High-entropy polymer produces a giant electrocaloric effect at low fields.

More than a decade of research on the electrocaloric (EC) effect has resulted in EC materials and EC multilayer chips that satisfy a minimum EC temperature change of 5 K required for caloric heat pumps1-3. However, these EC temperature changes are generated through the application of high electric fields4-8 (close to their dielectric breakdown strengths), which result in rapid degradation and fatigue of EC performance. Here we report a class of EC polymer that exhibits an EC entropy change of 37.5 J kg-1 K-1 and a temperature change of 7.5 K under 50 MV m-1, a 275% enhancement over the state-of-the-art EC polymers under the same field strength. We show that converting a small number of the chlorofluoroethylene groups in poly(vinylidene fluoride-trifluoroethylene-chlorofluoroethylene) terpolymer into covalent double bonds markedly increases the number of the polar entities and enhances the polar-nonpolar interfacial areas of the polymer. The polar phases in the polymer adopt a loosely correlated, high-entropy state with a low energy barrier for electric-field-induced switching. The polymer maintains performance for more than one million cycles at the low fields necessary for practical EC cooling applications, suggesting that this strategy may yield materials suitable for use in caloric heat pumps.

Subterahertz collective dynamics of polar vortices.

The collective dynamics of topological structures1-6 are of interest from both fundamental and applied perspectives. For example, studies of dynamical properties of magnetic vortices and skyrmions3,4 have not only deepened our understanding of many-body physics but also offered potential applications in data processing and storage7. Topological structures constructed from electrical polarization, rather than electron spin, have recently been realized in ferroelectric superlattices5,6, and these are promising for ultrafast electric-field control of topological orders. However, little is known about the dynamics underlying the functionality of such complex extended nanostructures. Here, using terahertz-field excitation and femtosecond X-ray diffraction measurements, we observe ultrafast collective polarization dynamics that are unique to polar vortices, with orders-of-magnitude higher frequencies and smaller lateral size than those of experimentally realized magnetic vortices3. A previously unseen tunable mode, hereafter referred to as a vortexon, emerges in the form of transient arrays of nanoscale circular patterns of atomic displacements, which reverse their vorticity on picosecond timescales. Its frequency is considerably reduced (softened) at a critical strain, indicating a condensation (freezing) of structural dynamics. We use first-principles-based atomistic calculations and phase-field modelling to reveal the microscopic atomic arrangements and corroborate the frequencies of the vortex modes. The discovery of subterahertz collective dynamics in polar vortices opens opportunities for electric-field-driven data processing in topological structures with ultrahigh speed and density.

Ultrahigh-energy photons up to 1.4 petaelectronvolts from 12 γ-ray Galactic sources.

The extension of the cosmic-ray spectrum beyond 1 petaelectronvolt (PeV; 1015 electronvolts) indicates the existence of the so-called PeVatrons-cosmic-ray factories that accelerate particles to PeV energies. We need to locate and identify such objects to find the origin of Galactic cosmic rays1. The principal signature of both electron and proton PeVatrons is ultrahigh-energy (exceeding 100 TeV) γ radiation. Evidence of the presence of a proton PeVatron has been found in the Galactic Centre, according to the detection of a hard-spectrum radiation extending to 0.04 PeV (ref. 2). Although γ-rays with energies slightly higher than 0.1 PeV have been reported from a few objects in the Galactic plane3-6, unbiased identification and in-depth exploration of PeVatrons requires detection of γ-rays with energies well above 0.1 PeV. Here we report the detection of more than 530 photons at energies above 100 teraelectronvolts and up to 1.4 PeV from 12 ultrahigh-energy γ-ray sources with a statistical significance greater than seven standard deviations. Despite having several potential counterparts in their proximity, including pulsar wind nebulae, supernova remnants and star-forming regions, the PeVatrons responsible for the ultrahigh-energy γ-rays have not yet been firmly localized and identified (except for the Crab Nebula), leaving open the origin of these extreme accelerators.

Utilizing the oxygen-atom trapping effect of Co3O4 with oxygen vacancies to promote chlorite activation for water decontamination.

Heterogeneous high-valent cobalt-oxo [≡Co(IV)=O] is a widely focused reactive species in oxidant activation; however, the relationship between the catalyst interfacial defects and ≡Co(IV)=O formation remains poorly understood. Herein, photoexcited oxygen vacancies (OVs) were introduced into Co3O4 (OV-Co3O4) by a UV-induced modification method to facilitate chlorite (ClO2-) activation. Density functional theory calculations indicate that OVs result in low-coordinated Co atom, which can directionally anchor chlorite under the oxygen-atom trapping effect. Chlorite first undergoes homolytic O-Cl cleavage and transfers the dissociated O atom to the low-coordinated Co atom to form reactive ≡Co(IV)=O with a higher spin state. The reactive ≡Co(IV)=O rapidly extracts one electron from ClO2- to form chlorine dioxide (ClO2), accompanied by the Co atom returning a lower spin state. As a result of the oxygen-atom trapping effect, the OV-Co3O4/chlorite system achieved a 3.5 times higher efficiency of sulfamethoxazole degradation (~0.1331 min-1) than the pristine Co3O4/chlorite system. Besides, the refiled OVs can be easily restored by re-exposure to UV light, indicating the sustainability of the oxygen atom trap. The OV-Co3O4 was further fabricated on a polyacrylonitrile membrane for back-end water purification, achieving continuous flow degradation of pollutants with low cobalt leakage. This work presents an enhancement strategy for constructing OV as an oxygen-atom trapping site in heterogeneous advanced oxidation processes and provides insight into modulating the formation of ≡Co(IV)=O via defect engineering.

Bioinspired iontronic synapse fibers for ultralow-power multiplexing neuromorphic sensorimotor textiles.

Artificial neuromorphic devices can emulate dendric integration, axonal parallel transmission, along with superior energy efficiency in facilitating efficient information processing, offering enormous potential for wearable electronics. However, integrating such circuits into textiles to achieve biomimetic information perception, processing, and control motion feedback remains a formidable challenge. Here, we engineer a quasi-solid-state iontronic synapse fiber (ISF) comprising photoresponsive TiO2, ion storage Co-MoS2, and an ion transport layer. The resulting ISF achieves inherent short-term synaptic plasticity, femtojoule-range energy consumption, and the ability to transduce chemical/optical signals. Multiple ISFs are interwoven into a synthetic neural fabric, allowing the simultaneous propagation of distinct optical signals for transmitting parallel information. Importantly, IFSs with multiple input electrodes exhibit spatiotemporal information integration. As a proof of concept, a textile-based multiplexing neuromorphic sensorimotor system is constructed to connect synaptic fibers with artificial fiber muscles, enabling preneuronal sensing information integration, parallel transmission, and postneuronal information output to control the coordinated motor of fiber muscles. The proposed fiber system holds enormous promise in wearable electronics, soft robotics, and biomedical engineering.

AptaDB: a comprehensive database integrating aptamer-target interactions.

Aptamers have emerged as research hotspots of the next generation due to excellent performance benefits and application potentials in pharmacology, medicine, and analytical chemistry. Despite the numerous aptamer investigations, the lack of comprehensive data integration has hindered the development of computational methods for aptamers and the reuse of aptamers. A public access database named AptaDB, derived from experimentally validated data manually collected from the literature, was hence developed, integrating comprehensive aptamer-related data, which include six key components: (i) experimentally validated aptamer-target interaction information, (ii) aptamer property information, (iii) structure information of aptamer, (iv) target information, (v) experimental activity information, and (vi) algorithmically calculated similar aptamers. AptaDB currently contains 1350 experimentally validated aptamer-target interactions, 1230 binding affinity constants, 1293 aptamer sequences, and more. Compared to other aptamer databases, it contains twice the number of entries found in available databases. The collection and integration of the above information categories is unique among available aptamer databases and provides a user-friendly interface. AptaDB will also be continuously updated as aptamer research evolves. We expect that AptaDB will become a powerful source for aptamer rational design and a valuable tool for aptamer screening in the future. For access to AptaDB, please visit http://lmmd.ecust.edu.cn/aptadb/.

Transparent ferroelectric crystals with ultrahigh piezoelectricity.

Transparent piezoelectrics are highly desirable for numerous hybrid ultrasound-optical devices ranging from photoacoustic imaging transducers to transparent actuators for haptic applications1-7. However, it is challenging to achieve high piezoelectricity and perfect transparency simultaneously because most high-performance piezoelectrics are ferroelectrics that contain high-density light-scattering domain walls. Here, through a combination of phase-field simulations and experiments, we demonstrate a relatively simple method of using an alternating-current electric field to engineer the domain structures of originally opaque rhombohedral Pb(Mg1/3Nb2/3)O3-PbTiO3 (PMN-PT) crystals to simultaneously generate near-perfect transparency, an ultrahigh piezoelectric coefficient d33 (greater than 2,100 picocoulombs per newton), an excellent electromechanical coupling factor k33 (about 94 per cent) and a large electro-optical coefficient γ33 (approximately 220 picometres per volt), which is far beyond the performance of the commonly used transparent ferroelectric crystal LiNbO3. We find that increasing the domain size leads to a higher d33 value for the [001]-oriented rhombohedral PMN-PT crystals, challenging the conventional wisdom that decreasing the domain size always results in higher piezoelectricity8-10. This work presents a paradigm for achieving high transparency and piezoelectricity by ferroelectric domain engineering, and we expect the transparent ferroelectric crystals reported here to provide a route to a wide range of hybrid device applications, such as medical imaging, self-energy-harvesting touch screens and invisible robotic devices.

admetSAR3.0: a comprehensive platform for exploration, prediction and optimization of chemical ADMET properties.

Absorption, distribution, metabolism, excretion and toxicity (ADMET) properties play a crucial role in drug discovery and chemical safety assessment. Built on the achievements of admetSAR and its successor, admetSAR2.0, this paper introduced the new version of the series, admetSAR3.0, as a comprehensive platform for chemical ADMET assessment, including search, prediction and optimization modules. In the search module, admetSAR3.0 hosted over 370 000 high-quality experimental ADMET data for 104 652 unique compounds, and supplemented chemical structure similarity search function to facilitate read-across. In the prediction module, we introduced comprehensive ADMET endpoints and two new sections for environmental and cosmetic risk assessments, empowering admetSAR3.0 to provide prediction for 119 endpoints, more than double numbers compared to the previous version. Furthermore, the advanced multi-task graph neural network framework offered robust and reliable support for ADMET prediction. In particular, a module named ADMETopt was added to automatically optimize the ADMET properties of query molecules through transformation rules or scaffold hopping. Finally, admetSAR3.0 provides user-friendly interfaces for multiple types of input data, such as SMILES string, chemical structure and batch molecule file, and supports various output types, including digital, chart displays and file downloads. In summary, admetSAR3.0 is anticipated to be a valuable and powerful tool in drug discovery and chemical safety assessment at http://lmmd.ecust.edu.cn/admetsar3/.

HHCDB: a database of human heterochromatin regions.

Heterochromatin plays essential roles in eukaryotic genomes, such as regulating genes, maintaining genome integrity and silencing repetitive DNA elements. Identifying genome-wide heterochromatin regions is crucial for studying transcriptional regulation. We propose the Human Heterochromatin Chromatin Database (HHCDB) for archiving heterochromatin regions defined by specific or combined histone modifications (H3K27me3, H3K9me2, H3K9me3) according to a unified pipeline. 42 839 743 heterochromatin regions were identified from 578 samples derived from 241 cell-types/cell lines and 92 tissue types. Genomic information is provided in HHCDB, including chromatin location, gene structure, transcripts, distance from transcription start site, neighboring genes, CpG islands, transposable elements, 3D genomic structure and functional annotations. Furthermore, transcriptome data from 73 single cells were analyzed and integrated to explore cell type-specific heterochromatin-related genes. HHCDB affords rich visualization through the UCSC Genome Browser and our self-developed tools. We have also developed a specialized online analysis platform to mine differential heterochromatin regions in cancers. We performed several analyses to explore the function of cancer-specific heterochromatin-related genes, including clinical feature analysis, immune cell infiltration analysis and the construction of drug-target networks. HHCDB is a valuable resource for studying epigenetic regulation, 3D genomics and heterochromatin regulation in development and disease. HHCDB is freely accessible at http://hhcdb.edbc.org/.

Real-space imaging of periodic nanotextures in thin films via phasing of diffraction data.

New properties and exotic quantum phenomena can form due to periodic nanotextures, including Moire patterns, ferroic domains, and topologically protected magnetization and polarization textures. Despite the availability of powerful tools to characterize the atomic crystal structure, the visualization of nanoscale strain-modulated structural motifs remains challenging. Here, we develop nondestructive real-space imaging of periodic lattice distortions in thin epitaxial films and report an emergent periodic nanotexture in a Mott insulator. Specifically, we combine iterative phase retrieval with unsupervised machine learning to invert the diffuse scattering pattern from conventional X-ray reciprocal-space maps into real-space images of crystalline displacements. Our imaging in PbTiO3/SrTiO3 superlattices exhibiting checkerboard strain modulation substantiates published phase-field model calculations. Furthermore, the imaging of biaxially strained Mott insulator Ca2RuO4 reveals a strain-induced nanotexture comprised of nanometer-thin metallic-structure wires separated by nanometer-thin Mott-insulating-structure walls, as confirmed by cryogenic scanning transmission electron microscopy (cryo-STEM). The nanotexture in Ca2RuO4 film is induced by the metal-to-insulator transition and has not been reported in bulk crystals. We expect the phasing of diffuse X-ray scattering from thin crystalline films in combination with cryo-STEM to open a powerful avenue for discovering, visualizing, and quantifying the periodic strain-modulated structures in quantum materials.

ARL6IP1 mediates small-molecule-induced alleviation of Alzheimer pathology through FXR1-dependent BACE1 translation initiation.

Exploring the potential lead compounds for Alzheimer's disease (AD) remains one of the challenging tasks. Here, we report that the plant extract conophylline (CNP) impeded amyloidogenesis by preferentially inhibiting BACE1 translation via the 5' untranslated region (5'UTR) and rescued cognitive decline in an animal model of APP/PS1 mice. ADP-ribosylation factor-like protein 6-interacting protein 1 (ARL6IP1) was then found to mediate the effect of CNP on BACE1 translation, amyloidogenesis, glial activation, and cognitive function. Through analysis of the 5'UTR-targetd RNA-binding proteins by RNA pulldown combined with LC-MS/MS, we found that FMR1 autosomal homolog 1 (FXR1) interacted with ARL6IP1 and mediated CNP-induced reduction of BACE1 by regulating the 5'UTR activity. Without altering the protein levels of ARL6IP1 and FXR1, CNP treatment promoted ARL6IP1 interaction with FXR1 and inhibited FXR1 binding to the 5'UTR both in vitro and in vivo. Collectively, CNP exhibited a therapeutic potential for AD via ARL6IP1. Through pharmacological manipulation, we uncovered a dynamic interaction between FXR1 and the 5'UTR in translational control of BACE1, adding to the understanding of the pathophysiology of AD.

AP2S1 regulates APP degradation through late endosome-lysosome fusion in cells and APP/PS1 mice.

AP2S1 is the sigma 2 subunit of adaptor protein 2 (AP2) that is essential for endocytosis. In this study, we investigated the potential role of AP2S1 in intracellular processing of amyloid precursor protein (APP), which contributes to the pathogenesis of Alzheimer disease (AD) by generating the toxic ß-amyloid peptide (Aß). We found that knockdown or overexpression of AP2S1 decreased or increased the protein levels of APP and Aß in cells stably expressing human full-length APP695, respectively. This effect was unrelated to endocytosis but involved lysosomal degradation. Morphological studies revealed that silencing of AP2S1 promoted the translocalization of APP from RAB9-positive late endosomes (LE) to LAMP1-positive lysosomes, which was paralleled by the enhanced LE-lysosome fusion. In support, silencing of vacuolar protein sorting-associated protein 41 (VPS41) that is implicated in LE-lyso fusion prevented AP2S1-mediated regulation of APP degradation and translocalization. In APP/PS1 mice, an animal model of AD, AAV-mediated delivery of AP2S1 shRNA in the hippocampus significantly reduced the protein levels of APP and Aß, with the concomitant APP translocalization, LE-lyso fusion and the improved cognitive functions. Taken together, these data uncover a LE-lyso fusion mechanism in APP degradation and suggest a novel role for AP2S1 in the pathophysiology of AD.

The disordered protein SERF promotes α-Synuclein aggregation through liquid-liquid phase separation.

The aggregation of α-Synuclein (α-Syn) into amyloid fibrils is the hallmark of Parkinson's disease. Under stress or other pathological conditions, the accumulation of α-Syn oligomers is the main contributor to the cytotoxicity. A potential approach for treating Parkinson's disease involves preventing the accumulation of these α-Syn oligomers. In this study, we present a novel mechanism involving a conserved group of disorderly proteins known as small EDRK-rich factor (SERF), which promotes the aggregation of α-Syn through a cophase separation process. Using diverse methods like confocal microscopy, fluorescence recovery after photobleaching assays, solution-state NMR spectroscopy, and Western blot, we determined that the N-terminal domain of SERF1a plays a role in the interactions that occur during cophase separation. Within these droplets, α-Syn undergoes a gradual transformation from solid condensates to amyloid fibrils, while SERF1a is excluded from the condensates and dissolves into the solution. Notably, in vivo experiments show that SERF1a cophase separation with α-Syn significantly reduces the deposition of α-Syn oligomers and decreases its cellular toxicity under stress. These findings suggest that SERF1a accelerates the conversion of α-Syn from highly toxic oligomers to less toxic fibrils through cophase separation, thereby mitigating the biological damage of α-Syn aggregation.

Non-equilibrium pathways to emergent polar supertextures.

Ultrafast stimuli can stabilize metastable states of matter inaccessible by equilibrium means. Establishing the spatiotemporal link between ultrafast excitation and metastability is crucial to understand these phenomena. Here we utilize single-shot optical pump-X-ray probe measurements to capture snapshots of the emergence of a persistent polar vortex supercrystal in a heterostructure that hosts a fine balance between built-in electrostatic and elastic frustrations by design. By perturbing this balance with photoinduced charges, an initially heterogeneous mixture of polar phase disorders within a few picoseconds, leading to a state composed of disordered ferroelectric and suppressed vortex orders. On the picosecond-nanosecond timescales, transient labyrinthine fluctuations develop, accompanied by the recovery of the vortex order. On longer timescales, these fluctuations are progressively quenched by dynamical strain modulations, which drive the collective emergence of a single vortex supercrystal phase. Our results, corroborated by dynamical phase-field modelling, reveal non-equilibrium pathways following the ultrafast excitation of designer systems to persistent metastability.

2D Conjugated Metal-Organic Frameworks: Defined Synthesis and Tailor-Made Functions.

Conspectus2D conjugated metal-organic frameworks (2D -MOFs) have emerged as a class of graphene-like materials with fully π-conjugated aromatic structures. Their unique structural characteristics provide abundant physiochemical features, including regular nanochannels, high electrical conductivity, and customizable band gaps. Recent intensive research has significantly advanced this field, yet the exploration of 2D c-MOFs with enhanced features is limited by the availability of organic linkages and topologies. Designing novel ligands is essential for the construction of new 2D c-MOFs with high crystallinity, excellent conductivity, and tailor-made functions.In this Account, we summarize our recent contributions in fine-tuning the topology of 2D c-MOFs through precise ligand design, thereby giving them fantastic structures and tailor-made functions. First, we propose the concept of replacing planar ligands by nonplanar ligands on conductive MOF skeletons. The incorporation of nonplanar ligands improves the solubility of large π-conjugated organic molecules without interfering with the interlayer π-stacking. Our investigation discovered that conjugate polycyclic aromatics-based ligands can be synthesized through in situ Scholl reactions by means of oxidative cyclodehydrogenation of a nonplanar precursor ligand during the solvothermal synthesis process. Hence, fully conjugated 2D c-MOFs can be directly synthesized from nonplanar organic ligands, simplifying and diversifying the preparation of 2D c-MOFs. Accordingly, the design flexibility of the ligands expands the topological structures and pore types. By controlling the synthesis conditions, we can successfully induce either a rhombus or a kagome topology from a nonplanar D2 symmetric ligand. Moreover, by employing a ligand engineering strategy, we incrementally increase the number of coordination functional groups on a twisted hexabenzocoronene core, resulting in the formation of three distinct symmetric hydroxyl ligands. These ligands elicit diverse target topologies and pore sizes, resulting in variances in the coordination node density on the skeletons. This, in turn, leads to differences in electron transfer abilities, ultimately causing variations in the electrical conductivity and mobility. In addition, we employ a straightforward coupling method to incorporate redox components, such as salphen and pyrazine, into nonplanar ligands, facilitating the synthesis of 2D c-MOFs with highly active centers. This strategy confers upon the resulting frameworks substantial capacity for catalysis and energy storage, offering a good platform for elucidating the structure-property relationships at the molecular level. Moreover, the well-defined synthesis of 2D c-MOFs imparts them with specific properties, particularly in the fields of electrical, electrochemical, and spintronic applications. At the end, the primary challenges facing 2D c-MOFs in achieving tailor functions and their practical applications are proposed. This account is expected to evoke new inspirations and innovative research in the field of 2D c-MOFs, especially in emerging interdisciplinary research areas.

Machine Learning Methods for Small Data Challenges in Molecular Science.

Small data are often used in scientific and engineering research due to the presence of various constraints, such as time, cost, ethics, privacy, security, and technical limitations in data acquisition. However, big data have been the focus for the past decade, small data and their challenges have received little attention, even though they are technically more severe in machine learning (ML) and deep learning (DL) studies. Overall, the small data challenge is often compounded by issues, such as data diversity, imputation, noise, imbalance, and high-dimensionality. Fortunately, the current big data era is characterized by technological breakthroughs in ML, DL, and artificial intelligence (AI), which enable data-driven scientific discovery, and many advanced ML and DL technologies developed for big data have inadvertently provided solutions for small data problems. As a result, significant progress has been made in ML and DL for small data challenges in the past decade. In this review, we summarize and analyze several emerging potential solutions to small data challenges in molecular science, including chemical and biological sciences. We review both basic machine learning algorithms, such as linear regression, logistic regression (LR), k-nearest neighbor (KNN), support vector machine (SVM), kernel learning (KL), random forest (RF), and gradient boosting trees (GBT), and more advanced techniques, including artificial neural network (ANN), convolutional neural network (CNN), U-Net, graph neural network (GNN), Generative Adversarial Network (GAN), long short-term memory (LSTM), autoencoder, transformer, transfer learning, active learning, graph-based semi-supervised learning, combining deep learning with traditional machine learning, and physical model-based data augmentation. We also briefly discuss the latest advances in these methods. Finally, we conclude the survey with a discussion of promising trends in small data challenges in molecular science.

Spatially resolved steady-state negative capacitance.

Negative capacitance is a newly discovered state of ferroelectric materials that holds promise for electronics applications by exploiting a region of thermodynamic space that is normally not accessible1-14. Although existing reports of negative capacitance substantiate the importance of this phenomenon, they have focused on its macroscale manifestation. These manifestations demonstrate possible uses of steady-state negative capacitance-for example, enhancing the capacitance of a ferroelectric-dielectric heterostructure4,7,14 or improving the subthreshold swing of a transistor8-12. Yet they constitute only indirect measurements of the local state of negative capacitance in which the ferroelectric resides. Spatial mapping of this phenomenon would help its understanding at a microscopic scale and also help to achieve optimal design of devices with potential technological applications. Here we demonstrate a direct measurement of steady-state negative capacitance in a ferroelectric-dielectric heterostructure. We use electron microscopy complemented by phase-field and first-principles-based (second-principles) simulations in SrTiO3/PbTiO3 superlattices to directly determine, with atomic resolution, the local regions in the ferroelectric material where a state of negative capacitance is stabilized. Simultaneous vector mapping of atomic displacements (related to a complex pattern in the polarization field), in conjunction with reconstruction of the local electric field, identify the negative capacitance regions as those with higher energy density and larger polarizability: the domain walls where the polarization is suppressed.

Author Correction: Spatially resolved steady-state negative capacitance.

In this Letter, the first name of author Bhagwati Prasad was misspelled Bhagawati. This error has been corrected online.

Tuning Topology Phases by Controlling Effective Screening and Depolarization in Oxide Superlattices.

Polar topological phases in oxide superlattices attracted significant attention due to their unique properties. Previous work revealed that a polar vortex and polar skyrmions exist in (PTO)/(STO) superlattices under different elastic constraints, i.e., on different substrates. Here, our phase-field simulation demonstrates that manipulating the PTO and STO layers' thickness can control the effective screening provided by STO and the depolarization degree in PTO, thus switching the system among the polar skyrmions, vortex labyrinth, or paraelectric phase without changing elastic constraints. Additionally, reducing the STO thickness creates interlayer coupling among PTO layers, generating the long-range order of topological phases within superlattices. Furthermore, we construct a PTO-STO thickness topological phase diagram. These findings offer insights into the polar topological phases' formation in oxide superlattices, elucidating the roles of ferroelectric and paraelectric layers in their formation.

Self-Adhesive, Stretchable, and Thermosensitive Iontronic Hydrogels for Highly Sensitive Neuromorphic Sensing-Synaptic Systems.

Artificial sensory afferent nerves that emulate receptor nanochannel perception and synaptic ionic information processing in chemical environments are highly desirable for bioelectronics. However, challenges persist in achieving life-like nanoscale conformal contact, agile multimodal sensing response, and synaptic feedback with ions. Here, a precisely tuned phase transition poly(N-isopropylacrylamide) (PNIPAM) hydrogel is introduced through the water molecule reservoir strategy. The resulting hydrogel with strongly cross-linked networks exhibits excellent mechanical performance (∼2000% elongation) and robust adhesive strength. Importantly, the hydrogel's enhanced ionic conductance and heterogeneous structure of the temperature-sensitive component enable highly sensitive strain information perception (GFmax = 7.94, response time ∼ 87 ms), temperature information perception (TCRmax = -1.974%/°C, response time ∼ 270 ms), and low energy consumption synaptic plasticity (42.2 fJ/spike). As a demonstration, a neuromorphic sensing-synaptic system is constructed integrating iontronic strain/temperature sensors with fiber synapses for real-time information sensing, discrimination, and feedback. This work holds enormous potential in bioinspired robotics and bioelectronics.