Long-range order enabled stability in quantum dot light-emitting diodes.

Light-emitting diodes (LEDs) based on perovskite quantum dots (QDs) have produced external quantum efficiencies (EQEs) of more than 25% with narrowband emission1,2, but these LEDs have limited operating lifetimes. We posit that poor long-range ordering in perovskite QD films-variations in dot size, surface ligand density and dot-to-dot stacking-inhibits carrier injection, resulting in inferior operating stability because of the large bias required to produce emission in these LEDs. Here we report a chemical treatment to improve the long-range order of perovskite QD films: the diffraction intensity from the repeating QD units increases three-fold compared with that of controls. We achieve this using a synergistic dual-ligand approach: an iodide-rich agent (aniline hydroiodide) for anion exchange and a chemically reactive agent (bromotrimethylsilane) that produces a strong acid that in situ dissolves smaller QDs to regulate size and more effectively removes less conductive ligands to enable compact, uniform and defect-free films. These films exhibit high conductivity (4 × 10-4 S m-1), which is 2.5-fold higher than that of the control, and represents the highest conductivity recorded so far among perovskite QDs. The high conductivity ensures efficient charge transportation, enabling red perovskite QD-LEDs that generate a luminance of 1,000 cd m-2 at a record-low voltage of 2.8 V. The EQE at this luminance is more than 20%. Furthermore, the stability of the operating device is 100 times better than previous red perovskite LEDs at EQEs of more than 20%.

Conductive MXene/Polymer Composites for Transparent Flexible Supercapacitors.

Transparent flexible energy storage devices are limited by the trade-off among flexibility, transparency, and charge storage capability of their electrode materials. Conductive polymers are intrinsically flexible, but limited by small capacitance. Pseudocapacitive MXene provides high capacitance, yet their opaque and brittle nature hinders their flexibility and transparency. Herein, the development of synergistically interacting conductive polymer Ti3C2Tx MXene/PEDOT:PSS composites is reported for transparent flexible all-solid-state supercapacitors, with an outstanding areal capacitance of 3.1 mF cm-2, a high optical transparency of 61.6%, and excellent flexibility and durability. The high capacitance and high transparency of the devices stem from the uniform and thorough blending of PEDOT:PSS and Ti3C2Tx, which is associated with the formation of O─H…O H-bonds in the composites. The conductive MXene/polymer composite electrodes demonstrate a rational means to achieve high-capacity, transparent and flexible supercapacitors in an easy and scalable manner.

Detection of Road Crack Images Based on Multistage Feature Fusion and a Texture Awareness Method.

Structural health monitoring for roads is an important task that supports inspection of transportation infrastructure. This paper explores deep learning techniques for crack detection in road images and proposes an automatic pixel-level semantic road crack image segmentation method based on a Swin transformer. This method employs Swin-T as the backbone network to extract feature information from crack images at various levels and utilizes the texture unit to extract the texture and edge characteristic information of cracks. The refinement attention module (RAM) and panoramic feature module (PFM) then merge these diverse features, ultimately refining the segmentation results. This method is called FetNet. We collect four public real-world datasets and conduct extensive experiments, comparing FetNet with various deep-learning methods. FetNet achieves the highest precision of 90.4%, a recall of 85.3%, an F1 score of 87.9%, and a mean intersection over union of 78.6% on the Crack500 dataset. The experimental results show that the FetNet approach surpasses other advanced models in terms of crack segmentation accuracy and exhibits excellent generalizability for use in complex scenes.

Ternary Organic Solar Cells with Power Conversion Efficiency Approaching 15% by Fine-Selecting the Third Component.

Nonfullerene acceptors with mediate bandgap play a crucial role in ternary devices as the third component, further boosting the performance of organic solar cells (OSCs). Herein, three F-series acceptors (F-H, F-Cl, and F-2Cl) with mediate bandgap are selected and introduced into the PM6:BDT-Br binary system as third component to find the detailed influence of end groups with chlorine (Cl) atom substitution on the performance of ternary organic solar cells. Due to the increased substitution of Cl atoms on the end groups, F-Cl and F-2Cl as guest acceptors reveal a superior ability to regulate the morphology of blend films, contributing to the ordered packing properties and high crystallinity. As a result, F-Cl and F-2Cl based ternary OSCs achieve significantly improved PCEs of 13.89% and 14.67%, respectively, compared with the binary devices (12.70%). On the contrary, F-H without Cl atom displays a poor compatibility with the host system, resulting in an inferior ternary device with a low PCE of 10.79%. This work indicates that F-series acceptors with mediate bandgap are a promising class of third component for high-performance ternary OSCs. And introducing more Cl atoms substitution on the end groups, especially F-2Cl, will own a broad applicability for other binary devices.

A Comprehensive Review on Metal Catalysts for the Production of Cyclopentanone Derivatives from Furfural and HMF.

The catalytic transformation of biomass-based furan compounds (furfural and HMF) for the synthesis of organic chemicals is one of the important ways to utilize renewable biomass resources. Among the numerous high-value products, cyclopentanone derivatives are a kind of valuable compound obtained by the hydrogenation rearrangement of furfural and HMF in the aqueous phase of metal-hydrogen catalysis. Following the vast application of cyclopentanone derivatives, this reaction has attracted wide attention since its discovery, and a large number of catalytic systems have been reported to be effective in this transformation. Among them, the design and synthesis of metal catalysts are at the core of the reaction. This review briefly introduces the application of cyclopentanone derivatives, the transformation mechanism, and the pathway of biomass-based furan compounds for the synthesis of cyclopentanone derivatives. The important progress of metal catalysts in the reaction since the first report in 2012 up to now is emphasized, the characteristics and catalytic performance of different metal catalysts are introduced, and the critical role of metal catalysts in the reaction is discussed. Finally, the future development of this transformation process was prospected.

High performance Li-ion capacitor fabricated with dual graphene-based materials.

Lithium-ion capacitors (LICs) are now drawing increasing attention because of their potential to overcome the current energy limitations of supercapacitors and power limitations of lithium-ion batteries. In this work, we designed LICs by combining an electric double-layer capacitor cathode and a lithium-ion battery anode. Both the cathode and anode are derived from graphene-modified phenolic resin with tunable porosity and microstructure. They exhibit high specific capacity, superior rate capability and good cycling stability. Benefiting from the graphene-enhanced electrode materials, the all graphene-based LICs demonstrate a high working voltage (4.2 V), high energy density of 142.9 Wh kg-1, maximum power density of 12.1 kW kg-1 with energy density of 50 Wh kg-1, and long stable cycling performance (with ∼88% capacity retention after 5000 cycles). Considering the high performance of the device, the cost-effective and facile preparation process of the active materials, this all graphene-based lithium-ion capacitor could have many promising applications in energy storage systems.

ZnO nanowire optoelectronic synapse for neuromorphic computing.

Artificial synapses that integrate functions of sensing, memory and computing are highly desired for developing brain-inspired neuromorphic hardware. In this work, an optoelectronic synapse based on the ZnO nanowire (NW) transistor is achieved, which can be used to emulate both the short-term and long-term synaptic plasticity. Synaptic potentiation is present when the device is stimulated by light pulses, arising from the light-induced O2desorption and the persistent photoconductivity behavior of the ZnO NW. On the other hand, synaptic depression occurs when the device is stimulated by electrical pulses in dark, which is realized by introducing a charge trapping layer in the gate dielectric to trap carriers. Simulation of a neural network utilizing the ZnO NW synapses is carried out, demonstrating a high recognition accuracy over 90% after only 20 training epochs for recognizing the Modified National Institute of Standards and Technology digits. The present nanoscale optoelectronic synapse has great potential in the development of neuromorphic visual systems.

Conducting polymer-inorganic nanocomposite-based gas sensors: a review.

With the rapid development of conductive polymers, they have shown great potential in room-temperature chemical gas detection, as their electrical conductivity can be changed upon exposure to oxidative or reductive gas molecules at room temperature. However, due to their relatively low conductivity and high affinity toward volatile organic compounds and water molecules, they always exhibit low sensitivity, poor stability, and gas selectivity, which hinder their practical gas sensor applications. In addition, inorganic sensitive materials show totally different advantages in gas sensors, such as high sensitivity, fast response to low concentration analytes, high surface area, and versatile surface chemistry, which could complement the conducting polymers in terms of the sensing characteristics. It seems to be a win-win choice to combine inorganic sensitive materials with polymers for gas detection due to their synergistic effects, which has attracted extensive interests in gas-sensing applications. In this review, we summarize the recent development in polymer-inorganic nanocomposite based gas sensors. The roles of inorganic nanomaterials in improving the gas-sensing performances of conducting polymers are introduced and the progress of conducting polymer-inorganic nanocomposites including metal oxides, metal, carbon (carbon nanotube, graphene), and ternary composites are presented. Finally, a conclusion and a perspective in the field of gas sensors incorporating conducting polymer-inorganic nanocomposite are summarized.

Encapsulated Silver Nanoparticles Can Be Directly Converted to Silver Nanoshell in the Gas Phase.

We report, for the first time, that an encapsulated silver nanoparticle can be directly converted to a silver nanoshell through a nanoscale localized oxidation and reduction process in the gas phase. Silver can be etched when exposed to a mixture of NH3/O2 gases through a mechanism analogous to the formation of aqueous Tollens' reagent, in which a soluble silver-ammonia complex was formed. Starting with Ag@resorcinol-formaldehyde (RF) resin core-shell nanoparticles, we demonstrate that RF-core@Ag-shell nanoparticles can be prepared successfully when the etching rate and RF thickness were well controlled. Due to the strong surface plasmon resonance (SPR) coupling effect among neighboring silver nanoparticles, the RF@Ag nanoparticle showed great SPR and SERS performance. This process provides a general route to the conversion of Ag-core to Ag-shell nanostructures and might be extended to other systems.

Neuron anatomy structure reconstruction based on a sliding filter.

BACKGROUND: Reconstruction of neuron anatomy structure is a challenging and important task in neuroscience. However, few algorithms can automatically reconstruct the full structure well without manual assistance, making it essential to develop new methods for this task. METHODS: This paper introduces a new pipeline for reconstructing neuron anatomy structure from 3-D microscopy image stacks. This pipeline is initialized with a set of seeds that were detected by our proposed Sliding Volume Filter (SVF), given a non-circular cross-section of a neuron cell. Then, an improved open curve snake model combined with a SVF external force is applied to trace the full skeleton of the neuron cell. A radius estimation method based on a 2D sliding band filter is developed to fit the real edge of the cross-section of the neuron cell. Finally, a surface reconstruction method based on non-parallel curve networks is used to generate the neuron cell surface to finish this pipeline. RESULTS: The proposed pipeline has been evaluated using publicly available datasets. The results show that the proposed method achieves promising results in some datasets from the DIgital reconstruction of Axonal and DEndritic Morphology (DIADEM) challenge and new BigNeuron project. CONCLUSION: The new pipeline works well in neuron tracing and reconstruction. It can achieve higher efficiency, stability and robustness in neuron skeleton tracing. Furthermore, the proposed radius estimation method and applied surface reconstruction method can obtain more accurate neuron anatomy structures.

Intrinsic Ge nanowire nonvolatile memory based on a simple core-shell structure.

Intrinsic Ge nanowires (NWs) with a Ge core covered by a thick Ge oxide shell are utilized to achieve nanoscale field-effect transistor nonvolatile memories, which show a large memory window and a high ON/OFF ratio with good retention. The retainable surface charge trapping is considered to be responsible for the memory effect, and the Ge oxide shell plays a key role as the insulating tunneling dielectric which must be thick enough to prevent stored surface charges from leaking out. Annealing the device in air is demonstrated to be a simple and effective way to attain thick Ge oxide on the Ge NW surface, and the Ge-NW-based memory corresponding to thick Ge oxide exhibits a much better retention capability compared with the case of thin Ge oxide.

A near ambient pressure XPS study of subnanometer silver clusters on Al2O3 and TiO2 ultrathin film supports.

We have investigated model systems of silver clusters with different sizes (3 and 15 atoms) deposited on alumina and titania supports using ambient pressure X-ray photoelectron spectroscopy. The electronic structures of silver clusters and support materials are studied upon exposure to various atmospheres (ultrahigh vacuum, O2 and CO) at different temperatures. Compared to bulk silver, the binding energies of silver clusters are about 0.55 eV higher on TiO2 and 0.95 eV higher on Al2O3 due to the final state effect and the interaction with supports. No clear size effect of the silver XPS peak is observed on different silver clusters among these samples. Silver clusters on titania show better stability against sintering. Al 2p and Ti 2p core level peak positions of the alumina and titania support surfaces change upon exposure to oxygen while the Ag 3d core level position remains unchanged. We discuss the origin of these core level shifts and their implications for catalytic properties of Ag clusters.

Bright field microscopic cells counting method for BEVS using nonlinear convergence index sliding band filter.

BACKGROUND: The Baculovirus Expression Vector System (BEVS) is a very popular expression vector system in gene engineering. An effective host cell line cultivation protocol can facilitate the baculovirus preparation and following experiments. However, the counting of the number of host cells in the protocol is usually performed by manual observation with microscopy, which is time consuming and labor intensive work, and prone to errors for one person or between different individuals. This study aims at giving a bright field insect cells counting protocol to help improve the efficient of BEVS. METHOD: To develop a reliable and accurate counting method for the host cells in the bright field, such as Sf9 insect cells, a novel method based on a nonlinear Transformed Sliding Band Filter (TSBF) was proposed. And 3 collaborators counted cells at the same time to produce the ground truth for evaluation. The performance of TSBF method was evaluated with the image datasets of Sf9 insect cells according to the different periods of cell cultivation on the cell density, error rate and growth curve. RESULTS: The average error rate of our TSBF method is 2.21% on average, ranging from 0.89% to 3.97%, which exhibited an excellent performance with its high accuracy in lower error rate compared with traditional methods and manual counting. And the growth curve was much the manual method well. CONCLUSION: Results suggest the proposed TSBF method can detect insect cells with low error rate, and it is suitable for the counting task in BEVS to take the place of manual counting by humans. Growth curve results can reflect the cells' growth manner, which was generated by our proposed TSBF method in this paper can reflected the similar manner with it's from the manual method. All of these proven that the proposed insect cell counting method can clearly improve the efficiency of BEVS.

Diagnostic significance of HRCT imaging features in adult mycoplasma pneumonia: a retrospective study.

Mycoplasma pneumoniae pneumonia (MPP) often overlaps with the clinical manifestations and chest imaging manifestations of other types of community-acquired pneumonia (CAP). We retrospectively analyzed the clinical and imaging data of a group of patients with CAP, summarized their clinical and imaging characteristics, and discussed the diagnostic significance of their certain HRCT findings. The HRCT findings of CAP researched in our study included tree-in-bud sign (TIB), ground-glass opacity (GGO), tree fog sign (TIB + GGO), bronchial wall thickening, air-bronchogram, pleural effusion and cavity. The HRCT findings of all cases were analyzed. Among the 200 cases of MPP, 174 cases showed the TIB, 193 showed the GGO, 175 showed the tree fog sign, 181 lacked air-bronchogram. In case taking the tree fog sign and lack of air-bronchogram simultaneously as an index to distinguish MPP from OCAP, the sensitivity was 87.5%, the specificity was 97.5%, the accuracy was 92.5%. This study showed that that specific HRCT findings could be used to distinguish MPP from OCAP. The combined HRCT findings including the tree fog sign and lacked air-bronchogram simultaneously would contribute to a more accurate diagnosis of MPP.

Direct Selective Epitaxy of 2D Sb2Te3 onto Monolayer WS2 for Vertical p-n Heterojunction Photodetectors.

Two-dimensional transition metal dichalcogenides (2D-TMDs) possess appropriate bandgaps and interact via van der Waals (vdW) forces between layers, effectively overcoming lattice compatibility challenges inherent in traditional heterojunctions. This property facilitates the creation of heterojunctions with customizable bandgap alignments. However, the prevailing method for creating heterojunctions with 2D-TMDs relies on the low-efficiency technique of mechanical exfoliation. Sb2Te3, recognized as a notable p-type semiconductor, emerges as a versatile component for constructing diverse vertical p-n heterostructures with 2D-TMDs. This study presents the successful large-scale deposition of 2D Sb2Te3 onto inert mica substrates, providing valuable insights into the integration of Sb2Te3 with 2D-TMDs to form heterostructures. Building upon this initial advancement, a precise epitaxial growth method for Sb2Te3 on pre-existing WS2 surfaces on SiO2/Si substrates is achieved through a two-step chemical vapor deposition process, resulting in the formation of Sb2Te3/WS2 heterojunctions. Finally, the development of 2D Sb2Te3/WS2 optoelectronic devices is accomplished, showing rapid response times, with a rise/decay time of 305 µs/503 µs, respectively.

Oxidation and reduction of size-selected subnanometer Pd clusters on Al2O3 surface.

In this paper, we investigate uniformly dispersed size-selected Pd(n) clusters (n = 4, 10, and 17) on alumina supports. We study the changes of clustered Pd atoms under oxidizing and reducing (O2 and CO, respectively) conditions in situ using ambient pressure XPS. The behavior of Pd in the clusters is quite different from that of Pd foil under the same conditions. For all Pd clusters, we observe only one Pd peak. The binding energy of this Pd 3d peak is ~1-1.4 eV higher than that of metallic Pd species and changes slightly in CO and O2 environments. On the Pd foil however many different Pd species co-exist on the surface and change their oxidation states under different conditions. We find that the Pd atoms in direct contact with Al2O3 differ in oxidation state from the surface Pd atoms in a foil under reaction conditions. Compared to previous literature, we find that Pd 3d peak positions are greatly influenced by the different types of Al2O3 supports due to the combination of both initial and final state effects.

ColonNet: A novel polyp segmentation framework based on LK-RFB and GPPD.

Colorectal cancer (CRC) holds the distinction of being the most prevalent malignant tumor affecting the digestive system. It is a formidable global health challenge, as it ranks as the fourth leading cause of cancer-related fatalities around the world. Despite considerable advancements in comprehending and addressing colorectal cancer (CRC), the likelihood of recurring tumors and metastasis remains a major cause of high morbidity and mortality rates during treatment. Currently, colonoscopy is the predominant method for CRC screening. Artificial intelligence has emerged as a promising tool in aiding the diagnosis of polyps, which have demonstrated significant potential. Unfortunately, most segmentation methods face challenges in terms of limited accuracy and generalization to different datasets, especially the slow processing and analysis speed has become a major obstacle. In this study, we propose a fast and efficient polyp segmentation framework based on the Large-Kernel Receptive Field Block (LK-RFB) and Global Parallel Partial Decoder(GPPD). Our proposed ColonNet has been extensively tested and proven effective, achieving a DICE coefficient of over 0.910 and an FPS of over 102 on the CVC-300 dataset. In comparison to the state-of-the-art (SOTA) methods, ColonNet outperforms or achieves comparable performance on five publicly available datasets, establishing a new SOTA. Compared to state-of-the-art methods, ColonNet achieves the highest FPS (over 102 FPS) while maintaining excellent segmentation results, achieving the best or comparable performance on the five public datasets. The code will be released at: https://github.com/SPECTRELWF/ColonNet.

Ultrasound radiomics models based on multimodal imaging feature fusion of papillary thyroid carcinoma for predicting central lymph node metastasis.

Objective: This retrospective study aimed to establish ultrasound radiomics models to predict central lymph node metastasis (CLNM) based on preoperative multimodal ultrasound imaging features fusion of primary papillary thyroid carcinoma (PTC). Methods: In total, 498 cases of unifocal PTC were randomly divided into two sets which comprised 348 cases (training set) and 150 cases (validition set). In addition, the testing set contained 120 cases of PTC at different times. Post-operative histopathology was the gold standard for CLNM. The following steps were used to build models: the regions of interest were segmented in PTC ultrasound images, multimodal ultrasound image features were then extracted by the deep learning residual neural network with 50-layer network, followed by feature selection and fusion; subsequently, classification was performed using three classical classifiers-adaptive boosting (AB), linear discriminant analysis (LDA), and support vector machine (SVM). The performances of the unimodal models (Unimodal-AB, Unimodal-LDA, and Unimodal-SVM) and the multimodal models (Multimodal-AB, Multimodal-LDA, and Multimodal-SVM) were evaluated and compared. Results: The Multimodal-SVM model achieved the best predictive performance than the other models (P < 0.05). For the Multimodal-SVM model validation and testing sets, the areas under the receiver operating characteristic curves (AUCs) were 0.910 (95% CI, 0.894-0.926) and 0.851 (95% CI, 0.833-0.869), respectively. The AUCs of the Multimodal-SVM model were 0.920 (95% CI, 0.881-0.959) in the cN0 subgroup-1 cases and 0.828 (95% CI, 0.769-0.887) in the cN0 subgroup-2 cases. Conclusion: The ultrasound radiomics model only based on the PTC multimodal ultrasound image have high clinical value in predicting CLNM and can provide a reference for treatment decisions.

Reducing Contact Resistance in Organic Field-Effect Transistors: A Comprehensive Comparison between 2D and Microrod Single Crystals.

A comprehensive comparison of organic single crystals based on a single material but with different dimensions provides a unique approach to probe their carrier injection mechanism. In this report, both two-dimensional (2D) and microrod single crystals with the same crystalline structure of an identical thiopyran derivative, 7,14-dioctylnaphtho[2,1-f:6,5-f']bis(cyclopentane[b]thiopyran) (C8-SS), are grown on a glycerol surface with the space-confined method. Organic field-effect transistors (OFETs) based on the 2D C8-SS single crystal exhibit superior performance compared with those based on the microrod single crystal, particularly in their contact resistance (RC). It is demonstrated that the resistance of the crystal bulk in the contact region plays a key role in RC of the OFETs. Thus, among the 30 devices tested, the microrod OFETs typically appear contact-limited, whereas the 2D OFETs possess significantly reduced RC arising from the tiny thickness of the 2D single crystal. The 2D OFETs show high operational stability and high channel mobility up to 5.7 cm2/V·s. The elucidation of the contact behavior highlights the merits and great potential of 2D molecular single crystals in organic electronics.

Voltage-Mode Ferroelectric Synapse for Neuromorphic Computing.

Ferroelectric materials with a modulable polarization extent hold promise for exploring voltage-driven neuromorphic hardware, in which direct current flow can be minimized. Utilizing a single active layer of an insulating ferroelectric polymer, we developed a voltage-mode ferroelectric synapse that can continuously and reversibly update its states. The device states are straightforwardly manifested in the form of variable output voltage, enabling large-scale direct cascading of multiple ferroelectric synapses to build a deep physical neural network. Such a neural network based on potential superposition rather than current flow is analogous to the biological counterpart driven by action potentials in the brain. A high accuracy of over 97% for the simulation of handwritten digit recognition is achieved using the voltage-mode neural network. The controlled ferroelectric polarization, revealed by piezoresponse force microscopy, turns out to be responsible for the synaptic weight updates in the ferroelectric synapses. The present work demonstrates an alternative strategy for the design and construction of emerging artificial neural networks.