Triplet Exciton Management via Solid-State Interactions in Organic Semiconductors

Tightly-bound excitons play an important role in the function of molecular materials for light emission and light harvesting. This thesis investigates the effects of solid-state interactions on triplet excitons in a new family of organometallic light emitters, carbene-metal-amides (CMAs). Triplet excitons are normally silent in luminescence due to the spin-forbidden decay process, whereas effectively harvesting triplet excitons helps to boost the performance of light-emitting devices. As the triplet excitons are sensitive to both molecular properties and external environment, in this thesis we deploy optical spectroscopy techniques to understand the effect of solid-state interactions on triplet excitons. After introducing the relevant theoretical and experimental background of triplet formation in a single molecule and interactions between molecules, we firstly describe the intermolecular electrostatic interactions and the role of triplet diffusion and find that the combined effects of both blueshift the charge-transfer energy while other photophysical properties remain relatively constant in gold-bridged CMA1. We then describe the crystallisation of CMA1 thin films, which allows us to experimentally investigate the link between molecular conformations and photophysical properties. A combination of restricted torsional distortion and molecular electronic polarisation greatly blueshifts the charge-transfer emission by around 400 meV in the crystalline versus the amorphous phase. We also discover that the intersystem crossing rate and emission kinetics are unaffected by the extent of torsional distortion. Finally, we apply electrostatic interactions to the other two coinage metal-bridged CMAs to explore the effect of heavy metal atoms on the intersystem crossing and luminescence mechanism. We show that the photophysical properties do not reflect expected trends based upon the heavy atom effect as both direct coupling between charge-transfers states and spin-vibronic coupling via an intermediate state are present

The effectiveness of ultrasound-guided core needle biopsy in detecting lymph node metastases in the axilla in patients with breast cancer: systematic review and meta-analysis

Objective: This study aimed to perform a meta-analysis to investigate the diagnostic safety and accuracy of Ultrasound-Guided Core Needle Biopsy (US-CNB) Axillary Lymph Nodes (ALNs) region in patients with Breast Cancer (BC). Methods: The authors searched the electronic databases PubMed, Scopus, Embase, and Web of Science for clinical trials about US-CNB for the detection of ALNs in breast cancer patients. The authors extracted and pooled raw data from the included studies and performed statistical analyses using Meta-DiSc 1.4 and Review Manager 5.3 software. A random effects model was used to calculate the data. At the same time, data from the Ultrasound-guided Fine-Needle Aspiration (US-FNA) were introduced for comparison with the US-CNB. In addition, the subgroup was performed to explore the causes of heterogeneity. (PROSPERO ID: CRD42022369491). Results: In total, 18 articles with 2521 patients were assessed as meeting the study criteria. The overall sensitivity was 0.90 (95% CI [Confidence Interval], 0.87‒0.91; p = 0.00), the overall specificity was 0.99 (95% CI 0.98‒1.00; p = 0.62), the overall area under the curve (AUC) was 0.98. Next, in the comparison of US-CNB and US-FNA, US-CNB is better than US-FNA in the diagnosis of ALNs metastases. The sensitivity was 0.88 (95% CI 0.84‒0.91; p = 0.12) vs. 0.73 (95% CI 0.69‒0.76; p = 0.91), the specificity was 1.00 (95% CI 0.99‒1.00; p = 1.00) vs. 0.99 (95% CI 0.67‒0.74; p = 0.92), and the AUC was 0.99 vs. 0.98. Subgroup analysis showed that heterogeneity may be related to preoperative Neoadjuvant Chemotherapy (NAC) treatment, region, size of tumor diameter, and the number of punctures. Conclusion: US-CNB has a satisfactory diagnostic performance with good specificity and sensitivity in the preoperative diagnosis of ALNs in BC patients

A consensus linkage map of the grass carp (Ctenopharyngodon idella) based on microsatellites and SNPs

<p>Abstract</p> <p>Background</p> <p>Grass carp (<it>Ctenopharyngodon idella</it>) belongs to the family Cyprinidae which includes more than 2000 fish species. It is one of the most important freshwater food fish species in world aquaculture. A linkage map is an essential framework for mapping traits of interest and is often the first step towards understanding genome evolution. The aim of this study is to construct a first generation genetic map of grass carp using microsatellites and SNPs to generate a new resource for mapping QTL for economically important traits and to conduct a comparative mapping analysis to shed new insights into the evolution of fish genomes.</p> <p>Results</p> <p>We constructed a first generation linkage map of grass carp with a mapping panel containing two F1 families including 192 progenies. Sixteen SNPs in genes and 263 microsatellite markers were mapped to twenty-four linkage groups (LGs). The number of LGs was corresponding to the haploid chromosome number of grass carp. The sex-specific map was 1149.4 and 888.8 cM long in females and males respectively whereas the sex-averaged map spanned 1176.1 cM. The average resolution of the map was 4.2 cM/locus. BLAST searches of sequences of mapped markers of grass carp against the whole genome sequence of zebrafish revealed substantial macrosynteny relationship and extensive colinearity of markers between grass carp and zebrafish.</p> <p>Conclusions</p> <p>The linkage map of grass carp presented here is the first linkage map of a food fish species based on co-dominant markers in the family Cyprinidae. This map provides a valuable resource for mapping phenotypic variations and serves as a reference to approach comparative genomics and understand the evolution of fish genomes and could be complementary to grass carp genome sequencing project.</p

CINFormer: Transformer network with multi-stage CNN feature injection for surface defect segmentation

Surface defect inspection is of great importance for industrial manufacture and production. Though defect inspection methods based on deep learning have made significant progress, there are still some challenges for these methods, such as indistinguishable weak defects and defect-like interference in the background. To address these issues, we propose a transformer network with multi-stage CNN (Convolutional Neural Network) feature injection for surface defect segmentation, which is a UNet-like structure named CINFormer. CINFormer presents a simple yet effective feature integration mechanism that injects the multi-level CNN features of the input image into different stages of the transformer network in the encoder. This can maintain the merit of CNN capturing detailed features and that of transformer depressing noises in the background, which facilitates accurate defect detection. In addition, CINFormer presents a Top-K self-attention module to focus on tokens with more important information about the defects, so as to further reduce the impact of the redundant background. Extensive experiments conducted on the surface defect datasets DAGM 2007, Magnetic tile, and NEU show that the proposed CINFormer achieves state-of-the-art performance in defect detection

Global Context Aggregation Network for Lightweight Saliency Detection of Surface Defects

Surface defect inspection is a very challenging task in which surface defects usually show weak appearances or exist under complex backgrounds. Most high-accuracy defect detection methods require expensive computation and storage overhead, making them less practical in some resource-constrained defect detection applications. Although some lightweight methods have achieved real-time inference speed with fewer parameters, they show poor detection accuracy in complex defect scenarios. To this end, we develop a Global Context Aggregation Network (GCANet) for lightweight saliency detection of surface defects on the encoder-decoder structure. First, we introduce a novel transformer encoder on the top layer of the lightweight backbone, which captures global context information through a novel Depth-wise Self-Attention (DSA) module. The proposed DSA performs element-wise similarity in channel dimension while maintaining linear complexity. In addition, we introduce a novel Channel Reference Attention (CRA) module before each decoder block to strengthen the representation of multi-level features in the bottom-up path. The proposed CRA exploits the channel correlation between features at different layers to adaptively enhance feature representation. The experimental results on three public defect datasets demonstrate that the proposed network achieves a better trade-off between accuracy and running efficiency compared with other 17 state-of-the-art methods. Specifically, GCANet achieves competitive accuracy (91.79% $F_{\beta}^{w}$, 93.55% $S_\alpha$, and 97.35% $E_\phi$) on SD-saliency-900 while running 272fps on a single gpu

An Optimized Channel Selection Method Based on Multifrequency CSP-Rank for Motor Imagery-Based BCI System

Background. Due to the redundant information contained in multichannel electroencephalogram (EEG) signals, the classification accuracy of brain-computer interface (BCI) systems may deteriorate to a large extent. Channel selection methods can help to remove task-independent electroencephalogram (EEG) signals and hence improve the performance of BCI systems. However, in different frequency bands, brain areas associated with motor imagery are not exactly the same, which will result in the inability of traditional channel selection methods to extract effective EEG features. New Method. To address the above problem, this paper proposes a novel method based on common spatial pattern- (CSP-) rank channel selection for multifrequency band EEG (CSP-R-MF). It combines the multiband signal decomposition filtering and the CSP-rank channel selection methods to select significant channels, and then linear discriminant analysis (LDA) was used to calculate the classification accuracy. Results. The results showed that our proposed CSP-R-MF method could significantly improve the average classification accuracy compared with the CSP-rank channel selection method

Microwave-Assisted Biosynthesis of Ag/ZrO2 Catalyst with Excellent Activity toward Selective Oxidation of 1,2-Propanediol

通讯作者地址: Huang, JLIn the biorefining process, polyols are important intermediates, and the oxidation of polyols toward other value added products is of great significance. This work describes a green and facile biosynthesis method for the preparation of Ag/ZrO2 catalyst for selective oxidation of 1,2-propanediol (a typical polyol). Cinnamomum comphora (CC) leaf extract was employed as the reducing and capping agent for the preparation of Ag nanoparticles (NPs) with the assistance of microwave irradiation. The main reducing agents were identified as polyphenols by Fourier transform infrared spectroscopic analysis of CC extracts before and after reaction. After electrostatic adsorption, the NPs were anchored onto the support ZrO2. The Ag/ZrO2 catalysts were found with good dispersity and showed excellent activity toward selective oxidation of 1,2-propanediol. The effects of the preparation conditions on catalyst activity were studied; the optimal condition was obtained (microwave time of 4 min, CC concentration of 12 g/L and Ag loading of 5%). Since the natural capping agents are easy to remove, the catalysts need no calcination treatment before catalytic reaction. Thus, the microwave-assisted biosynthesis appears to be environmentally benign as neither expensive chemicals nor intensive energy consumption is engaged.National Nature Science Foundation 21036004 2120614

Carbene-Metal-Amide Polycrystalline Materials Feature Blueshifted Energy yet Unchanged Kinetics of Emission

The nature of carbene-metal-amide (CMA) photoluminescence in the solid state is explored through spectroscopic and quantum-chemical investigations on a representative Au-centered molecule. The crystalline phase offers well-defined coplanar geometries-enabling the link between molecular conformations and photophysical properties to be unravelled. We show that a combination of restricted torsional distortion and molecular electronic polarization blue shift the charge-transfer emission by around 400 meV in the crystalline versus the amorphous phase, through energetically raising the less-dipolar S1 state relative to S0. This blue shift brings the lowest charge-transfer states very close to the localized carbazole triplet state, whose structured emission is observable at low temperature in the polycrystalline phase. Moreover, we discover that the rate of intersystem crossing and emission kinetics are unaffected by the extent of torsional distortion. We conclude that more coplanar triplet equilibrium conformations control the photophysics of CMAs