Slot‐die coating is recognized as the most compatible method for the roll‐to‐roll (R2R) processing of large‐area flexible organic solar cells (OSCs). However, the photovoltaic performance of ...large‐area flexible OSC lags significantly behind that of traditional spin‐coating devices. In this work, two acceptors, Qx‐1 and Qx‐2, show quite different film‐formation kinetics in the slot‐die coating process. In situ absorption spectroscopy indicates that the excessive crystallinity of Qx‐2 provides early phase separation and early aggregation, resulting in oversized crystal domains. Consequently, the PM6:Qx‐1‐based 1 cm2 flexible device exhibits an excellent power conversion efficiency (PCE) of 13.70%, which is the best performance among the slot‐die‐coated flexible devices; in contrast, the PM6:Qx‐2 blend shows a pretty poor efficiency, which is lower than 1%. Moreover, the 30 cm2 modules based on PM6:Qx‐1, containing six 5 cm2 sub‐cells, exhibit a PCE of 12.20%. After being stored in a glove box for over 6000 h, the PCE remains at 103% of its initial values, indicating excellent shelf stability. Therefore, these results show a promising future strategy for the upscaling fabrication of flexible large‐area OSCs.
In situ absorption measurement is used to investigate the aggregation behavior of acceptors during slot‐die‐coating. The 1 cm2 flexible device can reach a power conversion efficiency of 13.70%, with excellent shelf stability and upscaling ability. The connected modules (180 cm2) can effectively power a smartphone, showing great potential for future applications.
Drug delivery systems are generally believed to comprise drugs and excipients. A peptide–drug conjugate is a single molecule that can simultaneously play multiple roles in a drug delivery system, ...such as in vivo drug distribution, targeted release, and bioactivity functions. This molecule can be regarded as an integrated drug delivery system, so it is called a molecular drug delivery system. In the context of cancer therapy, a peptide–drug conjugate comprises a tumor-targeting peptide, a payload, and a linker. Tumor-targeting peptides specifically identify membrane receptors on tumor cells, improve drug-targeted therapeutic effects, and reduce toxic and side effects. Payloads with bioactive functions connect to tumor-targeting peptides through linkers. In this review, we explored ongoing clinical work on peptide–drug conjugates targeting various receptors. We discuss the binding mechanisms of tumor-targeting peptides and related receptors, as well as the limiting factors for peptide–drug conjugate-based molecular drug delivery systems.
A single peptide–drug conjugate molecule achieves multiple biological functions, which is proposed as a novel drug delivery system, the molecular drug delivery system.Recently, peptide–drug conjugates have been introduced as potential diagnostics and anticancer drugs in the clinic.Aminopeptidase N, integrins, the somatostatin receptor, and several other receptors are major molecular targeting receptors for current peptide–drug conjugate-based molecular drug delivery systems.Solving the problem of oral administration will greatly promote the development of peptide–drug conjugate molecular drug delivery systems.
Protein structures in the Protein Data Bank provide a wealth of data about the interactions that determine the native states of proteins. Using the probability theory, we derive an atomic ...distance‐dependent statistical potential from a sample of native structures that does not depend on any adjustable parameters (Discrete Optimized Protein Energy, or DOPE). DOPE is based on an improved reference state that corresponds to noninteracting atoms in a homogeneous sphere with the radius dependent on a sample native structure; it thus accounts for the finite and spherical shape of the native structures. The DOPE potential was extracted from a nonredundant set of 1472 crystallographic structures. We tested DOPE and five other scoring functions by the detection of the native state among six multiple target decoy sets, the correlation between the score and model error, and the identification of the most accurate non‐native structure in the decoy set. For all decoy sets, DOPE is the best performing function in terms of all criteria, except for a tie in one criterion for one decoy set. To facilitate its use in various applications, such as model assessment, loop modeling, and fitting into cryo‐electron microscopy mass density maps combined with comparative protein structure modeling, DOPE was incorporated into the modeling package MODELLER‐8.
Matching images and sentences demands a fine understanding of both modalities. In this article, we propose a new system to discriminatively embed the image and text to a shared visual-textual space. ...In this field, most existing works apply the ranking loss to pull the positive image/text pairs close and push the negative pairs apart from each other. However, directly deploying the ranking loss on heterogeneous features (i.e., text and image features) is less effective, because it is hard to find appropriate triplets at the beginning. So the naive way of using the ranking loss may compromise the network from learning inter-modal relationship. To address this problem, we propose the instance loss, which explicitly considers the intra-modal data distribution. It is based on an unsupervised assumption that each image/text group can be viewed as a class. So the network can learn the fine granularity from every image/text group. The experiment shows that the instance loss offers better weight initialization for the ranking loss, so that more discriminative embeddings can be learned. Besides, existing works usually apply the off-the-shelf features, i.e., word2vec and fixed visual feature. So in a minor contribution, this article constructs an end-to-end dual-path convolutional network to learn the image and text representations. End-to-end learning allows the system to directly learn from the data and fully utilize the supervision. On two generic retrieval datasets (Flickr30k and MSCOCO), experiments demonstrate that our method yields competitive accuracy compared to state-of-the-art methods. Moreover, in language-based person retrieval, we improve the state of the art by a large margin. The code has been made publicly available.
This paper is concerned with the global exponential stabilization of memristor-based chaotic neural networks with both time-varying delays and general activation functions. Here, we adopt nonsmooth ...analysis and control theory to handle memristor-based chaotic neural networks with discontinuous right-hand side. In particular, several new sufficient conditions ensuring exponential stabilization of memristor-based chaotic neural networks are obtained via periodically intermittent control. In addition, the proposed results here are easy to verify and they also extend the earlier publications. Finally, numerical simulations illustrate the effectiveness of the obtained results.
The goal of person reidentification (Re-ID) is to identify a given pedestrian from a network of nonoverlapping surveillance cameras. Most existing works follow the supervised learning paradigm which ...requires pairwise labeled training data for each pair of cameras. However, this limits their scalability to real-world applications where abundant unlabeled data are available. To address this issue, we propose a multi-feature fusion with adaptive graph learning model for unsupervised Re-ID. Our model aims to negotiate comprehensive assessment on the consistent graph structure of pedestrians with the help of special information of feature descriptors. Specifically, we incorporate multi-feature dictionary learning and adaptive multi-feature graph learning into a unified learning model such that the learned dictionaries are discriminative and the subsequent graph structure learning is accurate. An alternating optimization algorithm with proved convergence is developed to solve the final optimization objective. Extensive experiments on four benchmark data sets demonstrate the superiority and effectiveness of the proposed method.
Just as graphene triggered a new gold rush, three-dimensional graphene-based macrostructures (3D GBM) have been recognized as one of the most promising strategies for bottom-up nanotechnology and ...become one of the most active research fields during the last four years. In general, the basic structural features of 3D GBM, including its large surface area, which enhances the opportunity to contact pollutants, and its well-defined porous structure, which facilitates the diffusion of pollutant molecules into the 3D structure, enable 3D GBM to be an ideal material for pollutant management due to its excellent capabilities and easy recyclability. This review aims to describe the environmental applications and mechanisms of 3D GBM and provide perspective. Thus, the excellent performance of 3D GBM in environmental pollutant adsorption, transformation and detection are reviewed. Based on the structures and properties of 3D GBM, the removal mechanisms for dyes, oils, organic solvents, heavy metals, and gas pollutants are highlighted. We attempt to establish “structure–property–application” relationships for environmental pollution management using 3D GBM. Approaches involving tunable synthesis and decoration to regulate the micro-, meso-, and macro-structure and the active sites are also reviewed. The high selectivity, fast rate, convenient management, device applications and recycling utilization of 3D GBM are also emphasized.
This paper investigates global projective synchronization of nonidentical fractional-order neural networks (FNNs) based on sliding mode control technique. We firstly construct a fractional-order ...integral sliding surface. Then, according to the sliding mode control theory, we design a sliding mode controller to guarantee the occurrence of the sliding motion. Based on fractional Lyapunov direct methods, system trajectories are driven to the proposed sliding surface and remain on it evermore, and some novel criteria are obtained to realize global projective synchronization of nonidentical FNNs. As the special cases, some sufficient conditions are given to ensure projective synchronization of identical FNNs, complete synchronization of nonidentical FNNs and anti-synchronization of nonidentical FNNs. Finally, one numerical example is given to demonstrate the effectiveness of the obtained results.
Background and Purpose
Atopic dermatitis is a common chronic pruritic inflammatory disease of the skin involving neuro‐immune communication. Neuronal mechanism‐based therapeutic treatments remain ...lacking. We investigated the efficacy of intravenous lidocaine therapy on atopic dermatitis and the underlying neuro‐immune mechanism.
Experimental Approach
Pharmacological intervention, immunofluorescence, RNA‐sequencing, genetic modification and immunoassay were performed to dissect the neuro‐immune basis of itch and inflammation in atopic dermatitis‐like mouse model and in patients.
Key Results
Lidocaine alleviated skin lesions and itch in both atopic dermatitis patients and calcipotriol (MC903)‐induced atopic dermatitis model by blocking subpopulation of sensory neurons. QX‐314, a charged NaV blocker that enters through pathologically activated large‐pore ion channels and selectivity inhibits a subpopulation of sensory neurons, has the same effects as lidocaine in atopic dermatitis model. Genetic silencing NaV1.8‐expressing sensory neurons was sufficient to restrict cutaneous inflammation and itch in the atopic dermatitis model. However, pharmacological blockade of TRPV1‐positive nociceptors only abolished persistent itch but did not affect skin inflammation in the atopic dermatitis model, indicating a difference between sensory neuronal modulation of skin inflammation and itch. Inhibition of activity‐dependent release of calcitonin gene‐related peptide (CGRP) from sensory neurons by lidocaine largely accounts for the therapeutic effect of lidocaine in the atopic dermatitis model.
Conclusion and Implications
NaV1.8+ sensory neurons play a critical role in pathogenesis of atopic dermatitis and lidocaine is a potential anti‐inflammatory and anti‐pruritic agent for atopic dermatitis. A dissociable difference for sensory neuronal modulation of skin inflammation and itch contributes to further understanding of pathogenesis in atopic dermatitis.
We propose a new concept exploiting thermally activated delayed fluorescence (TADF) molecules as photosensitizers, storage units and signal transducers to harness solar thermal energy. Molecular ...composites based on the TADF core phenoxazine-triphenyltriazine (PXZ-TRZ) anchored with norbornadiene (NBD) were synthesized, yielding compounds PZDN and PZTN with two and four NBD units, respectively. Upon visible-light excitation, energy transfer to the triplet state of NBD occurred, followed by NBD → quadricyclane (QC) conversion, which can be monitored by changes in steady-state or time-resolved spectra. The small S
-T
energy gap was found to be advantageous in optimizing the solar excitation wavelength. Upon tuning the molecule's triplet state energy lower than that of NBD (61 kcal/mol), as achieved by another composite PZQN, the efficiency of the NBD → QC conversion decreased drastically. Upon catalysis, the reverse QC → NBD reaction occurred at room temperature, converting the stored chemical energy back to heat with excellent reversibility.
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