The realization of high-temperature superconductivity in La3Ni2O7 thin films under ambient pressure has triggered a fundamental inquiry into the microscopic origin of the superconducting ground state—specifically, whether the requisite charge carrier density and orbital polarization are induced purely by lattice strain or necessitate an intrinsic charge reconstruction driven by the interface. Here, we employ a systematic density functional theory (DFT + U) investigation of La3Ni2O7/SrLaAlO4 (001) heterostructures, utilizing a fully self-consistent model that explicitly incorporates substrate-induced compressive strain and interfacial reconstruction. We identify the intrinsic hole doping induced by interfacial Sr interdiffusion as the decisive factor in stabilizing the superconductivity-enabling electronic structure. Crucially, our 1-unit-cell (UC) model naturally reproduces the Fermi surface topology observed in angle-resolved photoemission spectroscopy (ARPES), most notably the critical Ni-dz² derived γ hole pocket at the Fermi level (EF). Layer-resolved analysis further reveals that this γ band originates exclusively from the interface-proximal bilayer, suggesting that the interfacial region dominates macroscopic transport. Based on this, we construct a minimal double-stacked two-orbital tight-binding model. Comparative analysis with the bulk phase attributes the reduced superconducting transition temperature (Tc) in thin films to the combined effects of the significantly reduced density of states (DOS) at EF and the suppression of vertical superexchange coupling (J⊥Z), driven by strain-induced out-of-plane lattice expansion. Our findings establish that interface engineering goes beyond mere strain imposition, acting as a decisive factor in modulating nickelate orbital physics.

Baseline correction is a critical preprocessing step to eliminate non-Raman scattering backgrounds in the Raman/SERS spectrum, ensuring accurate peak positions and intensities for qualitative and quantitative analysis. Recently, various machine learning-based approaches have been proposed for automatic baseline correction. Nevertheless, their generalizability is constrained, since neither background physicochemical origins nor critical fitting parameters are fully understood. Therefore, we developed AirNet, an automated baseline correction algorithm that integrates deep learning with chemometrics to balance global smoothness and local fidelity. AirNet includes three steps: (1) Raman peaks and baselines are identified with initialized weights by a ResUnet-based model; (2) an optimal smoothing parameter is adaptively selected by a multi-indicator evaluation strategy; (3) a reliable baseline is achieved with refined weights by a dynamic and robust adaptive iteratively reweighted penalized least squares (Dr-airPLS) under optimized smoothness. On both simulated and experimental Raman spectra, AirNet outperforms algorithms like airPLS, OP-airPLS, and DIRAS in both accuracy and generalizability, with a speed of 0.3 s per spectrum. Furthermore, the Homologous Model of AirNet ensures consistent baseline correction across homologous spectra. The segmented fitting strategy applies a region-specific smoothing parameter, facing a Raman spectrum with drastically changed background gradients. AirNet extracts high-fidelity Raman spectral information, providing a solid basis for reliable spectrum-structure correlation.

Polymers are integral to flexible and miniaturized electronics, but their inherently low thermal conductivity limits performance in high-power applications. While most efforts focus on enhancing intrachain phonon transport, the influence of interchain configuration remains poorly understood. In this study, we investigate thermal transport in twelve crystalline conjugated polymers by engineering interchain configurations (parallel vs. perpendicular) using atomistic simulations. Across all systems, the perpendicular configuration consistently yields higher axial thermal conductivity, with enhancements exceeding a factor of two in some cases. For example, planar poly(p-phenylene) (PPP) exhibits a thermal conductivity of 205.2 +/- 11.1 W m-1 K-1 in the perpendicular configuration, compared to 119.5 +/- 7.5 W m-1 K-1 in the parallel arrangement. Mechanistically, a perpendicular interchain configuration with a planar backbone significantly strengthens non-bonded interactions, enhances dihedral ordering, stiffens bonds, and suppresses angular fluctuations. These synergistic effects substantially reduce phonon scattering and increase phonon lifetimes. Overall, this study identifies interchain configuration as a key structural factor governing thermal transport in crystalline conjugated polymers, and provide physical insights and design guidelines for the rational development of high-thermal-conductivity polymers for thermal management applications.

Retrieving or generating two-dimensional molecular structures on the basis of vibrational spectra has been well demonstrated via deep learning models. However, deciphering three-dimensional molecular conformations is still challenging, primarily due to spectral ambiguities caused by conformational heterogeneity, which are difficult to resolve. To address this limitation, we propose Vib2Conf, a deep learning model directly discriminating 3D molecular conformations from vibrational spectra. We implement an attentional resampler to distill conformation-sensitive features from sparse spectral signals, and integrate Mixture-of-Experts (MoE) to partition the conformational space for precise geometric mapping. These modules enable Vib2Conf to achieve state-of-the-art top-1 recall exceeding 95% on traditional spectrum-structure benchmarks, including QM9S, VB-Mols, and QMe14S. More importantly, Vib2Conf can discriminate near-isomeric conformers with a top-1 recall of 82.06% on VB-Confs test set, where conformational isomers differ by a root-mean-square deviation (RMSD) of only ∼1 Å. In general, Vib2Conf is a promising method for fine-grained spectrum-to-conformation analysis.

Thermal transport across interfaces is a critical bottleneck in the thermal management of modern microelectronics, particularly as devices scale toward the nanoscale with increasingly high-power densities. While bulk material properties are well understood, the physics governing heat transfer at interfaces, defined by carrier transmission and scattering, remains a complex challenge. Here, we review methods and studies on interfacial thermal transport spectroscopy, bridging fundamental theory with the state-of-the-art modelling and experiments. We first examine the fundamentals of the phonon gas model and carrier coupling, followed by a detailed discussion of computational approaches ranging from atomistic Green's functions (AGF) and molecular dynamics (MD) simulations at the nanoscale to the Boltzmann transport equation (BTE) at the microscale. Then, we evaluate popular experimental techniques, such as frequency-domain thermoreflectance (FDTR), time-domain thermoreflectance (TDTR) and electron energy loss spectroscopy (EELS), emphasizing their role in resolving spectral phonon contributions. We further highlight emerging data-driven methods and machine learning approaches that accelerate physical understanding and materials discovery. Finally, we outline open challenges in characterizing spectral interfacial thermal transport within computational and experimental frameworks, as well as the persistent gaps between them. This review aims to provide a unified perspective on understanding and optimizing interfacial heat dissipation for next-generation electronic and energy devices.

Broad spectral response and high optoelectronic performance are essential metrics for perovskite-based photodetectors. Conventional approaches for achieving broadband perovskites primarily rely on structural or compositional modifications, which can be limited in extending the spectral response and may lack universal suitability. Here, we introduce a molecule-assisted interface engineering strategy to modulate charge transfer in CH3NH3PbCl3 single-crystalline photodetectors without altering their intrinsic structure. Interfacial charge transfer between CH3NH3PbCl3 and a perylene-3,4,9,10-tetracarboxylic acid dihydride (PTCDA) layer plays a crucial role in extending the photoresponse from the ultraviolet to the visible region, as confirmed by surfaceenhanced Raman scattering (SERS) spectroscopy. The PTCDA-engineered CH3NH3PbCl3 devices exhibit a photoresponsivity of 0.1 A.W-1 and a detectivity exceeding 10(12) Jones under 532 nm illumination. Photoresponsivity was found to depend on the molecular layer thickness, with excessive thickness reducing performance. This work demonstrates an effective strategy for broadening the spectral response of perovskitebased photodetectors. Moreover, SERS spectroscopy has been demonstrated as a powerful tool for probing interfacial charge transfer processes in devices at the molecular level.

Abstract Thermal conductivity (κ) is a pivotal physical property governing the performance of crystalline polymers in advanced technologies ranging from thermoelectrics to electronic thermal management. However, low-cost and rapid prediction of κ remains challenging due to the structural complexity of polymers and the three-order-of-magnitude span of κ values (0.1–100 W m−1 K−1) across different crystalline polymers. Herein, we present two refined theoretical models for fast prediction of chain-direction κ in PCFF-compatible crystalline polymers, built on a physics-informed framework that combines significant dataset expansion and machine-learning (ML)-driven descriptor optimization. First, we expanded the PCFF-compatible dataset from 42 to 157 crystalline polymers. Second, we leveraged Python libraries and ML tools for descriptor analysis: we constructed 16 physically interpretable descriptors tied to phonon transport properties and unit-cell characteristics and then performed exhaustive ML-assisted screening to identify optimal four-descriptor sets for two complementary fitting routes. Collectively, the models incorporate four key descriptors: backbone rotation ratio (P), fraction of carbon atoms in the unit cell (C), unit-cell packing fraction (Φ), and interchain density of noncovalent atom pairs (ρin). Route I preserves the Slack-model-derived crystalline term f1 for enhanced physical interpretability and only optimizes the correction factor f2, while Route II jointly optimizes f1 and f2 to maximize predictive performance. Both models achieve a balance of simplicity, predictive performance, and mechanistic transparency, with mean absolute logarithmic error (MALE) and root-mean-square logarithmic error (RMSLE) that are lower than those of our previous linear-regression-based model and even approach the predictive performance of ML models (random forest (RF), Gaussian process regression (GPR), and support vector regression (SVR)). Mechanistic analysis reveals that smaller P, C, and Φ, combined with larger ρin, synergistically prolong phonon lifetimes, enhance group velocities, and increase volumetric heat capacity, collectively boosting chain-direction κ. These advancements enable more reliable rapid screening of κ and provide actionable guidelines for the rational design of crystalline polymers with target κ values, advancing the development of polymer-based materials for energy conversion and thermal management applications.

Metal hydrides are typically nonstoichiometric compounds at ambient pressure, commonly exhibiting hydrogen vacancies. Recently, hydrogen-rich metal hydrides featuring clathrate hydrogen sublattices have attracted great attention as potential room-temperature superconductors under pressure. However, accurately determining their hydrogen stoichiometry remains experimentally challenging, and the impact of hydrogen vacancies on their superconductivity is unclear. Here, we investigate the superconductivity and thermodynamic properties of CeH10 with hydrogen vacancies. Our results reveal that the nonstoichiometric clathrate hydride CeH9.75, featuring hydrogen vacancies in a fcc lattice, exhibits lower thermodynamic stabilization pressure compared to the perfect CeH10 crystal. While hydrogen vacancies are generally known to induce superionic states in clathrate hydrides, we find that the superionic transition temperature for CeH9.75 is actually higher than that of CeH10, suggesting that hydrogen vacancies may increase the hydrogen diffusion barrier and enhance the stability of clathrate hydrides under pressure. Furthermore, the calculated superconducting transition temperature of CeH9.75 show excellent agreement with experimental data. These findings indicate that nonstoichiometry may be a common feature in the high-pressure phase diagrams of clathrate hydrides.

Surface-enhanced Raman spectroscopy (SERS) has evolved significantly over fifty years into a powerful analytical technique. This review aims to achieve five main goals. (1) Providing a comprehensive history of SERS's discovery, its experimental and theoretical foundations, its connections to advances in nanoscience and plasmonics, and highlighting collective contributions of key pioneers. (2) Classifying four pivotal phases from the view of innovative methodologies in the fifty-year progression: initial development (mid-1970s to mid-1980s), downturn (mid-1980s to mid-1990s), nano-driven transformation (mid-1990s to mid-2010s), and recent boom (mid-2010s onwards). (3) Illuminating the entire journey and framework of SERS and its family members such as tip-enhanced Raman spectroscopy (TERS) and shell-isolated nanoparticle-enhanced Raman spectroscopy (SHINERS) and highlighting the trajectory. (4) Emphasizing the importance of innovative methods to overcome developmental bottlenecks, thereby expanding the material, morphology, and molecule generalities to leverage SERS as a versatile technique for broad applications. (5) Extracting the invaluable spirit of groundbreaking discovery and perseverant innovations from the pioneers and trailblazers. These key inspirations include proactively embracing and leveraging emerging scientific technologies, fostering interdisciplinary cooperation to transform the impossible into reality, and persistently searching to break bottlenecks even during low-tide periods, as luck is what happens when preparation meets opportunity.

There will be a paradigm shift in chemical and biological research, to be enabled by autonomous, closed-loop, real-time self-directed decision-making experimentation. Spectrum-to-structure correlation, which is to elucidate molecular structures with spectral information, is the core step in understanding the experimental results and to close the loop. However, current approaches usually divide the task into either database-dependent retrieval and database-independent generation and neglect the inherent complementarity between them. In this study, we proposed Vib2Mol, a unified deep learning framework designed to flexibly handle diverse spectrum-to-structure tasks according to the available prior knowledge by bridging the retrieval and generation. Empowered by our coarse-to-fine retrieval and generate-then-rerank strategies, Vib2Mol not only achieves state-of-the-art performance in analyzing theoretical Infrared and Raman spectra, but also outperform previous models on experimental data. Moreover, our model demonstrates promising capabilities in predicting reaction products and sequencing peptides, enabling vibrational spectroscopy a potential guide for autonomous scientific discovery workflows.

Soft biomaterials, characterized by structural disorder and weak interactions, pose significant challenges for structural characterization by conventional methods, which typically rely on long-range crystalline order or strong electron scattering. Here, we report a computationally assisted polarized Raman spectroscopy strategy that enables direct, in situ determination of three-dimensional (3D) molecular structures and their intermolecular interactions, correlating molecular-scale orientations with macroscopic morphologies. We validated our approach using the well-characterized α-glycine crystal, achieving structural precision comparable to that of single-crystal X-ray diffraction (RMSD = 0.367 ± 0.003 Å). Extending our method to structurally complex peptides, we resolve biomaterial diphenylalanine structure (RMSD = 0.743 ± 0.009 Å) and determine Alzheimer's-associated peptide (r-acAβ) assemblies at angstrom precision. Furthermore, our approach uniquely identified specific vibrational signatures associated with hydrogen bonding and hydrophobic interactions, linking the reversible switching of peptide self-assembly through environmental stimuli. This method provides a transformative structural characterization platform, offering critical insights into molecular assembly mechanisms and enabling the rational design of next-generation functional biomaterials.

The year 2024 marks the 50th anniversary of the discovery of surface-enhanced Raman spectroscopy (SERS). Over recent years, SERS has experienced rapid development and became a critical tool in biomedicine with its unparalleled sensitivity and molecular specificity. This review summarizes the advancements and challenges in SERS substrates, nanotags, instrumentation, and spectral analysis for biomedical applications. We highlight the key developments in colloidal and solid SERS substrates, with an emphasis on surface chemistry, hotspot design, and 3D hydrogel plasmonic architectures. Additionally, we introduce recent innovations in SERS nanotags, including those with interior gaps, orthogonal Raman reporters, and near-infrared-II-responsive properties, along with biomimetic coatings. Emerging technologies such as optical tweezers, plasmonic nanopores, and wearable sensors have expanded SERS capabilities for single-cell and single-molecule analysis. Advances in spectral analysis, including signal digitalization, denoising, and deep learning algorithms, have improved the quantification of complex biological data. Finally, this review discusses SERS biomedical applications in nucleic acid detection, protein characterization, metabolite analysis, single-cell monitoring, and in vivo deep Raman spectroscopy, emphasizing its potential for liquid biopsy, metabolic phenotyping, and extracellular vesicle diagnostics. The review concludes with a perspective on clinical translation of SERS, addressing commercialization potentials and the challenges in deep tissue in vivo sensing and imaging.

Recently, the superconductivity of bilayer nickelate La3Ni2O7 has been observed in the thin film at ambient pressure, facilitated by epitaxial strain. Here, we investigate the effects of film thickness and carrier doping on the electronic structure of La3Ni2O7 thin films with thickness of 0.5-3 unit cells (UC) using first-principles calculations. At an optimal doping concentration of 0.4 holes per formula unit for 2UC film, the Ni-"d" _("z" ^"2" ) interlayer bonding state metallizes, leading to the formation of γ pockets at the Fermi surface, which quantitatively matches the experimental results of angle-resolved photoemission spectroscopy (ARPES). These findings provide theoretical support for recent experimental observations of ambient-pressure superconductivity in La3Ni2O7 thin films and highlight the crucial role of film thickness and carrier doping in modulating electronic properties.

On the 50th anniversary of the discovery of surface-enhanced Raman spectroscopy (SERS), numerous reviews highlighted SERS advancements from different aspects, such as the historical evolution, enhancement mechanisms, quantitative analysis, and medical applications. However, how to develop the rapid SERS analysis for real samples has rarely been summarized yet. This review highlights the following three pivotal steps in this direction: (1) establishing reliable and highly selectively sensitive SERS analysis in the laboratory; (2) developing rapid sample pretreatment; and (3) AI-enhanced qualitative and quantitative SERS analysis.

Strengthening the interaction between the target and SERS substrate is crucial for sensitive SERS detection; we thereby explored the molecular structure-dependent SERS sensitivity for negatively charged targets on the positively charged SERS substrate. Both experimental and theoretical studies confirm that the SERS sensitivity is determined by the electrostatic interaction between the target and linker. This interaction is not only manipulated by the protonation capacity of the linker and its surface adsorption configuration and geometry but also significantly determined by the target's structure, encompassing electronegativity and the number of interaction sites. The optimized interaction leads to a marked improvement in detection sensitivity of up to 1-3 orders of magnitude. The interaction mechanism revealed in this work not only provides theoretical guidance and technical support for electrostatically driven SERS detection but also offers a conceptual framework that can be extended to various SERS detections based on diverse surface forces.

Nowadays, surface-enhanced Raman spectroscopy (SERS) has become a powerful tool for rapidly detecting and analyzing textile cultural relics due to its ability to provide chemical information with single-molecule sensitivity. However, preserving a high level of detection sensitivity while avoiding sample damage remains a persistent challenge. In this work, we developed a SERS approach with both microextraction and detection functions. The alcohol-water droplet with iodide-modified Au nanoparticles (AuIMNPs) is directly dropped on the textile, where dyes strongly bound on textiles can be extracted by ethanol (EtOH). As a result, the sample can be well preserved from being damaged. In particular, the volatility of EtOH allows the molecules to be captured in the hot spots through the capillary effect during droplet evaporation, resulting in a dramatic increase in Raman signal intensity. This highly sensitive strategy can be used to measure dyes in plant extracts and mock-up textiles. Furthermore, the capability of SERS to provide fingerprint information allows us to distinguish different dyes in overdyeing textiles. Eventually, this approach is successfully applied to identify dyes of authentic ancient Chinese textiles. This rapid, universal, and negligibly invasive approach provides a powerful way to study textile cultural relics. Developing a noninvasive technique to identify ancient textile dyes is crucial for human civilization. A SERS measurement approach with the alcohol-water droplet is proposed to realize both microextraction and detection of dyes in textile cultural relics. Without any sample pretreatment, dyes in plant extracts and artificial textiles can be identified. Furthermore, different dyes are distinguished in overdyeing through the fingerprint information of SERS. Eventually, this approach is successfully applied to identify dyes of authentic ancient Chinese textiles.image

While surface-enhanced Raman spectroscopy (SERS) has experienced substantial advancements since its discovery in the 1970s, it is an opportunity to celebrate achievements, consider ongoing endeavors, and anticipate the future trajectory of SERS. In this perspective, we encapsulate the latest breakthroughs in comprehending the electromagnetic enhancement mechanisms of SERS, and revisit CT mechanisms of semiconductors. We then summarize the strategies to improve sensitivity, selectivity, and reliability. After addressing experimental advancements, we comprehensively survey the progress on spectrum-structure correlation of SERS showcasing their important role in promoting SERS development. Finally, we anticipate forthcoming directions and opportunities, especially in deepening our insights into chemical or biological processes and establishing a clear spectrum-structure correlation.

Existential state of solutes substantially affects the efficiency and direction of various chemical and biological processes, about which current consensus is still limited at macro and micro levels. At the trace level, solutes assume a pivotal role across a spectrum of critical fields. However, their existential states, especially at interfaces, remain largely elusive. Herein, an exceptional evolution of solute molecules is unveiled from micro to trace, solution to interface, with the aid of surface-enhanced Raman spectroscopy, extinction, DLS and theoretical simulations. Given predominant existence of monomers within the solution, these aggregates dominate the interfacial behavior of solute molecules. Moreover, a universal, aggregate-controlled mechanism is demonstrated that aggregates triggered by cosolvent, which can dramatically promote efficiency of catalytic reactions. The results provide novel insights into the interaction mechanisms between reactants and catalysts, potentially offering fresh perspectives for the manipulation of multiphase catalysis and related biological processes.

Spectrum-structure correlation is playing an increasingly crucial role in spectral analysis and has undergone significant development in recent decades. With the advancement of spectrometers, the high-throughput detection triggers the explosive growth of spectral data, and the research extension from small molecules to biomolecules accompanies massive chemical space. Facing the evolving landscape of spectrum-structure correlation, conventional chemometrics becomes ill-equipped, and deep learning assisted chemometrics rapidly emerges as a flourishing approach with superior ability of extracting latent features and making precise predictions. In this review, the molecular and spectral representations and fundamental knowledge of deep learning are first introduced. We then summarize the development of how deep learning assist to establish the correlation between spectrum and molecular structure in the recent 5 years, by empowering spectral prediction (i.e., forward structure-spectrum correlation) and further enabling library matching and de novo molecular generation (i.e., inverse spectrum-structure correlation). Finally, we highlight the most important open issues persisted with corresponding potential solutions. With the fast development of deep learning, it is expected to see ultimate solution of establishing spectrum-structure correlation soon, which would trigger substantial development of various disciplines.