CsPbX3 -ITO (X = Cl, Br, I) Nano-Heterojunctions: Voltage Tuned Positive to Negative Photoresponse.

All-Inorganic perovskite CsPbX3 (X = Cl, Br, I) quantum dots (QDs) have attracted tremendous attention in the past few years for their appealing performance in optoelectronic applications. Major properties of CsPbX3 QDs include the positive photoconductivity (PPC) and the defect tolerance of the in-band trap states. Here it is reported that when hybridizing CsPbX3 QDs with indium tin oxide (ITO) nanocrystals to form CsPbX3 -ITO nano-heterojunctions (NHJs), a voltage tuned photoresponse-from PPC to negative photoconductivity (NPC) transform-is achieved in lateral drain-source structured ITO/CsPbX3 -ITO-NHJs/ITO devices. A model combining exciton, charge separation, transport, and most critical the voltage driven electron filling of the in-band trap states with drain-source voltage (VDS ) above a threshold, is proposed to understand this unusual PPC-NPC transform mechanism, which is different from that of any known nanomaterial system. This finding exhibits potentials for developing devices such as photodetectors, optoelectronic switches, and memories.

Heterostructural CsPbX3-PbS (X = Cl, Br, I) Quantum Dots with Tunable Vis-NIR Dual Emission.

Perovskite and chalcogenide quantum dots (QDs) are important nano semiconductors. It has been a challenge to synthesize heterostructural QDs combining perovskite and chalcogenide with tailorable photoelectronic properties. In this report, heterostructural CsPbX3-PbS (X = Cl, Br, I) QDs were successfully synthesized via a room temperature in situ transformation route. The CsPbX3-PbS QDs show a tunable dual emission feature with the visible and near-infrared (NIR) photoluminescence (PL) corresponding to CsPbX3 and PbS, respectively. Typically, the formation and evolution of the heterostructural CsPbBr3-PbS QDs with reaction time was investigated. Femtosecond transient absorption spectroscopy (TAS) was applied to illuminate the exciton dynamics in CsPbBr3-PbS QDs. The mild synthetic method and TAS proved perovskite to PbS energy transfer may pave the way toward highly efficient QD photovoltaic and optoelectronic devices.

Conformity behavior in crises: evidence from the COVID-19 epidemic in China.

Once a mass health crisis breaks out, it causes concern among whole societies. Thus, understanding the individual's behavior in response to such events is key in government crisis management. From the perspective of social influence theory, this study adopts the empirical research method to collect data information in February 2020 through online survey, with a view to comprehensively describe the individuals'conformity behavior during the COVID-19 outbreak in China. The individual's conformity behavior and new influencing factors were identified. The results revealed that affective risk perception, cognitive risk perception, and individual risk knowledge had a positive significant impact on normative influence. Affective risk perception and individual risk knowledge had a positive significant on informative influence. Cognitive risk perception did not significantly impact informative influence. Informative influence and normative influence had a positive effect on conformity behavior. These results have significant implications for the management behavior of the government.

The effects of back-projection variants in BPF-like TOF PET reconstruction using CNN filtration - Based on simulated and clinical brain data.

BACKGROUND: The back-projection strategies such as confidence weighting (CW) and most likely annihilation position (MLAP) have been adopted into back-projection-and-filtering-like (BPF-like) deep reconstruction model and shown great potential on fast and accurate PET reconstruction. Although the two methods degenerate to an identical model at the time resolution of 0 ps, they represent two distinct approaches at the realistic time resolutions of current commercial systems. There is a lack of a systematic and fair assessment on these differences. PURPOSE: This work aims to analyze the impact of back-projection variants on CNN-based PET image reconstruction to find the most effective back-projection model, and ultimately contribute to accurate PET reconstruction. METHODS: Different back-projection strategies (CW and MLAP) and different angular view processing methods (view-summed and view-grouped) were considered, leading to the comparison of four back-projection variants integrated with the same CNN filtration model. Meanwhile, we investigated two strategies of physical effect compensation, either introducing pre-corrected data as the input or adding a channel of attenuation map to the CNN model. After training models separately on Monte-Carlo-simulated BrainWeb phantoms with full dose (events = 3×107), we tested them on both simulated phantoms and clinical brain scans with two dosage levels. For the performance assessment, peak signal-to-noise ratio (PSNR) and root mean square error (RMSE) were used to evaluate the pixel-wise error, structural similarity index (SSIM) to evaluate the structural similarity, and contrast recovery coefficient (CRC) in manually selected ROI to compare the region recovery. RESULTS: Compared to two MLAP-based histo-image reconstruction models, two CW-based back-projected image methods produced clearer, sharper, and more detailed images, from both simulated and clinical data. For angular view processing methods, view-grouped histo-image improved image quality, while view-grouped cwbp-image showed no advantage except for contrast recovery. Quantitative analysis on simulated data demonstrated that the view-summed cwbp-image model achieved the best PSNR, RMSE, SSIM, while the 8-view cwbp-image model achieved the best CRC in lesions and the white matter. Additionally, the multi-channel input model including the back-projection image and attenuation map was proved to be the most efficient and simplest method for compensating for physical effects for brain data. Applying Gaussian blur to the histo-image yielded images with limited improvement. All above results hold for both the half-dose and the full-dose cases. CONCLUSION: For brain imaging, the evaluation based on metrics PSNR, RMSE, SSIM, and CRC indicates that the view-summed CW-based back-projection variant is the most effective input for the BPF-like reconstruction model using CNN filtration, which can involve the attenuation map through an additional channel to effectively compensate for physical effects.

The effect of overall parental satisfaction on Chinese students' learning engagement: Role of student anxiety and educational implications.

Learning engagement is one of the important research contents of learning psychology. The level of learning engagement directly affects students' academic performance and future development. Based on the survey data of primary and secondary school parents and students collated at the beginning of 2019, control factors such as students' gender, school location, parents' education level, total annual family income, parental rearing methods, etc. The study found that parental overall satisfaction can significantly and positively predict students' learning engagement. Mediation effect analysis found that students' anxiety completely mediated the effect on parental overall satisfaction and students' learning engagement. Cultivate good parent-child relationships; Establish positive teacher-student relationships; Build a harmonious relationship with classmates. Families and schools should work together to create an atmosphere conducive to the healthy growth of students.

Nucleation-mediated growth of chiral 3D organic-inorganic perovskite single crystals.

Although their zero- to two-dimensional counterparts are well known, three-dimensional chiral hybrid organic-inorganic perovskite single crystals have remained difficult because they contain no chiral components and their crystal phases belong to centrosymmetric achiral point groups. Here we report a general approach to grow single-crystalline 3D lead halide perovskites with chiroptical activity. Taking MAPbBr3 (MA, methylammonium) perovskite as a representative example, whereas achiral MAPbBr3 crystallized from precursors in solution by inverse temperature crystallization method, the addition of micro- or nanoparticles as nucleating agents promoted the formation of chiral crystals under a near equilibrium state. Experimental characterization supported by calculations showed that the chirality of the 3D APbX3 (where A is an ammonium ion and X is Cl, Br or mixed Cl-Br or Br-I) perovskites arises from chiral patterns of the A-site cations and their interaction with the [PbX6]4- octahedra in the perovskite structure. The chiral structure obeys the lowest-energy principle and thereby thermodynamically stable. The chiral 3D hybrid organic-inorganic perovskites served in a circularly polarized light photodetector prototype successfully.

Parents' Educational Anxiety Under the "Double Reduction" Policy Based on the Family and Students' Personal Factors.

Introduction: Educational anxiety is a true portrayal of Chinese parents. As an endogenous problem in the development of modern education system, the pressure of students' entrance examination continues to rise. Methods: Based on the empirical survey data of education in 3298 families, this study uses the analysis method of multiple linear regressions to analyze the influencing factors of parental education anxiety and finds that educational anxiety has become a common social emotion. Results: After the regression analysis of the four-layer model was established, it was found that different school segments, school priorities, and student achievements had a significant impact on parents' educational anxiety, and factors such as annual family income, educational satisfaction and family parenting style had a significant impact on parental educational anxiety. It is worth noting that the gap in parental expectations has a significant positive effect on educational anxiety, and the greater the expectation gap, the higher the educational anxiety of parents. Discussion: Therefore, it is suggested that governments at all levels should conscientiously implement the task of "reducing burdens" and rationally allocate high-quality educational resources; parents and teachers should start from the needs of children to establish a scientific concept of education and the concept of becoming a talent; the whole society should put forward reasonable educational expectations in education, and be good at adjusting expectations on time so that education can return to a rational natural growth state and eliminate parents' educational anxiety.

UV-vis-IR Broad Spectral Photodetectors Based on VO2-ZnO Nanocrystal Films.

As a narrow band semiconductor at room temperature and a metallic material above ∼68 °C, functional VO2 films are widely investigated for smart windows, whereas their potential for ultraviolet-visible-infrared (UV-vis-IR) broad spectral photodetectors has not been efficiently studied. In this report, photodetectors based on VO2-ZnO nanocrystal composite films were prepared by nanocrystal-mist (NC-mist) deposition. An enhanced photodetection switching ratio was achieved covering the ultraviolet to infrared wavelength. Due to the synergetic effect of nanosize, surface, phase transition, percolation threshold, and the band structure of the heterojunction, the transfer and transport of photogenerated carriers modulate the device performance. This study probes new chances of applying VO2-semiconductor-based nanocomposites for broad spectral photodetectors.

A back-projection-and-filtering-like (BPF-like) reconstruction method with the deep learning filtration from listmode data in TOF-PET.

PURPOSE: The time-of-flight (TOF) information improves signal-to-noise ratio (SNR) for positron emission tomography (PET) imaging. Existing analytical algorithms for TOF PET usually follow a filtered back-projection process on reconstructing images from the sinogram data. This work aims to develop a back-projection-and-filtering-like (BPF-like) algorithm that reconstructs the TOF PET image directly from listmode data rapidly. METHODS: We extended the 2D conventional non-TOF PET projection model to a TOF case, where projection data are represented as line integrals weighted by the one-dimensional TOF kernel along the projection direction. After deriving the central slice theorem and the TOF back-projection of listmode data, we designed a deep learning network with a modified U-net architecture to perform the spatial filtration (reconstruction filter). The proposed BP-Net method was validated via Monte Carlo simulations of TOF PET listmode data with three different time resolutions for two types of activity phantoms. The network was only trained on the simulated full-dose XCAT dataset and then evaluated on XCAT and Jaszczak data with different time resolutions and dose levels. RESULTS: Reconstructed images show that when compared with the conventional BPF algorithm and the MLEM algorithm proposed for TOF PET, the proposed BP-Net method obtains better image quality in terms of peak signal-to-noise ratio, relative mean square error, and structure similarity index; besides, the reconstruction speed of the BP-Net is 1.75 times faster than BPF and 29.05 times faster than MLEM using 15 iterations. The results also indicate that the performance of the BP-Net degrades with worse time resolutions and lower tracer doses, but degrades less than BPF or MLEM reconstructions. CONCLUSION: In this work, we developed an analytical-like reconstruction in the form of BPF with the reconstruction filtering operation performed via a deep network. The method runs even faster than the conventional BPF algorithm and provides accurate reconstructions from listmode data in TOF-PET, free of rebinning data to a sinogram.

4D-AirNet: a temporally-resolved CBCT slice reconstruction method synergizing analytical and iterative method with deep learning.

Four-dimensional (4D) cone-beam CT (CBCT) reconstructs temporally-resolved phases of 3D volumes often with the same amount of projection data that are meant for reconstructing a single 3D volume. 4D CBCT is a sparse-data problem that is very challenging for high-quality 4D CBCT image reconstruction. Here we develop a new method, namely 4D-AirNet, that synergizes analytical and iterative method with deep learning for high-quality temporally-resolved CBCT slice reconstruction. 4D-AirNet is an unrolling method using the optimization framework of fused analytical and iterative reconstruction (AIR), which is based on proximal forward-backward splitting (PFBS). Three different strategies are developed for 4D-AirNet: random-phase (RP), prior-guided (PG), and all-phase (AP). RP-AirNet and PG-AirNet utilize phase-by-phase training and reconstruction, while PG-AirNet also uses a prior image reconstructed with all-phase projection data. Dense connectivity is built into 4D-AirNet networks for improved reconstruction quality. In contrast, AP-AirNet trains and reconstructs all phases simultaneously. In addition, the joint regularization method of DL and conventional spatiotemporal total variation (TV) is investigated. 4D-AirNet methods were evaluated in comparison with conventional iterative (TV) and deep learning (LEARN) methods, using simulated 2D-t CBCT scans from a lung dataset with various sparse-data levels. The reconstruction results suggest 4D-AirNet methods outperform TV and LEARN, and AP-AirNet provides the best reconstruction quality overall.

Low-dose CT with deep learning regularization via proximal forward-backward splitting.

Low-dose x-ray computed tomography (LDCT) is desirable for reduced patient dose. This work develops new image reconstruction methods with deep learning (DL) regularization for LDCT. Our methods are based on the unrolling of a proximal forward-backward splitting (PFBS) framework with data-driven image regularization via deep neural networks. In contrast to PFBS-IR, which utilizes standard data fidelity updates via an iterative reconstruction (IR) method, PFBS-AIR involves preconditioned data fidelity updates that fuse the analytical reconstruction (AR) and IR methods in a synergistic way, i.e. fused analytical and iterative reconstruction (AIR). The results suggest that the DL-regularized methods (PFBS-IR and PFBS-AIR) provide better reconstruction quality compared to conventional methods (AR or IR). In addition, owing to the AIR, PFBS-AIR noticeably outperformed PFBS-IR and another DL-based postprocessing method, FBPConvNet.

Continuous preparation of antimony nanocrystals with near infrared photothermal property by pulsed laser ablation in liquids.

Antimony nanocrystals (Sb NCs) are of interest in energy storage, catalysis and cancer therapy for its special physical, chemical and biomedical properties. However, methodology challenges still remain in preparation of colloidal Sb NCs, due to the restricted reaction solution systems, high temperature and time costing for common routes. Herein, size controllable colloidal Sb NCs were continuously prepared by pulsed laser ablation of Sb target in different solvents, owning to the metal nanodroplet explosive ejection and thermal evaporation mechanisms. These well dispersed and stable Sb NCs showed excellent photothermal property in the near-infrared-II window.

Design study of fully wearable high-performance brain PETs for neuroimaging in free movement.

A practical wearable brain PET scanner capable of dynamic neuroimaging during free bodily movement will enable potential breakthrough basic neuroscience studies and help develop imaging-based neurological diagnoses and treatments. Weight, brain coverage, and sensitivity are three fundamental technical obstacles in the development of Fully Wearable High-Performance (FWHP) brain PET scanners. The purpose of this study is to investigate the feasibility of building a FWHP brain PET using a limited volume of lutetium-yttrium oxyorthosilicate (LYSO) scintillator crystals. Six scanners, consisted of the same volume (2.66 kg) of LYSO scintillators with combinations of 2 different crystal pitches (3 mm and 1.5 mm) and 3 different crystal lengths (20 mm, 10 mm, and 5 mm), were simulated. The performances of the six scanners were assessed and compared with Siemen's HRRT brain PET and mCT whole-body PET, in terms of aperture, axial field of views (AFOV), sensitivity, spatial resolution, count rates, and image noise property. The time-of-flight (TOF) information was included in the image reconstruction to improve the effective sensitivity. The effects of the TOF was assessed by scanning a Jaszczak phantom and reconstructing images with the maximum likelihood expectation maximization (MLEM) algorithm with different timing settings (non-TOF, 500 ps, 200 ps, 100 ps and 50 ps Coincidence Time Resolution, CTR). The signal-noise ratio (SNR) of the images were assessed and compared with those of the HRRT scanner and mCT scanner. The results show that it is possible to construct a FWHP brain PET with better spatial resolution than the dedicated HRRT brain PET, comparable effective sensitivity (with 50 ∼ 100 ps CTR), and whole-brain coverage (23.7 cm inner diameter and 13.4 cm axial field of view) using 2.66 kg of LYSO.

AirNet: Fused analytical and iterative reconstruction with deep neural network regularization for sparse-data CT.

PURPOSE: Sparse-data computed tomography (CT) frequently occurs, such as breast tomosynthesis, C-arm CT, on-board four-dimensional cone-beam CT (4D CBCT), and industrial CT. However, sparse-data image reconstruction remains challenging due to highly undersampled data. This work develops a data-driven image reconstruction method for sparse-data CT using deep neural networks (DNN). METHODS: The new method so-called AirNet is designed to incorporate the benefits from analytical reconstruction method (AR), iterative reconstruction method (IR), and DNN. It is built upon fused analytical and iterative reconstruction (AIR) that synergizes AR and IR via the optimization framework of modified proximal forward-backward splitting (PFBS). By unrolling PFBS into IR updates of CT data fidelity and DNN regularization with residual learning, AirNet utilizes AR such as FBP during the data fidelity, introduces dense connectivity into DNN regularization, and learns PFBS coefficients and DNN parameters that minimize the loss function during the training stage; and then AirNet with trained parameters can be used for end-to-end image reconstruction. RESULTS: A CT atlas of 100 prostate scans was used to validate the AirNet in comparison with state-of-art DNN-based postprocessing and image reconstruction methods. The validation loss in AirNet had the fastest decreasing rate, owing to inherited fast convergence from AIR. AirNet was robust to noise in projection data and content differences between the training set and the images to be reconstructed. The impact of image quality on radiotherapy treatment planning was evaluated for both photon and proton therapy, and AirNet achieved the best treatment plan quality, especially for proton therapy. For example, with limited-angle data, the maximal target dose for AirNet was 109.5% in comparison with the ground truth 109.1%, while it was significantly elevated to 115.1% and 128.1% for FBPConvNet and LEARN, respectively. CONCLUSIONS: A new image reconstruction AirNet is developed for sparse-data CT image reconstruction. AirNet achieved the best image reconstruction quality both visually and quantitatively among all methods under comparison for all sparse-data scenarios (sparse-view and limited-angle), and provided the best photon and proton treatment plan quality based on sparse-data CT.

Developing a 'multiPatchPET' system in GATE for a PET system design with irregular geometries.

This work modified the commonly used Monte Carlo tool package GATE by developing a new 'multiPatchPET' system so that GATE users can easily simulate PET systems with irregular geometries. The motivation was to design a brain PET scanner with high sensitivity. It is known that compact PET scanners with a large solid coverage angle can achieve high sensitivity with fewer scintillation detectors, and thus have the potential to provide better image quality in brain PET imaging than conventional ring PET scanners. However, considering a straightforward example with the largest possible solid angle, a spherical PET scanner is hard to manufacture. A more practical alternative would be a sphere-like polyhedral PET scanner with flat detector patches. Moreover, when monolithic scintillators are chosen to construct these flat detector modules, detection efficiency is further improved. Thus, we plan to design a sphere-like polyhedral PET scanner made up of monolithic scintillators. Unfortunately, in our design study, we found that simulating such a scanner with the latest GATE version (8.0) was not trivial, since no predefined systems could be used. In this work we introduced a 'multiPatchPET' system to GATE, which we and other GATE users will be able to use to develop PET scanners with any irregular geometry and any shape of patch. To validate our modification, a single block detector and an mCT scanner were simulated via both the original 'ecat' system and the new 'multiPatchPET' system. The results show no difference in terms of the detecting efficiency and reconstruction image. Then we used the 'multiPatchPET' system to simulate an 86 surface polyhedral brain PET scanner. Compared with two cylindrical brain scanners, the polyhedral brain scanner shows a higher sensitivity and has fewer noisy images. Thus, it was proved that our modification, which is accessible to the nuclear imaging research community, equipped GATE with a powerful and user-friendly tool to simulate complex scanners with irregular patches easily.

Simulation study of a high-performance brain PET system with dodecahedral geometry.

PURPOSE: In brain imaging, the spherical PET system achieves the highest sensitivity when the solid angle is concerned. However, it is not practical. In this work, we designed an alternative sphere-like scanner, the dodecahedral scanner, which has a high sensitivity in imaging and a high feasibility to manufacture. We simulated this system and compared the performance with a few other dedicated brain PET systems. METHODS: Monte Carlo simulations were conducted to generate data of the dedicated brain PET system with the dodecahedral geometry (11 regular pentagon detectors). The data were then reconstructed using the in-house developed software with the fully three-dimensional maximum-likelihood expectation maximization (3D-MLEM) algorithm. RESULTS: Results show that the proposed system has a high-sensitivity distribution for the whole field of view (FOV). With a depth-of-interaction (DOI) resolution around 6.67 mm, the proposed system achieves the spatial resolution of 1.98 mm. Our simulation study also shows that the proposed system improves the image contrast and reduces noise compared with a few other dedicated brain PET systems. Finally, simulations with the Hoffman phantom show the potential application of the proposed system in clinical applications. CONCLUSIONS: In conclusion, the proposed dodecahedral PET system is potential for widespread applications in high-sensitivity, high-resolution PET imaging, to lower the injected dose.