Photonic crystals built by time in ancient Roman glass.

Ancient glass objects typically show distinctive effects of deterioration as a result of environmentally induced physicochemical transformations of their surface over time. Iridescence is one of the distinctive signatures of aging that is most commonly found on excavated glass. In this work, we present an ancient glass fragment that exhibits structural color through surface weathering resulting in iridescent patinas caused by silica reprecipitation in nanoscale lamellae. This archaeological artifact reveals an unusual hierarchically assembled photonic crystal with extremely ordered nanoscale domains, high spectral selectivity, and reflectivity (~90%), that collectively behaves like a gold mirror. Optical characterization paired with nanoscale elemental analysis further underscores the high quality of this structure providing a window into this sophisticated natural photonic crystal assembled by time.

Spreading processes with mutations over multilayer networks.

A key scientific challenge during the outbreak of novel infectious diseases is to predict how the course of the epidemic changes under countermeasures that limit interaction in the population. Most epidemiological models do not consider the role of mutations and heterogeneity in the type of contact events. However, pathogens have the capacity to mutate in response to changing environments, especially caused by the increase in population immunity to existing strains, and the emergence of new pathogen strains poses a continued threat to public health. Further, in the light of differing transmission risks in different congregate settings (e.g., schools and offices), different mitigation strategies may need to be adopted to control the spread of infection. We analyze a multilayer multistrain model by simultaneously accounting for i) pathways for mutations in the pathogen leading to the emergence of new pathogen strains, and ii) differing transmission risks in different settings, modeled as network layers. Assuming complete cross-immunity among strains, namely, recovery from any infection prevents infection with any other (an assumption that will need to be relaxed to deal with COVID-19 or influenza), we derive the key epidemiological parameters for the multilayer multistrain framework. We demonstrate that reductions to existing models that discount heterogeneity in either the strain or the network layers may lead to incorrect predictions. Our results highlight that the impact of imposing/lifting mitigation measures concerning different contact network layers (e.g., school closures or work-from-home policies) should be evaluated in connection with their effect on the likelihood of the emergence of new strains.

Predicting metabolite-disease associations based on auto-encoder and non-negative matrix factorization.

Metabolism refers to a series of orderly chemical reactions used to maintain life activities in organisms. In healthy individuals, metabolism remains within a normal range. However, specific diseases can lead to abnormalities in the levels of certain metabolites, causing them to either increase or decrease. Detecting these deviations in metabolite levels can aid in diagnosing a disease. Traditional biological experiments often rely on a lot of manpower to do repeated experiments, which is time consuming and labor intensive. To address this issue, we develop a deep learning model based on the auto-encoder and non-negative matrix factorization named as MDA-AENMF to predict the potential associations between metabolites and diseases. We integrate a variety of similarity networks and then acquire the characteristics of both metabolites and diseases through three specific modules. First, we get the disease characteristics from the five-layer auto-encoder module. Later, in the non-negative matrix factorization module, we extract both the metabolite and disease characteristics. Furthermore, the graph attention auto-encoder module helps us obtain metabolite characteristics. After obtaining the features from three modules, these characteristics are merged into a single, comprehensive feature vector for each metabolite-disease pair. Finally, we send the corresponding feature vector and label to the multi-layer perceptron for training. The experiment demonstrates our area under the receiver operating characteristic curve of 0.975 and area under the precision-recall curve of 0.973 in 5-fold cross-validation, which are superior to those of existing state-of-the-art predictive methods. Through case studies, most of the new associations obtained by MDA-AENMF have been verified, further highlighting the reliability of MDA-AENMF in predicting the potential relationships between metabolites and diseases.

Advancing single-cell RNA-seq data analysis through the fusion of multi-layer perceptron and graph neural network.

The advancement of single-cell sequencing technology has smoothed the ability to do biological studies at the cellular level. Nevertheless, single-cell RNA sequencing (scRNA-seq) data presents several obstacles due to the considerable heterogeneity, sparsity and complexity. Although many machine-learning models have been devised to tackle these difficulties, there is still a need to enhance their efficiency and accuracy. Current deep learning methods often fail to fully exploit the intrinsic interconnections within cells, resulting in unsatisfactory results. Given these obstacles, we propose a unique approach for analyzing scRNA-seq data called scMPN. This methodology integrates multi-layer perceptron and graph neural network, including attention network, to execute gene imputation and cell clustering tasks. In order to evaluate the gene imputation performance of scMPN, several metrics like cosine similarity, median L1 distance and root mean square error are used. These metrics are utilized to compare the efficacy of scMPN with other existing approaches. This research utilizes criteria such as adjusted mutual information, normalized mutual information and integrity score to assess the efficacy of cell clustering across different approaches. The superiority of scMPN over current single-cell data processing techniques in cell clustering and gene imputation investigations is shown by the experimental findings obtained from four datasets with gold-standard cell labels. This observation demonstrates the efficacy of our suggested methodology in using deep learning methodologies to enhance the interpretation of scRNA-seq data.

Multilayer analysis of dynamic network reconfiguration in pediatric posttraumatic stress disorder.

Neuroimage studies have reported functional connectome abnormalities in posttraumatic stress disorder (PTSD), especially in adults. However, these studies often treated the brain as a static network, and time-variance of connectome topology in pediatric posttraumatic stress disorder remain unclear. To explore case-control differences in dynamic connectome topology, resting-state functional magnetic resonance imaging data were acquired from 24 treatment-naïve non-comorbid pediatric posttraumatic stress disorder patients and 24 demographically matched trauma-exposed non-posttraumatic stress disorder controls. A graph-theoretic analysis was applied to construct time-varying modular structure of whole-brain networks by maximizing the multilayer modularity. Network switching rate at the global, subnetwork, and nodal levels were calculated and compared between posttraumatic stress disorder and trauma-exposed non-posttraumatic stress disorder groups, and their associations with posttraumatic stress disorder symptom severity and sex interactions were explored. At the global level, individuals with posttraumatic stress disorder exhibited significantly lower network switching rates compared to trauma-exposed non-posttraumatic stress disorder controls. This difference was mainly involved in default-mode and dorsal attention subnetworks, as well as in inferior temporal and parietal brain nodes. Posttraumatic stress disorder symptom severity was negatively correlated with switching rate in the global network and default mode network. No significant differences were observed in the interaction between diagnosis and sex/age. Pediatric posttraumatic stress disorder is associated with dynamic reconfiguration of brain networks, which may provide insights into the biological basis of this disorder.

Understanding the coevolution of mask wearing and epidemics: A network perspective.

Nonpharmaceutical interventions (NPIs) such as mask wearing can be effective in mitigating the spread of infectious diseases. Therefore, understanding the behavioral dynamics of NPIs is critical for characterizing the dynamics of disease spread. Nevertheless, standard infection models tend to focus only on disease states, overlooking the dynamics of "beneficial contagions," e.g., compliance with NPIs. In this work, we investigate the concurrent spread of disease and mask-wearing behavior over multiplex networks. Our proposed framework captures both the competing and complementary relationships between the dueling contagion processes. Further, the model accounts for various behavioral mechanisms that influence mask wearing, such as peer pressure and fear of infection. Our results reveal that under the coupled disease-behavior dynamics, the attack rate of a disease-as a function of transition probability-exhibits a critical transition. Specifically, as the transmission probability exceeds a critical threshold, the attack rate decreases abruptly due to sustained mask-wearing responses. We empirically explore the causes of the critical transition and demonstrate the robustness of the observed phenomena. Our results highlight that without proper enforcement of NPIs, reductions in the disease transmission probability via other interventions may not be sufficient to reduce the final epidemic size.

Sex-specific Prediction of Cardiogenic Shock After Acute Coronary Syndromes: The SEX-SHOCK Score.

BACKGROUND: and aims: Cardiogenic shock (CS) remains the primary cause of in-hospital death after acute coronary syndromes (ACS), with its plateauing mortality rates approaching 50%. To test novel interventions, personalized risk prediction is essential. The ORBI (Observatoire Régional Breton sur l'Infarctus) score represents the first-of-its-kind risk score to predict in-hospital CS in ACS patients undergoing percutaneous coronary intervention (PCI). However, its sex-specific performance remains unknown, and refined risk prediction strategies are warranted. METHODS: This multinational study included a total of 53 537 ACS patients without CS on admission undergoing PCI. Following sex-specific evaluation of ORBI, regression and machine-learning models were used for variable selection and risk prediction. By combining best-performing models with highest-ranked predictors, SEX-SHOCK was developed, and internally and externally validated. RESULTS: The ORBI score showed lower discriminative performance for the prediction of CS in females than males in Swiss (AUC [95% CI]: 0.78 [0.76-0.81] vs. 0.81 [0.79-0.83]; p=0.048) and French ACS patients (0.77 [0.74-0.81] vs. 0.84 [0.81-0.86]; p=0.002). The newly developed SEX-SHOCK score, now incorporating ST-segment elevation, creatinine, C-reactive protein, and left ventricular ejection fraction, outperformed ORBI in both sexes (females: 0.81 [0.78-0.83]; males: 0.83 [0.82-0.85]; p<0.001), which prevailed following internal and external validation in RICO (females: 0.82 [0.79-0.85]; males: 0.88 [0.86-0.89]; p<0.001) and SPUM-ACS (females: 0.83 [0.77-0.90], p=0.004; males: 0.83 [0.80-0.87], p=0.001). CONCLUSIONS: The ORBI score showed modest sex-specific performance. The novel SEX-SHOCK score provides superior performance in females and males across the entire spectrum of ACS, thus providing a basis for future interventional trials and contemporary ACS management.

Titanium Self-Intercalation Induced Formation of Orthogonal (1 × 1) Edge/Surface Reconstruction in 1T-TiSe2: Atomic Scale Dynamics and Mechanistic Study.

Edges and surfaces play indispensable roles in affecting the chemical-physical properties of materials, particularly in two-dimensional transition metal dichalcogenides (TMDCs) with reduced dimensionality. Herein, we report a novel edge/surface structure in multilayer 1T-TiSe2, i.e., the orthogonal (1 × 1) reconstruction, induced by the self-intercalation of Ti atoms into interlayer octahedral sites of the host TiSe2 at elevated temperature. Formation dynamics of the reconstructed edge/surface are captured at the atomic level by in situ scanning transmission electron microscopy (STEM) and further validated by density functional theory (DFT), which enables the proposal of the nucleation mechanism and two growth routes (zigzag and armchair). Via STEM-electron energy loss spectroscopy (STEM-EELS), a chemical shift of 0.6 eV in Ti L3,2 is observed in the reconstructed edge/surface, which is attributed to the change of the coordination number and lattice distortion. The present work provides insights to tailor the atomic/electronic structures and properties of 2D TMDC materials.

In Situ Monitoring and Tuning Multilayer Stacking of Polymer Lamellar Crystals in Solution with Aggregation-Induced Emission.

Many types of self-assembled 2D materials with fascinating morphologies and novel properties have been prepared and used in solution. However, it is still a challenge to monitor their in situ growth in solution and to control the number of layers in these materials. Here, we demonstrate that the aggregation-induced emission (AIE) effect can be applied for the in situ decoupled tracing of the lateral growth and multilayer stacking of polymer lamellar crystals in solution. Multilayer stacking considerably enhances the photoluminescence intensity of the AIE molecules sandwiched between two layers of lamellar crystals, which is 2.4 times that on the surface of monolayer crystals. Both variation of the self-seeding temperature of crystal seeds and addition of a trace amount of long polymer chains during growth can control multilayer lamellar stacking, which are applied to produce tunable fluorescent patterns for functional applications.

Monolayer-like Exciton Recombination Dynamics of Multilayer MoSe2 Observed by Pump-Probe Microscopy.

Transition metal dichalcogenides (TMDCs) have garnered considerable interest over the past decade as a class of semiconducting layered materials. Most studies on the carrier dynamics in these materials have focused on the monolayer due to its direct bandgap, strong photoluminescence, and strongly bound excitons. However, a comparative understanding of the carrier dynamics in multilayer (e.g., >10 layers) flakes is still absent. Recent computational studies have suggested that excitons in bulk TMDCs are confined to individual layers, leading to room-temperature stable exciton populations. Using this new context, we explore the carrier dynamics in MoSe2 flakes that are between ∼16 and ∼125 layers thick. We assign the kinetics to exciton-exciton annihilation (EEA) and Shockley-Read-Hall recombination of free carriers. Interestingly, the average observed EEA rate constant (0.003 cm2/s) is nearly independent of flake thickness and 2 orders of magnitude smaller than that of an unencapsulated monolayer (0.33 cm2/s) but very similar to values observed in encapsulated monolayers. Thus, we posit that strong intralayer interactions minimize the effect of layer thickness on recombination dynamics, causing the multilayer to behave like the monolayer and exhibit an apparent EEA rate intrinsic to MoSe2.

Strong Sliding Ferroelectricity and Interlayer Sliding Controllable Spintronic Effect in Two-Dimensional HgI2 Layers.

Exploration of two-dimensional (2D) sliding ferroelectric (FE) materials with experimentally detectable ferroelectricity and value-added novel functionalities is highly sought for the development of 2D "slidetronics". Herein, based on first-principles calculations, we identify the synthesizable van der Waals (vdW) layered crystals HgX2 (X = Br and I) as a new class of 2D sliding ferroelectrics. Both HgBr2 and HgI2 in 2D multilayered forms adopt the preferential stacking sequence, leading to room temperature stable out-of-plane (vertical) ferroelectricity that can be reversed via the sliding of adjacent monolayers. Owing to strong interlayer coupling and interfacial charge rearrangement, 2D HgI2 layers possess strong sliding ferroelectricity up to 0.16 µC/cm2, readily detectable in experiment. Moreover, robust sliding ferroelectricity and interlayer sliding controllable Rashba spin texture of FE-HgI2 layers enable potential applications as 2D spintronic devices such that the electric control of electron spin detection can be realized at the 2D regime.

PyMulSim: a method for computing node similarities between multilayer networks via graph isomorphism networks.

BACKGROUND: In bioinformatics, interactions are modelled as networks, based on graph models. Generally, these support a single-layer structure which incorporates a specific entity (i.e., node) and only one type of link (i.e., edge). However, real-world biological systems consisting of biological objects belonging to heterogeneous entities, and these operate and influence each other in multiple contexts, simultaneously. Usually, node similarities are investigated to assess the relatedness between biological objects in a network of interest, and node embeddings are widely used for studying novel interaction from a topological point of view. About that, the state-of-the-art presents several methods for evaluating the node similarity inside a given network, but methodologies able to evaluate similarities between pairs of nodes belonging to different networks are missing. The latter are crucial for studies that relate different biological networks, e.g., for Network Alignment or to evaluate the possible evolution of the interactions of a little-known network on the basis of a well-known one. Existing methods are ineffective in evaluating nodes outside their structure, even more so in the context of multilayer networks, in which the topic still exploits approaches adapted from static networks. In this paper, we presented pyMulSim, a novel method for computing the pairwise similarities between nodes belonging to different multilayer networks. It uses a Graph Isomorphism Network (GIN) for the representative learning of node features, that uses for processing the embeddings and computing the similarities between the pairs of nodes of different multilayer networks. RESULTS: Our experimentation investigated the performance of our method. Results show that our method effectively evaluates the similarities between the biological objects of a source multilayer network to a target one, based on the analysis of the node embeddings. Results have been also assessed for different noise levels, also through statistical significance analyses properly performed for this purpose. CONCLUSIONS: PyMulSim is a novel method for computing the pairwise similarities between nodes belonging to different multilayer networks, by using a GIN for learning node embeddings. It has been evaluated both in terms of performance and validity, reporting a high degree of reliability.

Random walk with restart on multilayer networks: from node prioritisation to supervised link prediction and beyond.

BACKGROUND: Biological networks have proven invaluable ability for representing biological knowledge. Multilayer networks, which gather different types of nodes and edges in multiplex, heterogeneous and bipartite networks, provide a natural way to integrate diverse and multi-scale data sources into a common framework. Recently, we developed MultiXrank, a Random Walk with Restart algorithm able to explore such multilayer networks. MultiXrank outputs scores reflecting the proximity between an initial set of seed node(s) and all the other nodes in the multilayer network. We illustrate here the versatility of bioinformatics tasks that can be performed using MultiXrank. RESULTS: We first show that MultiXrank can be used to prioritise genes and drugs of interest by exploring multilayer networks containing interactions between genes, drugs, and diseases. In a second study, we illustrate how MultiXrank scores can also be used in a supervised strategy to train a binary classifier to predict gene-disease associations. The classifier performance are validated using outdated and novel gene-disease association for training and evaluation, respectively. Finally, we show that MultiXrank scores can be used to compute diffusion profiles and use them as disease signatures. We computed the diffusion profiles of more than 100 immune diseases using a multilayer network that includes cell-type specific genomic information. The clustering of the immune disease diffusion profiles reveals shared shared phenotypic characteristics. CONCLUSION: Overall, we illustrate here diverse applications of MultiXrank to showcase its versatility. We expect that this can lead to further and broader bioinformatics applications.

Unravelling the multisensory learning advantage: Different patterns of within and across frequency-specific interactions drive uni- and multisensory neuroplasticity.

In the field of learning theory and practice, the superior efficacy of multisensory learning over uni-sensory is well-accepted. However, the underlying neural mechanisms at the macro-level of the human brain remain largely unexplored. This study addresses this gap by providing novel empirical evidence and a theoretical framework for understanding the superiority of multisensory learning. Through a cognitive, behavioral, and electroencephalographic assessment of carefully controlled uni-sensory and multisensory training interventions, our study uncovers a fundamental distinction in their neuroplastic patterns. A multilayered network analysis of pre- and post- training EEG data allowed us to model connectivity within and across different frequency bands at the cortical level. Pre-training EEG analysis unveils a complex network of distributed sources communicating through cross-frequency coupling, while comparison of pre- and post-training EEG data demonstrates significant differences in the reorganizational patterns of uni-sensory and multisensory learning. Uni-sensory training primarily modifies cross-frequency coupling between lower and higher frequencies, whereas multisensory training induces changes within the beta band in a more focused network, implying the development of a unified representation of audiovisual stimuli. In combination with behavioural and cognitive findings this suggests that, multisensory learning benefits from an automatic top-down transfer of training, while uni-sensory training relies mainly on limited bottom-up generalization. Our findings offer a compelling theoretical framework for understanding the advantage of multisensory learning.

PLM-T3SE: Accurate Prediction of Type III Secretion Effectors Using Protein Language Model Embeddings.

The Type III secretion effectors (T3SEs) are bacterial proteins synthesized by Gram-negative pathogens and delivered into host cells via the Type III secretion system (T3SS). These effectors usually play a pivotal role in the interactions between bacteria and hosts. Hence, the precise identification of T3SEs aids researchers in exploring the pathogenic mechanisms of bacterial infections. Since the diversity and complexity of T3SE sequences often make traditional experimental methods time-consuming, it is imperative to explore more efficient and convenient computational approaches for T3SE prediction. Inspired by the promising potential exhibited by pre-trained language models in protein recognition tasks, we proposed a method called PLM-T3SE that utilizes protein language models (PLMs) for effective recognition of T3SEs. First, we utilized PLM embeddings and evolutionary features from the position-specific scoring matrix (PSSM) profiles to transform protein sequences into fixed-length vectors for model training. Second, we employed the extreme gradient boosting (XGBoost) algorithm to rank these features based on their importance. Finally, a MLP neural network model was used to predict T3SEs based on the selected optimal feature set. Experimental results from the cross-validation and independent test demonstrated that our model exhibited superior performance compared to the existing models. Specifically, our model achieved an accuracy of 98.1%, which is 1.8%-42.4% higher than the state-of-the-art predictors based on the same independent data set test. These findings highlight the superiority of the PLM-T3SE and the remarkable characterization ability of PLM embeddings for T3SE prediction.

Creation of Grooved Tissue Engineering Scaffolds from Architectured Multilayer Polymer Composites by a Tuneable One-Step Degradation Process.

The surface properties of biomaterials interact directly with biological systems, influencing cellular responses, tissue integration, and biocompatibility. Surface topography plays a critical role in cardiac tissue engineering by affecting electrical conductivity, cardiomyocyte alignment, and contractile function. Current methods for controlling surface properties and topography in cardiac tissue engineering scaffolds are limited, expensive, and lack precision. This study introduces a low-cost, one-step degradation process to create scaffolds with well-defined micro-grooves from multilayered 3D printed poly(lactic acid)/thermoplastic polyurethane composites. The approach provides control over erosion rate and surface morphology, allowing easy tuning of scaffold topographical cues for tissue engineering applications. The findings reported in this study provide a library of easily tuneable scaffold topographical cues. A strong dependence of neonatal rat cardiomyocyte (NRCM) contact guidance with the multilayers' dimension and shape in partially degraded polylactic acid (PLA)/thermoplastic polyurethane (TPU) samples is observed. NRCMs cultured on samples with a layer thickness of 13 ± 2 µm and depth of 4.7 ± 0.2 µm demonstrate the most regular contractions. Hence, the proposed fabrication scheme can be used to produce a new generation of biomaterials with excellent controllability determined by multilayer thickness, printing parameters, and degradation treatment duration.

Enhanced Temperature Stability of Pyroelectric Sensing in Multilayer Potassium Sodium Niobate-Based Ceramics with Graded Polarization Rotation.

Pyroelectric effect which refers to electrical responses induced by time temperature-dependent fluctuations has received extensive attention, showing promising application prospects for infrared (IR) technology. Although enhanced pyroelectric performances are obtained in potassium sodium niobate-based ceramics at room temperature via multi-symmetries coexistence design, the poor pyroelectric temperature stability is still an urging desire that needs to be resolved. Herin, by constructing multilayer composite ceramics and adjusting the proportion of stacked layers, improved pyroelectric coefficient, and figures of merit (FOMs), as well as enhanced temperature stabilities can be achieved. With a remained high pyroelectric coefficient of 5.45 × 10-4 C m-2°C-1 at room temperature, the pyroelectric parameters almost keep unchanged in the temperature range of 30-100 °C, showing great properties advantages compared with previous reports. The excellent properties can be attributed to the graded polarization rotation states among each lamination induced by successive phase transitions. The novel strategy for achieving stable pyroelectric sensing can further promote the application in the IR sensors field.

Preload-Induced Switchable Adhesion.

Animals with robust attachment abilities commonly exhibit stable attachment and convenient detachment. However, achieving an efficient attachment-detachment function in bioinspired adhesives is challenging owing to the complexity and delay of actuators. In this study, a class of multilayer adhesives (MAs) comprising backing, middle, and bottom layers is proposed to realize rapid switching by only adjusting the preload. At low preload, the MAs maintain intimate contact with the substrate. By contrast, a sufficiently large preload results in significant deformation of the middle layer, causing underside buckling and reducing adhesion. By optimizing the structural parameters of the MAs, a high switching ratio (up to 136×) can be achieved under different preloads. Furthermore, the design of the MAs incorporates a film-terminated structure, which prevents the embedding of dirt particles, simplifies cleaning, and maintains the separation and uprightness of the microstructures. Consequently, the MAs demonstrate practical potential for simple and efficient transportation applications, as they achieve switchable adhesion through their structure, exhibiting a high switching ratio and fast switching.

An Asymmetric Layer Structure Enables Robust Multifunctional Wearable Bacterial Cellulose Composite Film with Excellent Electrothermal/Photothermal and EMI Shielding Performance.

Highly robust flexible multifunctional film with excellent electromagnetic interference shielding and electrothermal/photothermal characteristics are highly desirable for aerospace, military, and wearable devices. Herein, an asymmetric gradient multilayer structured bacterial cellulose@Fe3O4/carbon nanotube/Ti3C2Tx (BC@Fe3O4/CNT/Ti3C2Tx) multifunctional composite film is fabricated with simultaneously demonstrating fast Joule response, excellent EMI shielding effectiveness (EMI SE) and photothermal conversion properties. The asymmetric gradient 6-layer composite film with 40% of Ti3C2Tx possesses excellent mechanical performance with exceptional tensile strength (76.1 MPa), large strain (14.7%), and good flexibility. This is attributed to the asymmetric gradient multilayer structure designed based on the hydrogen bonding self-assembly strategy between Ti3C2Tx and BC. It achieved an EMI SE of up to 71.3 dB, which is attributed to the gradient "absorption-reflection-reabsorption" mechanism. Furthermore, this composite film also exhibits excellent low-voltage-driven Joule heating (up to 80.3 °C at 2.5 V within 15 s) and fast-response photothermal performance (up to 101.5 °C at 1.0 W cm-2 within 10 s), which is attributed to the synergistic effect of heterostructure. This work demonstrates the fabrication of multifunctional bacterial cellulose@Fe3O4/carbon nanotube/Ti3C2Tx composite film has promising potentials for next-generation wearable electronic devices in energy conversion, aerospace, and artificial intelligence.

Dual-Interface Solar Evaporator with Highly-Efficient Thermal Regulation via Suspended Multilayer Design.

Facing the increasing global shortage of freshwater resources, this study presents a suspended multilayer evaporator (SMLE), designed to tackle the principal issues plaguing current solar-driven interfacial evaporation technologies, specifically, substantial thermal losses and limited water production. This approach, through the implementation of a multilayer structural design, enables superior thermal regulation throughout the evaporation process. This evaporator consists of a radiation damping layer, a photothermal conversion layer, and a bottom layer that leverages radiation, wherein the bottom layer exhibits a notable infrared emissivity. The distinctive feature of the design effectively reduces radiative heat loss and facilitates dual-interface evaporation by heating the water surface through mid-infrared radiation. The refined design leads to a notable evaporation rate of 2.83 kg m-2 h-1. Numerical simulations and practical performance evaluations validate the effectiveness of the multilayer evaporator in actual use scenarios. This energy-recycling and dual-interface evaporation multilayered approach propels the design of high-efficiency solar-driven interfacial evaporators forward, presenting new insights into developing effective water-energy transformation systems.