Ag-modified enhance the performances of ZnO@CFs based omnidirectional photoelectrochemical ultraviolet detectors.

There are several prospective applications for omnidirectional ultraviolet (UV) detectors and underwater detection detectors in optical systems and optical fields. In this work, ZnO nanorods arrays were grown on carbon fibers (CFs). An appropriate amount of Ag nanoparticles (NPs) was deposited on the surface of ZnO nanorods by photochemical deposition. This improved the performance of photoelectrochemical (PEC) based UV detectors. Under 365 nm and 10 mW cm-2UV irradiation, the photocurrent density of the 30s-Ag/ZnO@CFs based PEC UV detector can reach 1.28 mA cm-2, which is about 7 times that of the ZnO@CFs based PEC UV detector, and the rising time is shortened from 0.17 to 0.10 s. The reason is that increased absorption of ultraviolet light induced by the localized surface plasmon resonance. In addition, the detector exhibits a good flexibility and remains flexible after hundreds of bends and twists. Moreover, the detector is responsive in the range of rotation angle from 0° to 360°. It provides an insight to improve the photoelectric performance and underwater omnidirectional detection ability of the PEC UV detector.

MoViT: Memorizing Vision Transformers for Medical Image Analysis.

The synergy of long-range dependencies from transformers and local representations of image content from convolutional neural networks (CNNs) has led to advanced architectures and increased performance for various medical image analysis tasks due to their complementary benefits. However, compared with CNNs, transformers require considerably more training data, due to a larger number of parameters and an absence of inductive bias. The need for increasingly large datasets continues to be problematic, particularly in the context of medical imaging, where both annotation efforts and data protection result in limited data availability. In this work, inspired by the human decision-making process of correlating new "evidence" with previously memorized "experience", we propose a Memorizing Vision Transformer (MoViT) to alleviate the need for large-scale datasets to successfully train and deploy transformer-based architectures. MoViT leverages an external memory structure to cache history attention snapshots during the training stage. To prevent overfitting, we incorporate an innovative memory update scheme, attention temporal moving average, to update the stored external memories with the historical moving average. For inference speedup, we design a prototypical attention learning method to distill the external memory into smaller representative subsets. We evaluate our method on a public histology image dataset and an in-house MRI dataset, demonstrating that MoViT applied to varied medical image analysis tasks, can outperform vanilla transformer models across varied data regimes, especially in cases where only a small amount of annotated data is available. More importantly, MoViT can reach a competitive performance of ViT with only 3.0% of the training data. In conclusion, MoViT provides a simple plug-in for transformer architectures which may contribute to reducing the training data needed to achieve acceptable models for a broad range of medical image analysis tasks.

Boosting Charge Transport and Catalytic Performance in MoS2 by Zn2+ Intercalation Engineering for Lithium-Sulfur Batteries.

Transition metal dichalcogenides (TMDs) have been widely studied as catalysts for lithium-sulfur batteries due to their good catalytic properties. However, their poor electronic conductivity leads to slow sulfur reduction reactions. Herein, a simple Zn2+ intercalation strategy was proposed to promote the phase transition from semiconducting 2H-phase to metallic 1T-phase of MoS2. Furthermore, the Zn2+ between layers can expand the interlayer spacing of MoS2 and serve as a charge transfer bridge to promote longitudinal transport along the c-axis of electrons. DFT calculations further prove that Zn-MoS2 possesses better charge transfer ability and stronger adsorption capacity. At the same time, Zn-MoS2 exhibits excellent redox electrocatalytic performance for the conversion and decomposition of polysulfides. As expected, the lithium-sulfur battery using Zn0.12MoS2-carbon nanofibers (CNFs) as the cathode has high specific capacity (1325 mAh g-1 at 0.1 C), excellent rate performance (698 mAh g-1 at 3 C), and outstanding cycle performance (it remains 604 mAh g-1 after 700 cycles with a decay rate of 0.045% per cycle). This study provides valuable insights for improving electrocatalytic performance of lithium-sulfur batteries.

ReconFormer: Accelerated MRI Reconstruction Using Recurrent Transformer.

The accelerating magnetic resonance imaging (MRI) reconstruction process is a challenging ill-posed inverse problem due to the excessive under-sampling operation in k -space. In this paper, we propose a recurrent Transformer model, namely ReconFormer, for MRI reconstruction, which can iteratively reconstruct high-fidelity magnetic resonance images from highly under-sampled k -space data (e.g., up to 8× acceleration). In particular, the proposed architecture is built upon Recurrent Pyramid Transformer Layers (RPTLs). The core design of the proposed method is Recurrent Scale-wise Attention (RSA), which jointly exploits intrinsic multi-scale information at every architecture unit as well as the dependencies of the deep feature correlation through recurrent states. Moreover, benefiting from its recurrent nature, ReconFormer is lightweight compared to other baselines and only contains 1.1 M trainable parameters. We validate the effectiveness of ReconFormer on multiple datasets with different magnetic resonance sequences and show that it achieves significant improvements over the state-of-the-art methods with better parameter efficiency. The implementation code and pre-trained weights are available at https://github.com/guopengf/ReconFormer.

CEST and nuclear Overhauser enhancement imaging with deep learning-extrapolated semisolid magnetization transfer reference: Scan-rescan reproducibility and reliability studies.

PURPOSE: To develop a novel MR physics-driven, deep-learning, extrapolated semisolid magnetization transfer reference (DeepEMR) framework to provide fast, reliable magnetization transfer contrast (MTC) and CEST signal estimations, and to determine the reproducibility and reliability of the estimates from the DeepEMR. METHODS: A neural network was designed to predict a direct water saturation and MTC-dominated signal at a certain CEST frequency offset using a few high-frequency offset features in the Z-spectrum. The accuracy, scan-rescan reproducibility, and reliability of MTC, CEST, and relayed nuclear Overhauser enhancement (rNOE) signals estimated from the DeepEMR were evaluated on numerical phantoms and in heathy volunteers at 3 T. In addition, we applied the DeepEMR method to brain tumor patients and compared tissue contrast with other CEST calculation metrics. RESULTS: The DeepEMR method demonstrated a high degree of accuracy in the estimation of reference MTC signals at ±3.5 ppm for APT and rNOE imaging, and computational efficiency (˜190-fold) compared with a conventional fitting approach. In addition, the DeepEMR method achieved high reproducibility and reliability (intraclass correlation coefficient = 0.97, intersubject coefficient of variation = 3.5%, and intrasubject coefficient of variation = 1.3%) of the estimation of MTC signals at ±3.5 ppm. In tumor patients, DeepEMR-based amide proton transfer images provided higher tumor contrast than a conventional MT ratio asymmetry image, particularly at higher B1 strengths (>1.5 µT), with a distinct delineation of the tumor core from normal tissue or peritumoral edema. CONCLUSION: The DeepEMR approach is feasible for measuring clean APT and rNOE effects in longitudinal and cross-sectional studies with low scan-rescan variability.

Construction of urchin-like bimetallic phosphides induced by carbon dots for efficient wide pH hydrogen production.

The development of an efficient noble-metal-free and pH-universal electrocatalyst for the hydrogen evolution reaction (HER) would be highly significant for hydrogen (H2) production via electrocatalytic water splitting. However, developing such a catalyst remains a formidable task. Herein, a strategy is proposed for the in situ fabrication of a novel urchin-like NiCoP microsphere catalyst (0.5CDs-NiCoP/NF) on nickel foam (NF) using carbon dots (CDs) as a directing agent. The strong bonding between the CDs and metals provides additional active sites, giving 0.5CDs-NiCoP/NF excellent electrocatalytic hydrogen evolution performance in environments ranging from acidic to basic. Moreover, the unique structure of 0.5CDs-NiCoP/NF endows this catalyst with low Tafel slopes of 73, 146 and 74 mV dec-1 for HER in acidic, neutral and alkaline conditions, respectively. This performance exceeds that of numerous other reported non-precious HER catalysts. In summary, this work offers a novel and efficient strategy for the design and synthesis of low-cost, efficient, and robust transition metal phosphides (TMPs) electrocatalysts.

Bloch simulator-driven deep recurrent neural network for magnetization transfer contrast MR fingerprinting and CEST imaging.

PURPOSE: To develop a unified deep-learning framework by combining an ultrafast Bloch simulator and a semisolid macromolecular magnetization transfer contrast (MTC) MR fingerprinting (MRF) reconstruction for estimation of MTC effects. METHODS: The Bloch simulator and MRF reconstruction architectures were designed with recurrent neural networks and convolutional neural networks, evaluated with numerical phantoms with known ground truths and cross-linked bovine serum albumin phantoms, and demonstrated in the brain of healthy volunteers at 3 T. In addition, the inherent magnetization-transfer ratio asymmetry effect was evaluated in MTC-MRF, CEST, and relayed nuclear Overhauser enhancement imaging. A test-retest study was performed to evaluate the repeatability of MTC parameters, CEST, and relayed nuclear Overhauser enhancement signals estimated by the unified deep-learning framework. RESULTS: Compared with a conventional Bloch simulation, the deep Bloch simulator for generation of the MTC-MRF dictionary or a training data set reduced the computation time by 181-fold, without compromising MRF profile accuracy. The recurrent neural network-based MRF reconstruction outperformed existing methods in terms of reconstruction accuracy and noise robustness. Using the proposed MTC-MRF framework for tissue-parameter quantification, the test-retest study showed a high degree of repeatability in which the coefficients of variance were less than 7% for all tissue parameters. CONCLUSION: Bloch simulator-driven, deep-learning MTC-MRF can provide robust and repeatable multiple-tissue parameter quantification in a clinically feasible scan time on a 3T scanner.

CEST2022: Amide proton transfer-weighted MRI improves the diagnostic performance of multiparametric non-contrast-enhanced MRI techniques in patients with post-treatment high-grade gliomas.

New or enlarged lesions in malignant gliomas after surgery and chemoradiation can be associated with tumor recurrence or treatment effect. Due to similar radiographic characteristics, conventional-and even some advanced MRI techniques-are limited in distinguishing these two pathologies. Amide proton transfer-weighted (APTw) MRI, a protein-based molecular imaging technique that does not require the administration of any exogenous contrast agent, was recently introduced into the clinical setting. In this study, we evaluated and compared the diagnostic performances of APTw MRI with several non-contrast-enhanced MRI sequences, such as diffusion-weighted imaging, susceptibility-weighted imaging, and pseudo-continuous arterial spin labeling. Thirty-nine scans from 28 glioma patients were obtained on a 3 T MRI scanner. A histogram analysis approach was employed to extract parameters from each tumor area. Statistically significant parameters (P < 0.05) were selected to train multivariate logistic regression models to evaluate the performance of MRI sequences. Multiple histogram parameters, particularly from APTw and pseudo-continuous arterial spin labeling images, demonstrated significant differences between treatment effect and recurrent tumor. The regression model trained on the combination of all significant histogram parameters achieved the best result (area under the curve = 0.89). We found that APTw images added value to other advanced MR images for the differentiation of treatment effect and tumor recurrence.

Amide proton transfer-weighted MRI features of acute ischemic stroke subtypes.

Stroke is a highly heterogeneous disorder with distinct subtypes, and the stroke subtype influences the outcome. Amide proton transfer-weighted (APTW) MRI has been demonstrated to be promising in stroke patients, but the image characteristics of stroke subtypes have not been sufficiently investigated. The purpose of this study was to investigate the APTW MRI features of different subtypes of acute ischemic stroke (AIS). Ninety-two AIS patients presenting within 96 h of symptom onset were enrolled and examined with a 3.0-T MRI system. Patients were grouped into four subtypes: lacunar circulation infarcts (LACI, n = 33); total anterior circulation infarcts (TACI, n = 9); partial anterior circulation infarcts (PACI, n = 28); and posterior circulation infarcts (POCI, n = 22). APTW values in the lesion (APTWlesion ) and the contralateral normal-appearing region (APTWcontral ) were measured. The change in APTW values between the acute ischemic lesion and the contralateral normal-appearing region (APTWles-con ) was calculated. A two-sample t-test, one-way ANOVA, and the Chi-square method were used. There were significant differences between APTWlesion and APTWcontral in the three categories of nonlacunar strokes (TACI, PACI, and POCI, all p < 0.01), but not for lacunar strokes (LACI, p = 0.080). TACI patients had the lowest APTWlesion and APTWles-con in all groups (p < 0.05). In the POCI group, patients with supratentorial infarcts showed significant differences between APTWlesion and APTWcontral (p = 0.001), while the differences were not significant for infratentorial infarcts (p = 0.135). Our results suggest that the APT effect was heterogeneous in different stroke subtypes, and that APTW MRI gave an excellent performence in depicting nonlacunar AIS in supratentorial locations.

Ti3 C2 TX MXene Ink Direct Writing Flexible Sensors for Disposable Paper Toys.

Flexible electronics have gained great attention in recent years owing to their promising applications in biomedicine, sustainable energy, human-machine interaction, and toys for children. Paper mainly produced from cellulose fibers is attractive substrate for flexible electronics because it is biodegradable, foldable, tailorable, and light-weight. Inspired by daily handwriting, the rapid prototyping of sensing devices with arbitrary patterns can be achieved by directly drawing conductive inks on flat or curved paper surfaces; this provides huge freedom for children to design and integrate "do-it-yourself (DIY)" electronic toys. Herein, viscous and additive-free ink made from Ti3 C2 TX MXene sediment is employed to prepare disposable paper electronics through a simple ball pen drawing. The as-drawn paper sensors possess hierarchical microstructures with interweaving nanosheets, nanoflakes, and nanoparticles, therefore exhibiting superior mechanosensing performances to those based on single/fewer-layer MXene nanosheets. As proof-of-concept applications, several popular children's games are implemented by the MXene-based paper sensors, including "You say, I guess," "Emotional expression," "Rock-Paper-Scissors," "Arm wrestling," "Throwing game," "Carrot squat," and "Grab the cup," as well as a DIY smart whisker for a cartoon mouse. Moreover, MXene-based paper sensors are safe and disposable, free from producing any e-waste and hazard to the environment.

Enhancement of Light and X-ray Charging in Persistent Luminescence Nanoparticle Scintillators Zn2SiO4:Mn2+, Yb3+, Li.

Persistent luminescence nanoparticle scintillators (PLNS) have been attempted for X-ray-induced photodynamic therapy (X-PDT) because persistent luminescence after ceasing radiation can make PLNS use less cumulative irradiation time and dose to generate the same amount of reactive oxygen species (ROS) compared with conventional scintillators to combat cancer cells. However, excessive surface defects in PLNS reduce the luminescence efficiency and quench the persistent luminescence, which is fatal to the efficacy of X-PDT. Herein, the PLNS of SiO2@Zn2SiO4:Mn2+, Yb3+, Li+ was designed by the energy trap engineering and synthesized by a simple template method, which has excellent X-ray and UV-excited persistent luminescence and continuously tunable emission spectra from 520 to 550 nm. Its luminescence intensity and afterglow time are more than 7 times that of the reported Zn2SiO4:Mn2+ used for X-PDT. By loading a Rose Bengal (RB) photosensitizer, an effective persistent energy transfer from the PLNS to photosensitizer is observed even after the removal of X-ray irradiation. The X-ray dose of nanoplatform SiO2@Zn2SiO4:Mn2+, Yb3+, Li+@RB in X-PDT of HeLa cancer cells was reduced to 0.18 Gy compared to the X-ray dose of 1.0 Gy for Zn2SiO4:Mn for X-PDT. This indicates that the Zn2SiO4:Mn2+, Yb3+, Li+ PLNS have great potential for X-PDT applications.

Applications of chemical exchange saturation transfer magnetic resonance imaging in identifying genetic markers in gliomas.

Chemical exchange saturation transfer (CEST) imaging is an important molecular magnetic resonance imaging technique that can image numerous low-concentration biomolecules with water-exchangeable protons (such as cellular proteins) and tissue pH. CEST, or more specially amide proton transfer-weighted imaging, has been widely used for the detection, diagnosis, and response assessment of brain tumors, and its feasibility in identifying molecular markers in gliomas has also been explored in recent years. In this paper, after briefing on the basic principles and quantification methods of CEST imaging, we review its early applications in identifying isocitrate dehydrogenase mutation status, MGMT methylation status, 1p/19q deletion status, and H3K27M mutation status in gliomas. Finally, we discuss the limitations or weaknesses in these studies.

Acquisition sequences and reconstruction methods for fast chemical exchange saturation transfer imaging.

Chemical exchange saturation transfer (CEST) imaging is an emerging molecular magnetic resonance imaging (MRI) technique that has been developed and employed in numerous diseases. Based on the unique saturation transfer principle, a family of CEST-detectable biomolecules in vivo have been found capable of providing valuable diagnostic information. However, CEST MRI needs a relatively long scan time due to the common long saturation labeling module and typical acquisition of multiple frequency offsets and signal averages, limiting its widespread clinical applications. So far, a plethora of imaging schemes and techniques has been developed to accelerate CEST MRI. In this review, the key acquisition and reconstruction methods for fast CEST imaging are summarized from a practical and systematic point of view. The first acquisition sequence section describes the major development of saturation schemes, readout patterns, ultrafast z-spectroscopy, and saturation-editing techniques for rapid CEST imaging. The second reconstruction method section lists the important advances of parallel imaging, compressed sensing, sparsity in the z-spectrum, and algorithms beyond the Fourier transform for speeding up CEST MRI.

Amide Proton Transfer-Weighted Magnetic Resonance Imaging for Detecting Severity and Predicting Outcome after Traumatic Brain Injury in Rats.

After traumatic brain injury (TBI), early assessment of secondary injury severity is critically important for estimating prognosis and treatment stratification. Currently, secondary injury severity is difficult to estimate. The objective of this study was to investigate the capacity of non-invasive amide proton transfer-weighted (APTw) magnetic resonance imaging (MRI) techniques to assess TBI injury in different brain regions and predict long-term neurobehavior outcomes. Fifty-five male and female rats were subjected to a controlled cortical impact with one of three different impactor depths to produce different degrees of TBI. Multi-parameter MRI data were acquired on a 4.7-Tesla scanner at 1 h, 1 day, and 3 days. Immunofluorescence staining was used to detect activated microglia at 3 days, and neurobehavioral tests were performed to assess long-term outcomes after 28 days. The APTw signal in the injury core at 1 day correlated with deficits in sensorimotor function, the sucrose preference test (a test for anhedonia), and spatial memory function on the Barnes maze. The APTw signal in the perilesion ipsilateral cortex gradually increased after TBI, and the value at 3 days correlated with microglia density at 3 days and with spatial memory decline and anhedonia at 28 days. The correlation between APTw and activated microglia was also observed in the ipsilateral thalamus, and its correlation to memory deficit and depression was evident in other ipsilateral sites. These results suggest that APTw imaging can be used for detecting secondary injury and as a potential predictor of long-term outcomes from TBI.

Review and consensus recommendations on clinical APT-weighted imaging approaches at 3T: Application to brain tumors.

Amide proton transfer-weighted (APTw) MR imaging shows promise as a biomarker of brain tumor status. Currently used APTw MRI pulse sequences and protocols vary substantially among different institutes, and there are no agreed-on standards in the imaging community. Therefore, the results acquired from different research centers are difficult to compare, which hampers uniform clinical application and interpretation. This paper reviews current clinical APTw imaging approaches and provides a rationale for optimized APTw brain tumor imaging at 3 T, including specific recommendations for pulse sequences, acquisition protocols, and data processing methods. We expect that these consensus recommendations will become the first broadly accepted guidelines for APTw imaging of brain tumors on 3 T MRI systems from different vendors. This will allow more medical centers to use the same or comparable APTw MRI techniques for the detection, characterization, and monitoring of brain tumors, enabling multi-center trials in larger patient cohorts and, ultimately, routine clinical use.

Design of Vertically Aligned Two-Dimensional Heterostructures of Rigid Ti3C2TX MXene and Pliable Vanadium Pentoxide for Efficient Lithium Ion Storage.

Designing a thick electrode with appropriate mass loading is a prerequisite toward practical applications for lithium ion batteries (LIBs) yet suffers severe limitations of slow electron/ion transport, unavoidable volume expansion, and the involvement of inactive additives, which lead to compromised output capacity, poor rate perforamnce, and cycling instability. Herein, self-supported thick electrode composed of vertically aligned two-dimensional (2D) heterostructures (V-MXene/V2O5) of rigid Ti3C2TX MXene and pliable vanadium pentoxide are assembled via an ice crystallization-induced strategy. The vertical channels prompt fast electron/ion transport within the entire electrode; in the meantime, the 3D MXene scaffold provides mechanical robustness during lithiation/delithiation. The optimized electrodes with 1 and 5 mg cm-2 of V-MXene/V2O5 respectively deliver 472 and 300 mAh g-1 at a current density of 0.2 A g-1, rate performance with 380 and 222 mAh g-1 retained at 5 A g-1, and reliability over 800 charge/discharge cycles.

2D material-based peroxidase-mimicking nanozymes: catalytic mechanisms and bioapplications.

The boom in nanotechnology brings new insights into the development of artificial enzymes (nanozymes) with ease of modification, lower manufacturing cost, and higher catalytic stability than natural enzymes. Among various nanomaterials, two-dimensional (2D) nanomaterials exhibit promising enzyme-like properties for a plethora of bioapplications owing to their unique physicochemical characteristics of tuneable composition, ultrathin thickness, and huge specific surface area. Herein, we review the recent advances in several 2D material-based nanozymes, such as carbonaceous nanosheets, metal-organic frameworks (MOFs), transition metal dichalcogenides (TMDs), layered double hydroxides (LDHs), and transition metal oxides (TMOs), clarify the mechanisms of peroxidase (POD)-mimicking catalytic behaviors, and overview the potential bioapplications of 2D nanozymes.

Universal Strategy for Preparing Highly Stable PBA/Ti3C2Tx MXene toward Lithium-Ion Batteries via Chemical Transformation.

Prussian blue analogues (PBAs) are believed to be intriguing anode materials for Li+ storage because of their tunable composition, designable topologies, and tailorable porous structures, yet they suffer from severe capacity decay and inferior cycling stability due to the volume variation upon lithiation and high electrical resistance. Herein, we develop a universal strategy for synthesizing small PBA nanoparticles hosted on two-dimensional (2D) MXene or rGO (PBA/MX or PBA/rGO) via an in situ transformation from ultrathin layered double hydroxides (LDH) nanosheets. 2D conductive nanosheets allow for fast electron transport and guarantee the full utilization of PBA even at high rates; at the meantime, PBA nanoparticles effectively prevent 2D materials from restacking and facilitate rapid ion diffusion. The optimized Ni0.8Mn0.2-PBA/MX as an anode for lithium-ion batteries (LIBs) delivers a capacity of 442 mAh g-1 at 0.1 A g-1 and an excellent cycling robustness in comparison with bare PBA bulk crystals. We believe that this study offers an alternative choice for rationally designing PBA-based electrode materials for energy storage.

Enhanced Jahn-Teller distortion boosts molybdenum trioxide's superior lithium ion storage capability.

Upgrading the energy density and cycling life of current lithium ion batteries is urgently needed for developing advanced portable electronics and electric vehicles. Amorphous transition metal oxides (TMO) with inherent lattice disorders exhibit enormous potential as electrode materials owing to their high specific capacity, fast ion diffusion, and excellent cyclic stability. Yet, challenges remain in their controllable synthesis. In this study, the amorphous phase is induced into α-MoO3 crystal nanobelts at room temperature with the aid of Jahn-Teller effect via enhanced lattice distortion triggered by the accumulation of low-valent molybdenum centers. The optimized HI-MoO3-36 h exhibits high reversible capacities of 886.0 at 0.1 A g-1 and 491.1 mA h g-1 at 1.0 A g-1, respectively, along with outstanding stability retaining 83.4% initial capacity after 100 cycles at 0.1 A g-1. The crystal engineering strategy proposed in this work is believed to be a salutary reference towards the synthesis of high-performance TMO anodes for energy storage applications.