Improving Interfacial and Compressive Properties of Polyimide Fiber by Constructing Inorganic Layers on Fiber Surface.

The compressive performance of organic fiber has always been a key problem, limiting its development. In this paper, silicon oxide, alumina, and titanium oxide particles were separately deposited on the surface of high-strength and high-modulus polyimide (PI) fibers to form a structural supporting shell by using a magnetron sputtering method. The theoretical thickness was calculated by thermogravimetric analysis in good agreement with the actual thickness determined from scanning electron microscopy. The mechanics, surface, and interface properties of the measured fibers were analyzed mainly from the aspects of surface energy, interfacial shear strength (IFSS), and compression strength. The results showed that after magnetron sputtering, the inorganic shells were uniformly deposited on the surface of PI fiber, resulting in an increase in the content of inorganic elements as well as the roughness. As a result, the surface energy and IFSS of silica-coated fiber was increased by 174 and 85.6%, respectively, and compression strength was increased by 45.7%. This study provides a new approach for improving the interface property and compression strength of high-strength and high-modulus PI-fiber-reinforced composites.

Deep Electron Redistributions Induced by Dual Junctions Facilitating Electroreduction of Dilute Nitrate to Ammonia.

The electronic states of metal catalysts can be redistributed by the rectifying contact between metal and semiconductor e.g., N-doped carbon (NC), while the interfacial regulation degree is very limited. Herein, a deep electronic state regulation is achieved by constructing a novel double-heterojunctional Co/Co3O4@NC catalyst containing Co/Co3O4 and Co3O4/NC heterojunctions. When used for dilute electrochemical NO3 - reduction reaction (NO3RR), the as-prepared Co/Co3O4@NC exhibits an outstanding Faradaic efficiency for NH3 formation (FENH3) of 97.9%, -0.4 V versus RHE and significant NH3 yield of 303.5 mmol h-1 gcat -1 at -0.6 V at extremely low nitrate concentrations (100 ppm NO3 --N). Experimental and theoretical results reveal that the dual junctions of Co/Co3O4 and Co3O4/NC drive a unidirectional electron transfer from Co to NC (Co→Co3O4→NC), resulting in electron-deficient Co atoms. The electron-deficient Co promotes NO3 - adsorption, the rate-determining step (RDS) for NO3RR, facilitating the dilute NO3RR to NH3. The design strategy provides a novel reference for unidirectional multistage regulation of metal electronic states boosting electrochemical dilute NO3RR, which opens up an avenue for deep electronic state regulation of electrocatalyst breaking the limitation of the electronic regulation degree by rectifying contact.

Anchoring covalent organic polymers on supports with tunable functional groups boosting the oxygen reduction performance under pH-universal conditions.

Iron phthalocyanine (FePc) is an attractive nonprecious metal candidate for electrocatalytic oxygen reduction reaction (ORR). However, its low catalytic performance under acidic and neutral conditions limits its practical application. Herein, the FePc-based covalent organic polymers (COPFePc) polymerized in situ on the functionalized multiwalled carbon nanotubes (R-MWCNT) containing different electron-withdrawing or electron-donating groups (COPFePc/R-MWCNT, R = COOH, OH or NH2) were synthesized for ORR. Among them, COPFePc/COOH-MWCNT exhibited the best ORR performance under pH-universal conditions (acidic, neutral, and alkaline). Density-functional theory (DFT) calculations demonstrate that the electron-withdrawing or electron-donating effect of the functional groups in COPFePc/R-MWCNT causes charge redistribution of the active center Fe. The COOH functional group with an electron-withdrawing ability shifts the d-band center of Fe away from the Fermi energy level and reduces the binding strength of oxygen-containing intermediates, accelerating the ORR kinetics and optimizing the catalytic activity.

Social class perception is driven by stereotype-related facial features.

Social class is a powerful hierarchy that determines many privileges and disadvantages. People form impressions of others' social class (like other important social attributes) from facial appearance, and these impressions correlate with stereotype judgments. However, what drives these related subjective judgments remains unknown. That is, what makes someone look like they are of higher or lower social class standing (e.g., rich or poor), and how does this relate to harmful or advantageous stereotypes? We addressed these questions using a perception-based data-driven method to model the specific three-dimensional facial features that drive social class judgments and compared them to those of stereotype-related judgments (competence, warmth, dominance, and trustworthiness), based on White Western culture participants and face stimuli. Using a complementary data-reduction analysis and machine learning approach, we show that social class judgments are driven by a unique constellation of facial features that reflect multiple embedded stereotypes: poor-looking (vs. rich-looking) faces are wider, shorter, and flatter with downturned mouths and darker, cooler complexions, mirroring features of incompetent, cold, and untrustworthy-looking (vs. competent, warm, and trustworthy-looking) faces. Our results reveal the specific facial features that underlie the connection between impressions of social class and stereotype-related social traits, with implications for central social perception theories, including understanding the causal links between stereotype knowledge and social class judgments. We anticipate that our results will inform future interventions designed to interrupt biased perception and social inequalities. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Strength of predicted information content in the brain biases decision behavior.

Prediction-for-perception theories suggest that the brain predicts incoming stimuli to facilitate their categorization.1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17 However, it remains unknown what the information contents of these predictions are, which hinders mechanistic explanations. This is because typical approaches cast predictions as an underconstrained contrast between two categories18,19,20,21,22,23,24-e.g., faces versus cars, which could lead to predictions of features specific to faces or cars, or features from both categories. Here, to pinpoint the information contents of predictions and thus their mechanistic processing in the brain, we identified the features that enable two different categorical perceptions of the same stimuli. We then trained multivariate classifiers to discern, from dynamic MEG brain responses, the features tied to each perception. With an auditory cueing design, we reveal where, when, and how the brain reactivates visual category features (versus the typical category contrast) before the stimulus is shown. We demonstrate that the predictions of category features have a more direct influence (bias) on subsequent decision behavior in participants than the typical category contrast. Specifically, these predictions are more precisely localized in the brain (lateralized), are more specifically driven by the auditory cues, and their reactivation strength before a stimulus presentation exerts a greater bias on how the individual participant later categorizes this stimulus. By characterizing the specific information contents that the brain predicts and then processes, our findings provide new insights into the brain's mechanisms of prediction for perception.

FGF2 Alleviates Microvascular Ischemia-Reperfusion Injury by KLF2-mediated Ferroptosis Inhibition and Antioxidant Responses.

An essential pathogenic element of acute limb ischemia/reperfusion (I/R) injury is microvascular dysfunction. The majority of studies indicates that fibroblast growth factor 2 (FGF2) exhibits protective properties in cases of acute I/R injury. Albeit its specific role in the context of acute limb I/R injury is yet unknown. An impressive post-reperfusion increase in FGF2 expression was seen in a mouse model of hind limb I/R, followed by a decline to baseline levels, suggesting a key role for FGF2 in limb survivability. FGF2 appeared to reduce I/R-induced hypoperfusion, tissue edema, skeletal muscle fiber injury, as well as microvascular endothelial cells (ECs) damage within the limb, according to assessments of limb vitality, Western blotting, and immunofluorescence results. The bioinformatics analysis of RNA-sequencing revealed that ferroptosis played a key role in FGF2-facilitated limb preservation. Pharmacological inhibition of NFE2L2 prevented ECs from being affected by FGF2's anti-oxidative and anti-ferroptosis activities. Additionally, silencing of kruppel-like factor 2 (KLF2) by interfering RNA eliminated the antioxidant and anti-ferroptosis effects of FGF2 on ECs. Further research revealed that the AMPK-HDAC5 signal pathway is the mechanism via which FGF2 regulates KLF2 activity. Data from luciferase assays demonstrated that overexpression of HDAC5 prevented KLF2 from becoming activated by FGF2. Collectively, FGF2 protects microvascular ECs from I/R injury by KLF2-mediated ferroptosis inhibition and antioxidant responses.

Deep Electronic State Regulation through Unidirectional Cascade Electron Transfer Induced by Dual Junction Boosting Electrocatalysis Performance.

Unidirectional cascade electron transfer induced by multi-junctions is essential for deep electronic state regulation of the catalytic active sites, while this advanced concept has rarely been investigated in the field of electrocatalysis. In the present work, a dual junction heterostructure (FePc/L-R/CN) is designed by anchoring iron phthalocyanine (FePc)/MXene (L-Ti3 C2 -R, R═OH or F) heterojunction on g-C3 N4 nanosheet substrates for electrocatalysis. The unidirectional cascade electron transfer (g-C3 N4 → L-Ti3 C2 -R → FePc) induced by the dual junction of FePc/L-Ti3 C2 -R and L-Ti3 C2 -R/g-C3 N4 makes the Fe center electron-rich and therefore facilitates the adsorption of O2 in the oxygen reduction reaction (ORR). Moreover, the electron transfer between FePc and MXene is facilitated by the axial Fe─O coordination interaction of Fe with the OH in alkalized MXene nanosheets (L-Ti3 C2 -OH). As a result, FePc/L-OH/CN exhibits an impressive ORR activity with a half-wave potential (E1/2 ) of 0.92 V, which is superior over the catalysts with a single junction and the state-of-the-art Pt/C (E1/2 = 0.85 V). This work provides a broad idea for deep regulation of electronic state by the unidirectional cascade multi-step charge transfer and can be extended to other proton-coupled electron transfer processes.

Sulforaphane protects microvascular endothelial cells in lower limb ischemia/reperfusion injury mice.

Background: Microvascular damage is a key pathological factor in acute lower limb ischemia/reperfusion (I/R) injury. Current evidence suggests that sulforaphane (SFN) protects tissue from I/R injury. However, the role of SFN in acute lower limb I/R injury remains elusive. This study aimed to investigate the role and potential mechanism of SFN in I/R-related microvascular damage in the limb. Methods: Limb viability was evaluated by laser Doppler imaging, tissue edema analysis and histological analysis. Western blotting and immunofluorescence were applied to analyze the levels of apoptosis, oxidative stress, autophagy, transcription factor EB (TFEB) activity and mucolipin 1 (MCOLN1)-calcineurin signaling pathway. Results: SFN administration significantly ameliorated I/R-induced hypoperfusion, tissue edema, skeletal muscle fiber injury and endothelial cell (EC) damage in the limb. Pharmacological inhibition of NFE2L2 (nuclear factor, erythroid 2 like 2) reversed the anti-oxidation and anti-apoptosis effects of SFN on ECs. Additionally, silencing of TFEB by interfering RNA abolished the SFN-induced autophagy restoration, anti-oxidant response and anti-apoptosis effects on ECs. Furthermore, silencing of MCOLN1 by interfering RNA and pharmacological inhibition of calcineurin inhibited the activity of TFEB induced by SFN, demonstrating that SFN regulates the activity of TFEB through the MCOLN1-calcineurin signaling pathway. Conclusion: SFN protects microvascular ECs against I/R injury by TFEB-mediated autophagy restoration and anti-oxidant response.

Enhanced electrochemical CO2 reduction performance of cobalt phthalocyanine with precise regulation of electronic states.

Herein, we report a facile strategy for constructing hybrid coordination configurations by combining functionalized graphene quantum dots (GQDs) with CoPc (CoPc/R-GQDs, with R being -NH2 or -OH) for electrochemical CO2 reduction. Benefiting from the high density of functional groups that can be provided by GQDs and the strong electron-donating property of -NH2, the examined CoPc/NH2-GQDs achieved a 100% faradaic efficiency for CO formation (FECO) at -0.8 to -0.9 V vs. RHE, and high FECO (over 90%) over a wide potential range of 500 mV. This work has presented a novel approach for catalyst design, specifically involving molecular engineering of quantum dots, which can also be applied to other essential electrochemical reactions.

Network Communications Flexibly Predict Visual Contents That Enhance Representations for Faster Visual Categorization.

Models of visual cognition generally assume that brain networks predict the contents of a stimulus to facilitate its subsequent categorization. However, understanding prediction and categorization at a network level has remained challenging, partly because we need to reverse engineer their information processing mechanisms from the dynamic neural signals. Here, we used connectivity measures that can isolate the communications of a specific content to reconstruct these network mechanisms in each individual participant (N = 11, both sexes). Each was cued to the spatial location (left vs right) and contents [low spatial frequency (LSF) vs high spatial frequency (HSF)] of a predicted Gabor stimulus that they then categorized. Using each participant's concurrently measured MEG, we reconstructed networks that predict and categorize LSF versus HSF contents for behavior. We found that predicted contents flexibly propagate top down from temporal to lateralized occipital cortex, depending on task demands, under supervisory control of prefrontal cortex. When they reach lateralized occipital cortex, predictions enhance the bottom-up LSF versus HSF representations of the stimulus, all the way from occipital-ventral-parietal to premotor cortex, in turn producing faster categorization behavior. Importantly, content communications are subsets (i.e., 55-75%) of the signal-to-signal communications typically measured between brain regions. Hence, our study isolates functional networks that process the information of cognitive functions.SIGNIFICANCE STATEMENT An enduring cognitive hypothesis states that our perception is partly influenced by the bottom-up sensory input but also by top-down expectations. However, cognitive explanations of the dynamic brain networks mechanisms that flexibly predict and categorize the visual input according to task-demands remain elusive. We addressed them in a predictive experimental design by isolating the network communications of cognitive contents from all other communications. Our methods revealed a Prediction Network that flexibly communicates contents from temporal to lateralized occipital cortex, with explicit frontal control, and an occipital-ventral-parietal-frontal Categorization Network that represents more sharply the predicted contents from the shown stimulus, leading to faster behavior. Our framework and results therefore shed a new light of cognitive information processing on dynamic brain activity.

Tuning the Hydroxyl Density of MXene to Regulate the Electrochemical Performance of Anchored Cobalt Phthalocyanine for CO2 Reduction.

Precise electronic state regulation through coordination environment optimization by metal-support interaction is a promising strategy to facilitate catalysis reaction, while the limited density of functional groups in the bulk substrate restricts the regulation degree. Herein, different sizes of Ti3C2Tx MXene with hydroxyl (-OH) terminal including the MXene layer (ML-OH, 3 µm), the MXene nanosheet (MNS-OH, 600 nm), and the MXene quantum dot (MQD-OH, 8 nm) were prepared to anchor CoPc, and the effect of -OH density on the performance of electrochemical CO2 reduction was systematically investigated. Notably, a linear relationship was established by plotting reactivity vs hydroxyl density. With the highest -OH density, CoPc/MQD-OH exhibits a superior Faradaic efficiency for CO formation (FECO) of ∼100% at -0.9 to -1.0 V vs RHE and a high FECO of >90% over a wide potential window from -0.8 to -1.4 V. The mechanism exploration shows that the axial coordination interaction of the -OH terminal with Co increases the electron density of the Co site, thus promoting the adsorption and activation of CO2. This work provides a new insight into designing of molecular catalysts with high efficiency and tunable structure for other electrochemical conversions.

Dopant-Induced Electronic States Regulation Boosting Electroreduction of Dilute Nitrate to Ammonium.

Electrochemically converting NO3 - into NH3 offers a promising route for water treatment. Nevertheless, electroreduction of dilute NO3 - is still suffering from low activity and/or selectivity. Herein, B as a modifier was introduced to tune electronic states of Cu and further regulate the performance of electrochemical NO3 - reduction reaction (NO3 RR) with dilute NO3 - concentration (≤100 ppm NO3 - -N). Notably, a linear relationship was established by plotting NH3 yield vs. the oxidation state of Cu, indicating that the increase of Cu+ content leads to an enhanced NO3 - -to-NH3 conversion activity. Under a low NO3 - -N concentration of 100 ppm, the optimal Cu(B) catalyst displays a 100 % NO3 - -to-NH3 conversion at -0.55 to -0.6 V vs. RHE, and a record-high NH3 yield of 309 mmol h-1 gcat -1 , which is more than 25 times compared with the pristine Cu nanoparticles (12 mmol h-1 gcat -1 ). This research provides an effective method for conversion of dilute NO3 - to NH3 , which has certain guiding significance for the efficient and green conversion of wastewater in the future.

Grounding deep neural network predictions of human categorization behavior in understandable functional features: The case of face identity.

Deep neural networks (DNNs) can resolve real-world categorization tasks with apparent human-level performance. However, true equivalence of behavioral performance between humans and their DNN models requires that their internal mechanisms process equivalent features of the stimulus. To develop such feature equivalence, our methodology leveraged an interpretable and experimentally controlled generative model of the stimuli (realistic three-dimensional textured faces). Humans rated the similarity of randomly generated faces to four familiar identities. We predicted these similarity ratings from the activations of five DNNs trained with different optimization objectives. Using information theoretic redundancy, reverse correlation, and the testing of generalization gradients, we show that DNN predictions of human behavior improve because their shape and texture features overlap with those that subsume human behavior. Thus, we must equate the functional features that subsume the behavioral performances of the brain and its models before comparing where, when, and how these features are processed.

Control over Electrochemical CO2 Reduction Selectivity by Coordination Engineering of Tin Single-Atom Catalysts.

Carbon-based single-atom catalysts (SACs) with well-defined and homogeneously dispersed metal-N4 moieties provide a great opportunity for CO2 reduction. However, controlling the binding strength of various reactive intermediates on catalyst surface is necessary to enhance the selectivity to a desired product, and it is still a challenge. In this work, the authors prepared Sn SACs consisting of atomically dispersed SnN3 O1 active sites supported on N-rich carbon matrix (Sn-NOC) for efficient electrochemical CO2 reduction. Contrary to the classic Sn-N4 configuration which gives HCOOH and H2 as the predominant products, Sn-NOC with asymmetric atomic interface of SnN3 O1 gives CO as the exclusive product. Experimental results and density functional theory calculations show that the atomic arrangement of SnN3 O1 reduces the activation energy for *COO and *COOH formation, while increasing energy barrier for HCOO* formation significantly, thereby facilitating CO2 -to-CO conversion and suppressing HCOOH production. This work provides a new way for enhancing the selectivity to a specific product by controlling individually the binding strength of each reactive intermediate on catalyst surface.

A Tandem Strategy for Enhancing Electrochemical CO2 Reduction Activity of Single-Atom Cu-S1 N3 Catalysts via Integration with Cu Nanoclusters.

We developed a tandem electrocatalyst for CO2 -to-CO conversion comprising the single Cu site co-coordinated with N and S anchored carbon matrix (Cu-S1 N3 ) and atomically dispersed Cu clusters (Cux ), denoted as Cu-S1 N3 /Cux . The as-prepared Cu-S1 N3 /Cux composite presents a 100 % Faradaic efficiency towards CO generation (FECO ) at -0.65 V vs. RHE and high FECO over 90 % from -0.55 to -0.75 V, outperforming the analogues with Cu-N4 (FECO only 54 % at -0.7 V) and Cu-S1 N3 (FECO 70 % at -0.7 V) configurations. The unsymmetrical Cu-S1 N3 atomic interface in the carbon basal plane possesses an optimized binding energy for the key intermediate *COOH compared with Cu-N4 site. At the same time, the adjacent Cux effectively promotes the protonation of *CO2 - by accelerating water dissociation and offering *H to the Cu-S1 N3 active sites. This work provides a tandem strategy for facilitating proton-coupled electron transfer over the atomic-level catalytic sites.

Modeling individual preferences reveals that face beauty is not universally perceived across cultures.

Facial attractiveness confers considerable advantages in social interactions,1,2 with preferences likely reflecting psychobiological mechanisms shaped by natural selection. Theories of universal beauty propose that attractive faces comprise features that are closer to the population average3 while optimizing sexual dimorphism.4 However, emerging evidence questions this model as an accurate representation of facial attractiveness,5-7 including representing the diversity of beauty preferences within and across cultures.8-12 Here, we demonstrate that Western Europeans (WEs) and East Asians (EAs) evaluate facial beauty using culture-specific features, contradicting theories of universality. With a data-driven method, we modeled, at both the individual and group levels, the attractive face features of young females (25 years old) in two matched groups each of 40 young male WE and EA participants. Specifically, we generated a broad range of same- and other-ethnicity female faces with naturally varying shapes and complexions. Participants rated each on attractiveness. We then reverse correlated the face features that drive perception of attractiveness in each participant. From these individual face models, we reconstructed a facial attractiveness representation space that explains preference variations. We show that facial attractiveness is distinct both from averageness and from sexual dimorphism in both cultures. Finally, we disentangled attractive face features into those shared across cultures, culture specific, and specific to individual participants, thereby revealing their diversity. Our results have direct theoretical and methodological impact for representing diversity in social perception and for the design of culturally and ethnically sensitive socially interactive digital agents.

Revealing the information contents of memory within the stimulus information representation framework.

The information contents of memory are the cornerstone of the most influential models in cognition. To illustrate, consider that in predictive coding, a prediction implies that specific information is propagated down from memory through the visual hierarchy. Likewise, recognizing the input implies that sequentially accrued sensory evidence is successfully matched with memorized information (categorical knowledge). Although the existing models of prediction, memory, sensory representation and categorical decision are all implicitly cast within an information processing framework, it remains a challenge to precisely specify what this information is, and therefore where, when and how the architecture of the brain dynamically processes it to produce behaviour. Here, we review a framework that addresses these challenges for the studies of perception and categorization-stimulus information representation (SIR). We illustrate how SIR can reverse engineer the information contents of memory from behavioural and brain measures in the context of specific cognitive tasks that involve memory. We discuss two specific lessons from this approach that generally apply to memory studies: the importance of task, to constrain what the brain does, and of stimulus variations, to identify the specific information contents that are memorized, predicted, recalled and replayed. This article is part of the Theo Murphy meeting issue 'Memory reactivation: replaying events past, present and future'.

Modelling face memory reveals task-generalizable representations.

Current cognitive theories are cast in terms of information-processing mechanisms that use mental representations1-4. For example, people use their mental representations to identify familiar faces under various conditions of pose, illumination and ageing, or to draw resemblance between family members. Yet, the actual information contents of these representations are rarely characterized, which hinders knowledge of the mechanisms that use them. Here, we modelled the three-dimensional representational contents of 4 faces that were familiar to 14 participants as work colleagues. The representational contents were created by reverse-correlating identity information generated on each trial with judgements of the face's similarity to the individual participant's memory of this face. In a second study, testing new participants, we demonstrated the validity of the modelled contents using everyday face tasks that generalize identity judgements to new viewpoints, age and sex. Our work highlights that such models of mental representations are critical to understanding generalization behaviour and its underlying information-processing mechanisms.

Dynamic Construction of Reduced Representations in the Brain for Perceptual Decision Behavior.

Over the past decade, extensive studies of the brain regions that support face, object, and scene recognition suggest that these regions have a hierarchically organized architecture that spans the occipital and temporal lobes [1-14], where visual categorizations unfold over the first 250 ms of processing [15-19]. This same architecture is flexibly involved in multiple tasks that require task-specific representations-e.g. categorizing the same object as "a car" or "a Porsche." While we partly understand where and when these categorizations happen in the occipito-ventral pathway, the next challenge is to unravel how these categorizations happen. That is, how does high-dimensional input collapse in the occipito-ventral pathway to become low dimensional representations that guide behavior? To address this, we investigated what information the brain processes in a visual perception task and visualized the dynamic representation of this information in brain activity. To do so, we developed stimulus information representation (SIR), an information theoretic framework, to tease apart stimulus information that supports behavior from that which does not. We then tracked the dynamic representations of both in magneto-encephalographic (MEG) activity. Using SIR, we demonstrate that a rapid (∼170 ms) reduction of behaviorally irrelevant information occurs in the occipital cortex and that representations of the information that supports distinct behaviors are constructed in the right fusiform gyrus (rFG). Our results thus highlight how SIR can be used to investigate the component processes of the brain by considering interactions between three variables (stimulus information, brain activity, behavior), rather than just two, as is the current norm.