource utilization rate is 0.054%-0.131%, and the average response time (10.31-24.12ms) is improved by more than 30% compared to particle swarm optimization genetic algorithm (18.89ms) and grey wolf optimization backpropagation network (16.21ms). The research provides a high-precision and low latency human-machine collaborative control solution for the field of smart homes.

As science and technology rapidly advance, the image recognition technology currently employed in the field of computer vision suffers from the drawback of inadequate recognition effectiveness. In response to this problem, an improved deep convolutional neural network model is introduced to recognize visual images to raise the precision of image recognition. The research improves the deep convolutional neural network model through wavelet analysis algorithm. The deep convolutional neural network model mainly reduces the number of parameters in image recognition, while the wavelet analysis algorithm mainly denoises the noise that appears in image recognition. On the basis of improving the deep convolutional neural network model, combined with the boundary Fisher analysis algorithm that can recognize high-dimensional data, an image recognition model is constructed to achieve efficient recognition of visual images. The experiment outcomes indicate that the proposed model has an average loss value of only 0.2573 when performing image recognition, significantly lower than other models, and its accuracy reaches 95.82%, significantly higher than other models. The proposed model achieves a recognition accuracy of 0.971 when recognizing images of different categories, significantly higher than other models. The above data indicate that the raised model has good recognition performance in the field of visual image recognition, and the image recognition accuracy is significantly higher than the other two models.Article highlightsBy using wavelet analysis to improve the VGG16 network, noise was effectively removed and the accuracy of image recognition was improved.By using boundary Fisher analysis to reduce the dimensionality of high-dimensional data, the robustness of the model was improved.The model can efficiently and accurately perform image recognition, and has application prospects in fields such as security monitoring.

This paper introduces a 0.5–5.8 GHz low-noise amplifier (LNA) incorporating a gyrator-C-based active inductor (AI) and an enhanced deep trench isolation (DTI) electrostatic discharge (ESD) diode. Results suggest that AIs exhibit excellent consistency under various process voltage temperatures (PVTs) as well as input powers and the improved DTI diodes reduce parasitic capacitance by an average of 8.5% compared to conventional ones. In terms of circuit design, comprehensive analyses of gain flatness and noise are conducted. Fabricated using a 0.18 μm SiGe BiCMOS technology, the LNA delivers a high S21 of 18.3 ± 0.3 dB, a minimum noise figure of 2.6 dB, and an S11 and S22 of less than −10 dB over the entire frequency band. Operating from a 3.3 V supply voltage with a core area of 0.049 mm2, it consumes 10 mA of current.

We theoretically study the Goos-Hänchen (GH) and Imbert–Fedorov (IF) shifts of a reflected Gaussian beam from a hyperbolic metasurface composed of graphene grating based on topological insulators (TIs). Perturbations are generated on the surface of TIs by applying a thin magnetic film, resulting in a broken time-reversal symmetry. The GH and IF shifts are greatly enhanced as a result of the combined interaction of the graphene grating and the topological magnetoelectric effect (TME). In particular, even with the p-polarized incident beam near Brewster angles, the magnitude of IF shifts is increased by approximately two orders when compared to the case without graphene or a single layer of graphene. A critical frequency is identified when the propagation model in TIs transitions from a surface wave to a bulk wave, which leads to comparatively substantial GH shifts with high reflection. By adjusting the filling ratio, chemical potential and rotation angle of the graphene grating, the shift of GH and IF can be controlled. The dependence of the spatial shifts on the TME and the degree of anisotropy of the TI are also discussed. Our results may provide new possibilities for applications of the TI with the TME.

Background Growing evidence has indicated that tumor biomarkers, including cytokeratin 19 fragment antigen 21–1 (Cyfra21–1), carbohydrate antigen 19–9 (CA19–9), carbohydrate antigen 72–4 (CA72–4), carcinoembryonic antigen (CEA) and squamous cell carcinoma antigen (SCC-Ag) were reported to be commonly used in diagnosis and prognosis in esophageal squamous cell carcinoma (ESCC). However, which is the best marker for predicting prognosis remains unknown. Few papers focused on the relationship between tumor biomarkers and postoperative treatment in ESCC. Methods A total of 416 ESCC patients were enrolled in this study. The association between tumor markers and overall survival (OS) was analyzed using Kaplan-Meier method with log-rank test, followed by multivariate Cox regression models. Results The results of Cox multivariate analysis indicated that among these tumor biomarkers, CA19–9 (≥ 37 vs. < 37) [hazard ratio (HR) = 2.130, 95% confidence interval (CI) = 1.138–3.986, p = 0.018] and CEA (≥ 5 vs. < 5) (HR = 1.827, 95% CI = 1.089–3.064, p = 0.022) were the independent prognostic factors of poor OS. For the ESCC patients with CA19–9 < 37, CEA < 5 or SCC-Ag < 1.5, the surgery plus postoperative chemotherapy group had a significantly longer OS than the surgery group alone ( p < 0.05), but this significant difference of OS between these two groups cannot be found in patients with CA19–9 ≥ 37, CEA ≥ 5 or SCC-Ag ≥ 1.5 ( p > 0.05). Conclusions CEA and CA19–9 maybe are superior to other tumor biomarkers as prognostic indicators in ESCC. CA19–9, CEA, SCC-Ag may be useful in predicting the therapeutic effect of postoperative chemotherapy in ESCC.