Covalent Organic Framework Ionomer Steering the CO2 Electroreduction Pathway on Cu at Industrial-Grade Current Density.

CO2 electroreduction holds great promise for addressing global energy and sustainability challenges. Copper (Cu) shows great potential for effective conversion of CO2 toward specific value-added and/or high-energy-density products. However, its limitation lies in relatively low product selectivity. Herein, we present that the CO2 reduction reaction (CO2RR) pathway on commercially available Cu can be rationally steered by modulating the microenvironment in the vicinity of the Cu surface with two-dimensional sulfonated covalent organic framework nanosheet (COF-NS)-based ionomers. Specifically, the selectivity toward methane (CH4) can be enhanced to more than 60% with the total current density up to 500 mA cm-2 in flow cells in both acidic (pH = 2) and alkaline (pH = 14) electrolytes. The COF-NS, characterized by abundant apertures, can promote the accumulation of CO2 and K+ near the catalyst surface, alter the adsorption energy and surface coverage of *CO, facilitate the dissociation of H2O, and finally modulate the reaction pathway for the CO2RR. Our approach demonstrates the rational modulation of reaction interfaces for the CO2RR utilizing porous open framework ionomers, showcasing their potential practical applications.

A context-dependent perspective to understand the relation between parent-child physiological synchrony and child socioemotional adjustment.

Physiological synchrony is an important biological process during which parent-child interaction plays a significant role in shaping child socioemotional adjustment. The present study held a context-dependent perspective to examine the conditional association between parent-child physiological synchrony and child socioemotional adjustment (i.e., relationship quality with parents and child emotion regulation) under different (i.e., from highly unsupportive to highly supportive) emotional contexts. One hundred and fifty school-age Chinese children (Mage = 8.64 years, 63 girls) and their primary caregivers participated in this study. After attaching electrocardiogram (ECG) electrodes, parent-child dyads were instructed to complete a 4-minute conflict discussion task. Parent-child physiological synchrony was calculated based on the within-dyad association between parents' and children's respiratory sinus arrhythmia (RSA) levels across eight 30-second epochs. Parental emotional support, child relationship quality with parents, and child emotion regulation during the discussion task were coded by trained research assistants. Supporting our hypotheses, parental emotional support moderated the relations of parent-child RSA synchrony with both child relationship quality with parents and child emotion regulation. Furthermore, the Johnson-Neyman technique of moderation indicated that the associations between parent and child RSA synchrony and child socioemotional adjustment indicators shifted from negative to positive as the parental emotional support became increasingly high. Our findings suggest that parent-child physiological synchrony may not be inherently adaptive or maladaptive, highlighting the importance of understanding the function of parent-child physiological synchrony under specific contexts. RESEARCH HIGHLIGHTS: Physiological synchrony may not be inherently adaptive or maladaptive, and the meanings of parent-child physiological synchrony might be contingent on contextual factors. Parental emotional support moderated the relations between parent-child respiratory sinus arrhythmia (RSA) synchrony and child socioemotional adjustment indicators (i.e., child relationship quality with parents and child emotion regulation). More positive/less negative parent-child RSA synchrony was associated with better child socioemotional adjustment under a supportive emotional context, whereas with poorer child socioemotional adjustment under an unsupportive emotional context. These findings highlight the significance of considering the emotional context in physiological synchrony studies.

Gradient Channel Segmentation in Covalent Organic Framework Membranes with Highly Oriented Nanochannels.

Covalent organic frameworks (COFs) offer an exceptional platform for constructing membrane nanochannels with tunable pore sizes and tailored functionalities, making them promising candidates for separation, catalysis, and sensing applications. However, the synthesis of COF membranes with highly oriented nanochannels remains challenging, and there is a lack of systematic studies on the influence of postsynthetic modification reactions on functionality distribution along the nanochannels. Herein, we introduced a "prenucleation and slow growth" approach to synthesize a COF membrane featuring highly oriented mesoporous channels and a high Brunauer-Emmett-Teller surface area of 2230 m2 g-1. Functional moieties were anchored to the pore walls via "click" reactions and coordinated with Cu ions to serve as segmentation functions. This led to a remarkable H2/CO2 separation performance that surpassed the Robeson upper bound. Moreover, we found that the functionalities distributed along the nanochannels could be influenced by functionality flexibility and postsynthetic reaction rate. This strategy paved the way for the accurate design and construction of COF-based artificial solid-state nanochannels with high orientation and precisely controlled channel environments.

Co-designed metaoptoelectronic deep learning.

A metaoptical system is co-designed with electronic hardware to implement deep learning image recognition. The optical convolution block includes a reflective metasurface to perform one layer of a deep neural network. The optical and digital components are jointly optimized to perform an image classification task attaining 65% accuracy, which is close to the 66% accuracy of a fully-digital network where the optical block is replaced by a digital convolution layer.

Data augmentation using continuous conditional generative adversarial networks for regression and its application to improved spectral sensing.

Machine learning-assisted spectroscopy analysis faces a prominent constraint in the form of insufficient spectral samples, which hinders its effectiveness. Meanwhile, there is a lack of effective algorithms to simulate synthetic spectra from limited samples of real spectra for regression models in continuous scenarios. In this study, we introduced a continuous conditional generative adversarial network (CcGAN) to autonomously generate synthetic spectra. The labels employed for generating the spectral data can be arbitrarily selected from within the range of labels associated with the real spectral data. Our approach effectively produced spectra using a small spectral dataset obtained from a self-interference microring resonator (SIMRR)-based sensor. The generated synthetic spectra were subjected to evaluation using principal component analysis, revealing an inability to discern them from the real spectra. Finally, to enhance the DNN regression model, these synthetic spectra are incorporated into the original training dataset as an augmentation technique. The results demonstrate that the synthetic spectra generated by CcGAN exhibit exceptional quality and significantly enhance the predictive performance of the DNN model. In conclusion, CcGAN exhibits promising potential in generating high-quality synthetic spectra and delivers a superior data augmentation effect for regression tasks.

Biallelic mutations in LSS in autosomal-recessive mutilating palmoplantar keratoderma.

Mutilating palmoplantar keratoderma (PPK) is a heterogeneous genetic disease that poses enormous challenges to clinical diagnosis and genetic counselling. Lanosterol synthase (LSS) gene encodes LSS involved in the biosynthesis pathway of cholesterol. Biallelic mutations in LSS were found to be related to diseases such as cataracts, hypotrichosis and palmoplantar keratoderma-congenital alopecia syndrome. The aim of this study was to investigate the contribution of the LSS mutation to mutilating PPK in a Chinese patient. The clinical and molecular characteristics of the patient were evaluated. A 38-year-old male patient with mutilating PPK was recruited in this study. We identified biallelic variants in the LSS gene (c.683C > T, p.Thr228Ile and c.779G > A, p.Arg260His). Immunoblotting revealed that the Arg260His mutant showed a significantly reduced expression level while Thr228Ile showed an expression level similar to that of the wild type. Thin layer chromatography revealed that mutant Thr228Ile retained partial enzymatic activity and mutant Arg260His did not show any catalytic activity. Our findings show the correlation between LSS mutations and mutilating PPK.

The Influence of Storage in Saline or Irradiation by Ultraviolet on Surface Hydrophilicity of Implant and Osseointegration: An Experimental Study in Rabbits.

Storage in aqueous solution or ultraviolet (UV) irradiation can retain or regain the hydrophilicity of titanium implant surface. In this study, 3 types of commercial titanium implants were used: ZBL (ZDI Bone Level), CEL (C-tech Esthetic Line), and modSLA (Straumann SLActive). ZBL and CEL implants were treated with UV irradiation for 4 hours. Surface characterization of the 4 groups (ZBL, ZBL-UV, CEL-UV, and modSLA) was evaluated by scanning electron microscopy and contact angle measurements. The in vivo bone response was evaluated by removal torque (RTQ) tests and histomorphometric analysis at 3, 6, and 12 weeks postimplantation. A total of 144 implants and 36 rabbits were used for experiments according to a previously established randomization sequence. The ZBL-UV, CEL-UV, and modSLA groups were hydrophilic, and nanostructures were observed on the modSLA implant surface. ModSLA achieved better RTQ value than ZBL at 12 weeks (P < .05). For histomorphometric analysis, ZBL-UV and CEL-UV implants showed higher bone area values in the cancellous bone zone at 6 weeks than did modSLA and ZBL implants (P < .05). In the cortical bone zone, all groups showed comparable bone-to-implant contact at all healing time points (P > .05). Both storage in saline and UV irradiation could retain or provoke hydrophilic surfaces and improve osseointegration. Compared with storage in saline, UV irradiation displayed slight advantages in promoting new bone formation in cancellous bone zone at an early stage.

Hierarchical Microtubular Covalent Organic Frameworks Achieved by COF-to-COF Transformation.

Pore environment and aggregated structure play a vital role in determining the properties of porous materials, especially regarding the mass transfer. Reticular chemistry imparts covalent organic frameworks (COFs) with well-aligned micro/mesopores, yet constructing hierarchical architectures remains a great challenge. Herein, we reported a COF-to-COF transformation methodology to prepare microtubular COFs. In this process, the C3 -symmetric guanidine units decomposed into C2 -symmetric hydrazine units, leading to the crystal transformation of COFs. Moreover, the aggregated structure and conversion degree varied with the reaction time, where the hollow tubular aggregates composed of mixed COF crystals could be obtained. Such hierarchical architecture leads to enhanced mass transfer properties, as proved by the adsorption measurement and chemical catalytic reactions. This self-template strategy was successfully applied to another four COFs with different building units.

2D Covalent Organic Framework for Water Harvesting with Fast Kinetics and Low Regeneration Temperature.

Atmospheric water harvesting represents a promising technique to address water stress. Advanced adsorbents have been rationally designed to achieve high water uptake, yet their water sorption kinetics and regeneration temperature greatly limit water production efficiency. Herein, we demonstrated that 2D covalent organic frameworks (COFs), featuring hydrophobic skeleton, proper hydrophilic site density, and 1D open channels significantly lowered the water diffusion and desorption energy barrier. DHTA-Pa COF showed a high water uptake of 0.48 g/g at 30 % R.H. with a remarkable adsorption rate of 0.72 L/Kg/h (298 K) and a desorption rate of 2.58 L/Kg/h (333 K). Moreover, more than 90 % adsorbed water could be released within 20 min at 313 K. This kinetic performance surpassed the reported porous materials and boosted the efficiency for multiple water extraction cycles. It may shed light on the material design strategy to achieve high daily water production with low-energy input.

Metal-Organic Frameworks for Breakthrough Separation of 2-Butene Isomers with High Dynamic Selectivity and Capacity.

Developing porous sorbents represents a potential energy-efficient way for industrial gas separation. However, a bottleneck for reducing the energy penalty is the trade-off between dynamic adsorption capacity and selectivity. Herein, we showed this problem can be overcome by modulating the kinetic and thermodynamic separation behaviours in metal-organic frameworks for sieving 2-butene geometric isomers, which are desired for upgrading the raffinates to higher value-added end products. We found that the iron-triazolate framework can realize the selective shape screening of 2-butene isomers assisted by electrostatic interactions at the pore apertures. Further introducing uncoordinated N binding sites by ligand substitution lowered the gas diffusion barrier and greatly boosted the dynamic separation performance. In breakthrough tests under ambient conditions, trans-2-C4 H8 can be efficiently separated from cis-2-C4 H8 with a record capacity of 2.10 mmol g-1 with high dynamic selectivity of 2.39.

Covalent Organic Frameworks with Record Pore Apertures.

The pore apertures dictate the guest accessibilities of the pores, imparting diverse functions to porous materials. It is highly desired to construct crystalline porous polymers with predesignable and uniform mesopores that can allow large organic, inorganic, and biological molecules to enter. However, due to the ease of the formation of interpenetrated and/or fragile structures, the largest pore aperture reported in the metal-organic frameworks is 8.5 nm, and the value for covalent organic frameworks (COFs) is only 5.8 nm. Herein, we construct a series of COFs with record pore aperture values from 7.7 to 10.0 nm by designing building blocks with large conformational rigidness, planarity, and suitable local polarity. All of the obtained COFs possess eclipsed stacking structures, high crystallinity, permanent porosity, and high stability. As a proof of concept, we successfully employed these COFs to separate pepsin that is ∼7 nm in size from its crudes and to protect tyrosinase from heat-induced deactivation.

Hydrophilicity gradient in covalent organic frameworks for membrane distillation.

Desalination can help to alleviate the fresh-water crisis facing the world. Thermally driven membrane distillation is a promising way to purify water from a variety of saline and polluted sources by utilizing low-grade heat. However, membrane distillation membranes suffer from limited permeance and wetting owing to the lack of precise structural control. Here, we report a strategy to fabricate membrane distillation membranes composed of vertically aligned channels with a hydrophilicity gradient by engineering defects in covalent organic framework films by the removal of imine bonds. Such functional variation in individual channels enables a selective water transport pathway and a precise liquid-vapour phase change interface. In addition to having anti-fouling and anti-wetting capability, the covalent organic framework membrane on a supporting layer shows a flux of 600 l m-2 h-1 with 85 °C feed at 16 kPa absolute pressure, which is nearly triple that of the state-of-the-art membrane distillation membrane for desalination. Our results may promote the development of gradient membranes for molecular sieving.

Construction of Interlayer Conjugated Links in 2D Covalent Organic Frameworks via Topological Polymerization.

Two-dimensional covalent organic frameworks (2D COFs) are well-defined polymeric sheets that usually stack in an eclipsed mode via van der Waals forces. Extensive efforts have been made to manipulate interlayer interactions, yet there still lack a way to construct conjugated connections between adjacent layers, which is important for (opto)electronic-related applications. Herein, we report an interlayer topological polymerization strategy to transform the well-organized diacetylene columnar arrays in three different 2D COFs (TAPFY-COF, TAPB-COF, and TAPP-COF) into conjugated enyne chains upon heating in the solid state. The resultant COFs (COF-P) with retained high crystallinity possess broadened absorption bands and narrowed band gaps. The newly formed conjugated chains provide extra charge carrier pathways through direct π-electron delocalization. As a proof-of-concept, after topological polymerization, the conductivity of the TAPFY-COF film achieves 2.8 × 10-4 S/cm without doping, and the photothermal, photoacoustic, and oxygen reduction catalytic performance of TAPP-COF is significantly improved.

Design framework for metasurface optics-based convolutional neural networks.

Deep learning using convolutional neural networks (CNNs) has been shown to significantly outperform many conventional vision algorithms. Despite efforts to increase the CNN efficiency both algorithmically and with specialized hardware, deep learning remains difficult to deploy in resource-constrained environments. In this paper, we propose an end-to-end framework to explore how to optically compute the CNNs in free-space, much like a computational camera. Compared to existing free-space optics-based approaches that are limited to processing single-channel (i.e., gray scale) inputs, we propose the first general approach, based on nanoscale metasurface optics, that can process RGB input data. Our system achieves up to an order of magnitude energy savings and simplifies the sensor design, all the while sacrificing little network accuracy.

Metal-Triazolate-Framework-Derived FeN4 Cl1 Single-Atom Catalysts with Hierarchical Porosity for the Oxygen Reduction Reaction.

The construction of single-atom catalysts (SACs) with high single atom densities, favorable electronic structures and fast mass transfer is highly desired. We have utilized metal-triazolate (MET) frameworks, a subclass of metal-organic frameworks (MOFs) with high N content, as precursors since they can enhance the density and regulate the electronic structure of single-atom sites, as well as generate abundant mesopores simultaneously. Fe single atoms dispersed in a hierarchically porous N-doped carbon matrix with high metal content (2.78 wt %) and a FeN4 Cl1 configuration (FeN4 Cl1 /NC), as well as mesopores with a pore:volume ratio of 0.92, were obtained via the pyrolysis of a Zn/Fe-bimetallic MET modified with 4,5-dichloroimidazole. FeN4 Cl1 /NC exhibits excellent oxygen reduction reaction (ORR) activity in both alkaline and acidic electrolytes. Density functional theory calculations confirm that Cl can optimize the adsorption free energy of Fe sites to *OH, thereby promoting the ORR process. The catalyst demonstrates great potential in zinc-air batteries. This strategy selects, designs, and adjusts MOFs as precursors for high-performance SACs.

Molecular-Sieving Membrane by Partitioning the Channels in Ultrafiltration Membrane by In†Situ Polymerization.

Commercial ultrafiltration membranes have proliferated globally for water treatment. However, their pore sizes are too large to sieve gases. Conjugated microporous polymers (CMPs) feature well-developed microporosity yet are difficult to be fabricated into membranes. Herein, we report a strategy to prepare molecular-sieving membranes by partitioning the mesoscopic channels in water ultrafiltration membrane (PSU) into ultra-micropores by space-confined polymerization of multi-functionalized rigid building units. Nine CMP@PSU membranes were obtained, and their separation performance for H2 /CO2 , H2 /N2 , and H2 /CH4 pairs surpass the Robeson upper bound and rival against the best of those reported membranes. Furthermore, highly crosslinked skeletons inside the channels result in the structural robustness and transfer into the excellent aging resistance of the CMP@PSU. This strategy may shed light on the design and fabrication of high-performance polymeric gas separation membranes.

Multi-Features Fusion for Fault Diagnosis of Pedal Robot Using Time-Speed Signals.

In order to realize automation of the pollutant emission tests of vehicles, a pedal robot is designed instead of a human-driven vehicle. Sometimes, the actual time-speed curve of the vehicle will deviate from the upper or lower limit of the worldwide light-duty test cycle (WLTC) target curve, which will cause a fault. In this paper, a new fault diagnosis method is proposed and applied to the pedal robot. Since principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE), and Autoencoder cannot extract feature information adequately when they are used alone, three types of feature components extracted by PCA, t-SNE, and Autoencoder are fused to form a nine-dimensional feature set. Then, the feature set is reduced into three-dimensional space via Treelet Transform. Finally, the fault samples are classified by Gaussian process classifier. Compared with the methods using only one algorithm to extract features, the proposed method has the minimum standard deviation, 0.0078, and almost the maximum accuracy, 98.17%. The accuracy of the proposed method is only 0.24% lower than that without Treelet Transform, but the processing time is 6.73% less than that without Treelet Transform. These indicate that the multi-features fusion model and Treelet Transform method is quite effective. Therefore, the proposed method is quite helpful for fault diagnosis of the pedal robot.

[Transcatheter delivery of recombinant adenovirus vector containing exogenous aquaporin gene in treatment of Sjögren's syndrome].

Sjögren's syndrome is a kind of autoimmune disease, whose main clinical symptoms are dry mouth, dry eye and chronic parotid glandular inflammation. The conservative treatments include artificial tears or saliva,oral administration of corticosteroids,and immunosuppressantsl with limited effectiveness. Along with the development of molecular biology, vast attentions are being paid to researches on gene therapy for Sjögren's syndrome, hopefully to bring gospel to patients with Sjögren's syndrome. This article reviews the recent research progresses on transcatheter delivery of recombinant adenovirus vector with aquaporin gene in experimental treatment of Sjögren's syndrome.

Unusual cysteine modifications in natural product biosynthesis.

l-Cysteine is a highly reactive amino acid that is modified into a variety of chemical structures, including cysteine sulfinic acid in human metabolic pathways, and sulfur-containing scaffolds of amino acids, alkaloids, and peptides in natural product biosynthesis. Among the modification enzymes responsible for these cysteine-derived compounds, metalloenzymes constitute an important family of enzymes that catalyze a wide variety of reactions. Therefore, understanding their reaction mechanisms is important for the biosynthetic production of cysteine-derived natural products. This review mainly summarizes recent mechanistic investigations of metalloenzymes, with a particular focus on recently discovered mononuclear non-heme iron (NHI) enzymes, dinuclear NHI enzymes, and radical-SAM enzymes involved in unusual cysteine modifications in natural product biosynthesis.

Cross-domain bearing fault diagnosis using dual-path convolutional neural networks and multi-parallel graph convolutional networks.

Bearing fault diagnosis is significant in ensuring large machinery and equipment's safe and stable operation. However, inconsistent operating environments can lead to data distribution differences between source and target domains. As a result, models trained solely on source-domain data may not perform well when applied to the target domain, especially when the target-domain data is unlabeled. Existing approaches focus on improving domain adaptive methods for effective transfer learning but neglect the importance of extracting comprehensive feature information. To tackle this challenge, we present a bearing fault diagnosis approach using dual-path convolutional neural networks (CNNs) and multi-parallel graph convolutional networks (GCNs), called DPC-MGCN, which can be applied to variable working conditions. To obtain complete feature information, DPC-MGCN leverages dual-path CNNs to extract local and global features from vibration signals in both the source and target domains. The attention mechanism is subsequently applied to identify crucial features, which are converted into adjacency matrices. Multi-parallel GCNs are then employed to further explore the structural information among these features. To minimize the distribution differences between the two domains, we incorporate the multi-kernel maximum mean discrepancy (MK-MMD) domain adaptation method. By applying the DPC-MGCN approach for diagnosing bearing faults under diverse working conditions and comparing it with other methods, we demonstrate its superior performance on various datasets.