Superlattice cathodes endow cation and anion co-intercalation for high-energy-density aluminium batteries.

Conventionally, rocking-chair batteries capacity primarily depends on cation shuttling. However, intrinsically high-charge-density metal-ions, such as Al3+, inevitably cause strong Coulombic ion-lattice interactions, resulting in low practical energy density and inferior long-term stability towards rechargeable aluminium batteries (RABs). Herein, we introduce tunable quantum confinement effects and tailor a family of anion/cation co-(de)intercalation superlattice cathodes, achieving high-voltage anion charge compensation, with extra-capacity, in RABs. The optimized superlattice cathode with adjustable van der Waals not only enables facile traditional cation (de)intercalation, but also activates O2- compensation with an extra anion reaction. Furthermore, the constructed cathode delivers high energy-density (466 Wh kg-1 at 107 W kg-1) and one of the best cycle stability (225 mAh g-1 over 3000 cycles at 2.0 A g-1) in RABs. Overall, the anion-involving redox mechanism overcomes the bottlenecks of conventional electrodes, thereby heralding a promising advance in energy-storage-systems.

PPG based Noninvasive Blood Glucose Monitoring using Multi-view Attention and Cascaded BiLSTM Hierarchical Feature Fusion Approach.

Diabetes is a chronic disease with exponential growth and poses significant challenges to global healthcare. Regular blood glucose (BG) monitoring is key for avoiding diabetic complications. Traditional BG measurement techniques are invasive and minimally invasive, causing pain, discomfort, cost, and infection risks. To address these issues, we developed a noninvasive BG monitoring approach on photoplethysmography (PPG) signals using multi-view attention and cascaded BiLSTM hierarchical feature fusion approach. Firstly, we implemented a convolutional multi-view attention block to extract the temporal features through adaptive contextual information aggregation. Secondly, we built a cascaded BiLSTM network to efficiently extract the fine-grained features through bidirectional learning. Finally, we developed a hierarchical feature fusion with bilinear polling through cross-layer interaction to obtain higher-order features for BG monitoring. For validation, we conducted comprehensive experimentation on up to 6 days of PPG and BG data from 21 participants. The proposed approach showed competitive results compared to existing approaches by RMSE of 1.67 mmol/L and MARD of 17.88%. Additionally, the clinical accuracy using Clarke error grid (CEG) analysis showed 98.80% of BG values in Zone A+B. Therefore, the proposed approach offers a favorable solution in diabetes management by noninvasively monitoring the BG levels.

TTFDNet: Precise Depth Estimation from Single-Frame Fringe Patterns.

This work presents TTFDNet, a transformer-based and transfer learning network for end-to-end depth estimation from single-frame fringe patterns in fringe projection profilometry. TTFDNet features a precise contour and coarse depth (PCCD) pre-processor, a global multi-dimensional fusion (GMDF) module and a progressive depth extractor (PDE). It utilizes transfer learning through fringe structure consistency evaluation (FSCE) to leverage the transformer's benefits even on a small dataset. Tested on 208 scenes, the model achieved a mean absolute error (MAE) of 0.00372 mm, outperforming Unet (0.03458 mm) models, PDE (0.01063 mm) and PCTNet (0.00518 mm). It demonstrated precise measurement capabilities with deviations of ~90 µm for a 25.4 mm radius ball and ~6 µm for a 20 mm thick metal part. Additionally, TTFDNet showed excellent generalization and robustness in dynamic reconstruction and varied imaging conditions, making it appropriate for practical applications in manufacturing, automation and computer vision.

AI-based segmentation of renal enhanced CT images for quantitative evaluate of chronic kidney disease.

To quantitatively evaluate chronic kidney disease (CKD), a deep convolutional neural network-based segmentation model was applied to renal enhanced computed tomography (CT) images. A retrospective analysis was conducted on a cohort of 100 individuals diagnosed with CKD and 90 individuals with healthy kidneys, who underwent contrast-enhanced CT scans of the kidneys or abdomen. Demographic and clinical data were collected from all participants. The study consisted of two distinct stages: firstly, the development and validation of a three-dimensional (3D) nnU-Net model for segmenting the arterial phase of renal enhanced CT scans; secondly, the utilization of the 3D nnU-Net model for quantitative evaluation of CKD. The 3D nnU-Net model achieved a mean Dice Similarity Coefficient (DSC) of 93.53% for renal parenchyma and 81.48% for renal cortex. Statistically significant differences were observed among different stages of renal function for renal parenchyma volume (VRP), renal cortex volume (VRC), renal medulla volume (VRM), the CT values of renal parenchyma (HuRP), the CT values of renal cortex (HuRC), and the CT values of renal medulla (HuRM) (F = 93.476, 144.918, 9.637, 170.533, 216.616, and 94.283; p < 0.001). Pearson correlation analysis revealed significant positive associations between glomerular filtration rate (eGFR) and VRP, VRC, VRM, HuRP, HuRC, and HuRM (r = 0.749, 0.818, 0.321, 0.819, 0.820, and 0.747, respectively, all p < 0.001). Similarly, a negative correlation was observed between serum creatinine (Scr) levels and VRP, VRC, VRM, HuRP, HuRC, and HuRM (r = - 0.759, - 0.777, - 0.420, - 0.762, - 0.771, and - 0.726, respectively, all p < 0.001). For predicting CKD in males, VRP had an area under the curve (AUC) of 0.726, p < 0.001; VRC, AUC 0.765, p < 0.001; VRM, AUC 0.578, p = 0.018; HuRP, AUC 0.912, p < 0.001; HuRC, AUC 0.952, p < 0.001; and HuRM, AUC 0.772, p < 0.001 in males. In females, VRP had an AUC of 0.813, p < 0.001; VRC, AUC 0.851, p < 0.001; VRM, AUC 0.623, p = 0.060; HuRP, AUC 0.904, p < 0.001; HuRC, AUC 0.934, p < 0.001; and HuRM, AUC 0.840, p < 0.001. The optimal cutoff values for predicting CKD in HuRP are 99.9 Hu for males and 98.4 Hu for females, while in HuRC are 120.1 Hu for males and 111.8 Hu for females. The kidney was effectively segmented by our AI-based 3D nnU-Net model for enhanced renal CT images. In terms of mild kidney injury, the CT values exhibited higher sensitivity compared to kidney volume. The correlation analysis revealed a stronger association between VRC, HuRP, and HuRC with renal function, while the association between VRP and HuRM was weaker, and the association between VRM was the weakest. Particularly, HuRP and HuRC demonstrated significant potential in predicting renal function. For diagnosing CKD, it is recommended to set the threshold values as follows: HuRP < 99.9 Hu and HuRC < 120.1 Hu in males, and HuRP < 98.4 Hu and HuRC < 111.8 Hu in females.

Local thermal adaption mediates the sensitivity of Daphnia magna to nanoplastics under global warming scenarios.

The toxicity of nanoplastics at environmentally relevant concentrations has received widespread attention in the context of global warming. Despite numerous studies on the impact of mean temperature (MT), the effects of daily temperature fluctuations (DTFs) on the ecotoxicity of nanoplastics remains largely unexplored. Moreover, the role of evolutionary adaptation in assessing long-term ecological risks is unclear. Here, we investigated the effects of polystyrene nanoplastics (5 µg L-1) on Daphnia magna under varying MT (20 °C and 24 °C) and DTFs (0 °C, 5 °C, and 10 °C). Capitalizing on a space-for-time substitution approach, we further assessed how local thermal adaptation affect the sensitivity of Daphnia to nanoplastics under global warming. Our results indicated that nanoplastics exposure in general reduced heartbeat rate, thoracic limb activity and feeding rate, and increased CytP450, ETS activity and Hgb concentrations. Higher MT and DTFs enhanced these effects. Notably, clones originating from their respective sites performed better under their native temperature conditions, indicating local thermal adaptation. Warm-adapted low-latitude D. magna showed stronger nanoplastics-induced increases in CytP450, ETS activity and Hgb concentrations under local MT 24 °C, while cold-adapted high-latitude D. magna showed stronger nanoplastics-induced decreases in heartbeat rate, thoracic limb activity and feeding rate under high MT than under low MT.

Efficient pure-red perovskite light-emitting diodes with strong passivation via ultrasmall-sized molecules.

Perovskite light-emitting diodes (PeLEDs) have attracted great attention in recent years; however, the halogen vacancy defects in perovskite notably hamper the development of high-efficiency devices. Previously, large-sized passivation agents have been usually used, while the effect of defect passivation is limited due to the weak bonding or the large space steric hindrance. Here, we predict that the ultrasmall-sized formate (Fa) and acetate (Ac) have more efficient passivation ability because of the stronger binding with the perovskite, as demonstrated by density functional theory calculation. We introduce ultrasmall-sized cesium salts (CsFa/CsAc) into buried interface, which can also diffuse into the bulk, resulting in both buried interface and bulk passivation. In addition, the improved perovskite growth has been found due to the enhanced hydrophily after introducing CsFa/CsAc as additive. According to these advantages, a pure-red PeLED with 24.2% efficiency at 639 nm has been achieved.

Direct space-time manipulation mechanism for spatio-temporal coupling of ultrafast light field.

Traditionally, manipulation of spatiotemporal coupling (STC) of the ultrafast light fields can be actualized in the space-spectrum domain with some 4-f pulse shapers, which suffers usually from some limitations, such as spectral/pixel resolution and information crosstalk associated with the 4-f pulse shapers. This work introduces a novel mechanism for direct space-time manipulation of ultrafast light fields to overcome the limitations. This mechanism combines a space-dependent time delay with some spatial geometrical transformations, which has been experimentally proved by generating a high-quality STC light field, called light spring (LS). The LS, owing a broad topological charge bandwidth of 11.5 and a tunable central topological charge from 2 to -11, can propagate with a stable spatiotemporal intensity structure from near to far fields. This achievement implies the mechanism provides an efficient way to generate complex STC light fields, such as LS with potential applications in information encryption, optical communication, and laser-plasma acceleration.

Synergistic π-Conjugation Organic Cathode for Ultra-Stable Aqueous Aluminum Batteries.

Rechargeable aqueous aluminum batteries (AABs) are promising energy storage technologies owing to their high safety and ultra-high energy-to-price ratio. However, either the strong electrostatic forces between high-charge-density Al3+ and host lattice, or sluggish large carrier-ion diffusion toward the conventional inorganic cathodes generates inferior cycling stability and low rate-capacity. To overcome these inherent confinements, a series of promising redox-active organic materials (ROMs) are investigated and a π-conjugated structure ROMs with synergistic C═O and C═N groups is optimized as the new cathode in AABs. Benefiting from the joint utilization of multi-redox centers and rich π-π intermolecular interactions, the optimized ROMs with unique ion coordination storage mechanism facilely accommodate complex active ions with mitigated coulombic repulsion and robust lattice structure, which is further validated via theoretical simulations. Thus, the cathode achieves enhanced rate performance (153.9 mAh g-1 at 2.0 A g-1) and one of the best long-term stabilities (125.7 mAh g-1 after 4,000 cycles at 1.0 A g-1) in AABs. Via molecular exploitation, this work paves the new direction toward high-performance cathode materials in aqueous multivalent-ion battery systems.

A Spiking Artificial Vision Architecture Based on Fully Emulating the Human Vision.

Intelligent vision necessitates the deployment of detectors that are always-on and low-power, mirroring the continuous and uninterrupted responsiveness characteristic of human vision. Nonetheless, contemporary artificial vision systems attain this goal by the continuous processing of massive image frames and executing intricate algorithms, thereby expending substantial computational power and energy. In contrast, biological data processing, based on event-triggered spiking, has higher efficiency and lower energy consumption. Here, this work proposes an artificial vision architecture consisting of spiking photodetectors and artificial synapses, closely mirroring the intricacies of the human visual system. Distinct from previously reported techniques, the photodetector is self-powered and event-triggered, outputting light-modulated spiking signals directly, thereby fulfilling the imperative for always-on with low-power consumption. With the spiking signals processing through the integrated synapse units, recognition of graphics, gestures, and human action has been implemented, illustrating the potent image processing capabilities inherent within this architecture. The results prove the 90% accuracy rate in human action recognition within a mere five epochs utilizing a rudimentary artificial neural network. This novel architecture, grounded in spiking photodetectors, offers a viable alternative to the extant models of always-on low-power artificial vision system.

The splicing factor Prpf31 is required for hematopoietic stem and progenitor cell expansion during zebrafish embryogenesis.

Pre-mRNA splicing is a precise regulated process and is crucial for system development and homeostasis maintenance. Mutations in spliceosomal components have been found in various hematopoietic malignancies (HMs) and have been considered as oncogenic derivers of HMs. However, the role of spliceosomal components in normal and malignant hematopoiesis remains largely unknown. Pre-mRNA processing factor 31 (PRPF31) is a constitutive spliceosomal component, which mutations are associated with autosomal dominant retinitis pigmentosa. PRPF31 was found to be mutated in several HMs, but the function of PRPF31 in normal hematopoiesis has not been explored. In our previous study, we generated a prpf31 knockout (KO) zebrafish line and reported that Prpf31 regulates the survival and differentiation of retinal progenitor cells by modulating the alternative splicing of genes involved in mitosis and DNA repair. In this study, by using the prpf31 KO zebrafish line, we discovered that prpf31 KO zebrafish exhibited severe defects in hematopoietic stem and progenitor cell (HSPC) expansion and its sequentially differentiated lineages. Immunofluorescence results showed that Prpf31-deficient HSPCs underwent malformed mitosis and M phase arrest during HSPC expansion. Transcriptome analysis and experimental validations revealed that Prpf31 deficiency extensively perturbed the alternative splicing of mitosis-related genes. Collectively, our findings elucidate a previously undescribed role for Prpf31 in HSPC expansion, through regulating the alternative splicing of mitosis-related genes.

Automatic Chinese Food recognition based on a stacking fusion model.

With commercialization of deep learning models, daily precision dietary record based on images from smartphones becomes possible. This study took advantage of Deep-learning techniques on visual recognition tasks and proposed a big-data-driven Deep-learning model regressing from food images. We established the largest data set of Chinese dishes to date, named CNFOOD-241. It contained more than 190,000 images with 241 categories, covering Staple food, meat, vegetarian diet, mixed meat and vegetables, soups, dessert category. This study also compares the prediction results of three popular deep learning models on this dataset, ResNeXt101_32x32d achieving up to 82.05% for top-1 accuracy and 97.13% for top-5 accuracy. Besides, this paper uses a multi-model fusion method based on stacking in the field of food recognition for the first time. We built a meta-learner after the base model to integrate the three base models of different architectures to improve robustness. The accuracy achieves 82.88% for top-1 accuracy.Clinical Relevance-This study proves that the application of artificial intelligence technology in the identification of Chinese dishes is feasible, which can play a positive role in people who need to control their diet, such as diabetes and obesity.

The splicing factor DHX38 enables retinal development through safeguarding genome integrity.

DEAH-Box Helicase 38 (DHX38) is a pre-mRNA splicing factor and also a disease-causing gene of autosomal recessive retinitis pigmentosa (arRP). The role of DHX38 in the development and maintenance of the retina remains largely unknown. In this study, by using the dhx38 knockout zebrafish model, we demonstrated that Dhx38 deficiency causes severe differentiation defects and apoptosis of retinal progenitor cells (RPCs) through disrupted mitosis and increased DNA damage. Furthermore, we found a significant accumulation of R-loops in the dhx38-deficient RPCs and human cell lines. Finally, we found that DNA replication stress is the prerequisite for R-loop-induced DNA damage in the DHX38 knockdown cells. Taken together, our study demonstrates a necessary role of DHX38 in the development of retina and reveals a DHX38/R-loop/replication stress/DNA damage regulatory axis that is relatively independent of the known functions of DHX38 in mitosis control.

Retinal degeneration in rpgra mutant zebrafish.

Introduction: Pathogenic mutations in RPGR ORF15, one of two major human RPGR isoforms, were responsible for most X-linked retinitis pigmentosa cases. Previous studies have shown that RPGR plays a critical role in ciliary protein transport. However, the precise mechanisms of disease triggered by RPGR ORF15 mutations have yet to be clearly defined. There are two homologous genes in zebrafish, rpgra and rpgrb. Zebrafish rpgra has a single transcript homologous to human RPGR ORF15; rpgrb has two major transcripts: rpgrb ex1-17 and rpgrb ORF15, similar to human RPGR ex1-19 and RPGR ORF15, respectively. rpgrb knockdown in zebrafish resulted in both abnormal development and increased cell death in the dysplastic retina. However, the impact of knocking down rpgra in zebrafish remains undetermined. Here, we constructed a rpgra mutant zebrafish model to investigate the retina defect and related molecular mechanism. Methods: we utilized transcription activator-like effector nuclease (TALEN) to generate a rpgra mutant zebrafish. Western blot was used to determine protein expression. RT-PCR was used to quantify gene transcription levels. The visual function of embryonic zebrafish was detected by electroretinography. Immunohistochemistry was used to observe the pathological changes in the retina of mutant zebrafish and transmission electron microscope was employed to view subcellular structure of photoreceptor cells. Results: A homozygous rpgra mutant zebrafish with c.1675_1678delins21 mutation was successfully constructed. Despite the normal morphological development of the retina at 5 days post-fertilization, visual dysfunction was observed in the mutant zebrafish. Further histological and immunofluorescence assays indicated that rpgra mutant zebrafish retina photoreceptors progressively began to degenerate at 3-6 months. Additionally, the mislocalization of cone outer segment proteins (Opn1lw and Gnb3) and the accumulation of vacuole-like structures around the connecting cilium below the OSs were observed in mutant zebrafish. Furthermore, Rab8a, a key regulator of opsin-carrier vesicle trafficking, exhibited decreased expression and evident mislocalization in mutant zebrafish. Discussion: This study generated a novel rpgra mutant zebrafish model, which showed retinal degeneration. our data suggested Rpgra is necessary for the ciliary transport of cone-associated proteins, and further investigation is required to determine its function in rods. The rpgra mutant zebrafish constructed in this study may help us gain a better understanding of the molecular mechanism of retinal degeneration caused by RPGR ORF15 mutation and find some useful treatment in the future.

Noninvasive Blood Glucose Monitoring Using Spatiotemporal ECG and PPG Feature Fusion and Weight-Based Choquet Integral Multimodel Approach.

change of blood glucose (BG) level stimulates the autonomic nervous system leading to variation in both human's electrocardiogram (ECG) and photoplethysmogram (PPG). In this article, we aimed to construct a novel multimodal framework based on ECG and PPG signal fusion to establish a universal BG monitoring model. This is proposed as a spatiotemporal decision fusion strategy that uses weight-based Choquet integral for BG monitoring. Specifically, the multimodal framework performs three-level fusion. First, ECG and PPG signals are collected and coupled into different pools. Second, the temporal statistical features and spatial morphological features in the ECG and PPG signals are extracted through numerical analysis and residual networks, respectively. Furthermore, the suitable temporal statistical features are determined with three feature selection techniques, and the spatial morphological features are compressed by deep neural networks (DNNs). Lastly, weight-based Choquet integral multimodel fusion is integrated for coupling different BG monitoring algorithms based on the temporal statistical features and spatial morphological features. To verify the feasibility of the model, a total of 103 days of ECG and PPG signals encompassing 21 participants were collected in this article. The BG levels of participants ranged between 2.2 and 21.8 mmol/L. The results obtained show that the proposed model has excellent BG monitoring performance with a root-mean-square error (RMSE) of 1.49 mmol/L, mean absolute relative difference (MARD) of 13.42%, and Zone A + B of 99.49% in tenfold cross-validation. Therefore, we conclude that the proposed fusion approach for BG monitoring has potentials in practical applications of diabetes management.

Single-cell RNA-sequencing analysis reveals enhanced non-canonical neurotrophic factor signaling in the subacute phase of traumatic brain injury.

BACKGROUND: Traumatic brain injury (TBI) is a leading cause of long-term disability in young adults and induces complex neuropathological processes. Cellular autonomous and intercellular changes during the subacute phase contribute substantially to the neuropathology of TBI. However, the underlying mechanisms remain elusive. In this study, we explored the dysregulated cellular signaling during the subacute phase of TBI. METHODS: Single-cell RNA-sequencing data (GSE160763) of TBI were analyzed to explore the cell-cell communication in the subacute phase of TBI. Upregulated neurotrophic factor signaling was validated in a mouse model of TBI. Primary cell cultures and cell lines were used as in vitro models to examine the potential mechanisms affecting signaling. RESULTS: Single-cell RNA-sequencing analysis revealed that microglia and astrocytes were the most affected cells during the subacute phase of TBI. Cell-cell communication analysis demonstrated that signaling mediated by the non-canonical neurotrophic factors midkine (MDK), pleiotrophin (PTN), and prosaposin (PSAP) in the microglia/astrocytes was upregulated in the subacute phase of TBI. Time-course profiling showed that MDK, PTN, and PSAP expression was primarily upregulated in the subacute phase of TBI, and astrocytes were the major source of MDK and PTN after TBI. In vitro studies revealed that the expression of MDK, PTN, and PSAP in astrocytes was enhanced by activated microglia. Moreover, MDK and PTN promoted the proliferation of neural progenitors derived from human-induced pluripotent stem cells (iPSCs) and neurite growth in iPSC-derived neurons, whereas PSAP exclusively stimulated neurite growth. CONCLUSION: The non-canonical neurotrophic factors MDK, PTN, and PSAP were upregulated in the subacute phase of TBI and played a crucial role in neuroregeneration.

Systematic investigation of the mechanical, electronic, and interfacial properties of high mobility monolayer InAs from first-principles calculations.

Due to the excellent electrostatic control, high mobility, large specific surface area, and suitable direct energy gap of two-dimensional (2D) indium arsenide (InAs), it is regarded as one of the most promising alternative channel materials for next-generation electronic and optoelectronic devices. Recently, 2D semiconducting InAs has been successfully prepared. Based on first-principles calculations, we calculate the mechanical, electronic, and interfacial properties of monolayer (ML) fully-hydrogen-passivated InAs (InAsH2) material. The results show that 2D InAsH2 with excellent stability has a suitable logic device band gap (1.59 eV) comparable to silicon (1.14 eV) and 2D MoS2 (1.80 eV), and the electron carrier mobility of ML InAsH2 (490 cm2 V-1 s-1) is twice as large as that of 2D MoS2 (200 cm2 V-1 s-1). In addition, we study the electronic structure of the interfacial contact characteristics of ML half-hydrogen-passivated InAs (InAsH) with seven bulk metals (Ag, Au, Cu, Al, Ni, Pd, Pt) and two 2D metals (ML Ti2C and ML graphene). 2D InAs was metallized after contact with the seven bulk metals and two 2D metals. Based on the above, we insert 2D boron nitride (BN) between ML InAsH and the seven low/high-power function bulk metals to eliminate the interfacial states. Remarkably, the semiconducting properties of 2D InAs with Pd and Pt electrodes are recovered, and 2D InAs achieves p-type ohmic contact with the Pt electrode, which facilitates high on-current and high-frequency operation of the transistor. Hence, this work provides systematic theoretical guidance for the design of next-generation electronic devices.

Wafer-Scale Single Crystal Hexagonal Boron Nitride Layers Grown by Submicron-Spacing Vapor Deposition.

The direct growth of wafer-scale single crystal two-dimensional (2D) hexagonal boron nitride (h-BN) layer with a controllable thickness is highly desirable for 2D-material-based device applications. Here, for the first time, a facile submicron-spacing vapor deposition (SSVD) method is reported to achieve 2-inch single crystal h-BN layers with controllable thickness from monolayer to tens of nanometers on the dielectric sapphire substrates using a boron film as the solid source. In the SSVD growth, the boron film is fully covered by the same-sized sapphire substrate with a submicron spacing, leading to an efficient vapor diffusion transport. The epitaxial h-BN layer exhibits extremely high crystalline quality, as demonstrated by both a sharp Raman E2g vibration mode (12 cm-1 ) and a narrow X-ray rocking curve (0.10°). Furthermore, a deep ultraviolet photodetector and a ZrS2 /h-BN heterostructure fabricated from the h-BN layer demonstrate its fascinating properties and potential applications. This facile method to synthesize wafer-scale single crystal h-BN layers with controllable thickness paves the way to future 2D semiconductor-based electronics and optoelectronics.

Strain-Tunable Electronic and Transport Properties of One-Dimensional Fibrous Phosphorus Nanotubes.

The one-dimensional van der Waals (1D vdW) material fibrous red phosphorus (FRP) nanotubes are a promising direct-bandgap semiconductor with high carrier mobility and anisotropic optical responses because of low deformation potential and dangling-bond-free anisotropic interface. Employing first-principles calculations, we captured the potential of 1D FRP nanotubes. The thermal stability of 1D FRP nanotubes was confirmed by phonon calculation. Meanwhile, Raman spectroscopy indicated the strong vibration mode (366 cm-1) is along the phosphorus nanotube. Interestingly, spatial anisotropy bandgaps were found along with various stacking orientations. The charge transport calculations showed that the 1D FRP nanotube has a high hole mobility (499.2 cm2 V-1 s-1), considering the weak acoustic phonon scattering. More importantly, we found that the hole mobility changes dramatically (down to 7.1 cm2 V-1 s-1) under the strain, and the strain-dependent charge transport property of 1D FRP nanotubes could be considered to have many potential applications for electronics, optoelectronics, and switching devices.

Dark soliton families in quintic nonlinear lattices.

We prove that the dark solitons can be stable in the purely quintic nonlinear lattices, including the fundamental, tripole and five-pole solitons. These dark soliton families are generated on the periodic nonlinear backgrounds. The propagation constant affects the forms of these solitons, while the number of poles does not lead to the variation of the backgrounds. The dark solitons are stable only when the propagation constant is moderately large.

FISI: frequency domain integration sequential imaging at 1.26×1013 frames per second and 108 lines per millimeter.

High spatial resolution on the image plane (intrinsic spatial resolution) has always been a problem for ultrafast imaging. Single-shot ultrafast imaging methods can achieve high spatial resolution on the object plane through amplification systems but with low intrinsic spatial resolutions. We present frequency domain integration sequential imaging (FISI), which encodes a transient dynamic by an inversed 4f (IFF) system and decodes it using optical spatial frequencies recognition (OFR), which overcomes the limitation of the spatial frequencies recognition algorithm. In an experiment on the process of an air plasma channel, FISI achieved shadow imaging of the channel with a framing rate of 1.26×1013 fps and an intrinsic spatial resolution of 108 lp/mm (the spatial resolution on the image plane). Owing to its excellent framing time and high intrinsic spatial resolution, FISI can probe both repeatable and unrepeatable ultrafast phenomena, such as laser-induced damage, plasma physics, and shockwave interactions in living cells with high quality.