Xing Yi Ling

This Review examines culinary systems as experimentally accessible models of interfacial materials design. Culinary operations, including emulsification, foaming, gelation, spherification, controlled heating, smoking, and dehydration, govern gas‐liquid, oil‐water, solid‐liquid, polymer‐nanoparticle, and hydrogel‐analyte interfaces, thereby coupling microstructure to transport, mechanics, optical response, and sensory performance. We then analyze how programmable heated high‐shear mixers, pressure cookers, food‐derived hydrocolloids, protein fibrils, metal‐phenolic networks, anthocyanins, and plasmonic nanostructures can support functional materials, sensing, and potentially more sustainable processing. Original Au/agar‐agar and Ag/agar‐agar experiments are presented as bounded proof‐of‐concept demonstrations of this translation. The Review closes by identifying constraints in safety classification, regulatory acceptance, reproducibility, stability, scale‐up, and end‐of‐life assessment. This interface‐centered framework positions culinary chemistry not merely as applied chemistry, but as a source of processing concepts and structure‐property relationships for advanced materials.

Metabolomic detection enables a systems-level understanding of biological processes, while many emerging demands remain unmet. Surface-enhanced Raman spectroscopy (SERS) has recently evolved into a promising platform for metabolic detection yet not reaching true metabolomics. This Mini-Review is motivated by recent advances in understanding molecule–nanomaterial interactions aimed at addressing the related limitations. We first outline the fundamental principles enabling SERS-based metabolomic detection, including specificity, sensitivity, near-field compatibility with small metabolites, and nondestructiveness. Gaps between current SERS techniques and true metabolomics are delineated, and the key technical advances to overcome these challenges are also highlighted, including digital SERS, SERSome, molecule-resolvable SERSome, probe-functionalized nanomaterials, and artificial intelligence-assisted analysis. These developments have enabled SERS across multiple analytical paradigms, spanning targeted detection, phenotypic profiling, and emerging metabolomics. At last, we discuss the future challenges in hopes of advancing SERS from a sensing-oriented technique to a true metabolomic platform, ultimately facilitating the decoding of biological systems.

The spatial confinement of electromagnetic hotspots ( 10 ⁶ , a regime inaccessible to conventional surface-enhanced Raman scattering (SERS) platforms. In silico simulations and experiments reveal ~20-fold enhancements in signal intensity and spatial reach compared to conventional nanoarrays. As a proof of concept, we demonstrate ultrasensitive, label-free classification of extracellular vesicles, 80 to 200 nm in diameter, derived from patients with colorectal cancer with 99.8% accuracy, surpassing traditional SERS (<87.5%). More broadly, this cascaded excitation strategy shifts the emphasis from nanogap optimization to the engineering of spatially extended fields through hybrid light-focusing architectures, enabling advances in spectroscopy, biosensing, nanophotonics, and diagnostics.

Chemical receptors capable of broadly differentiating small ions remain constrained by static binding architectures that limit adaptability across chemically diverse analytes. Here, we introduce sodium 3-mercaptoethanesulfonate (MES-Na) as a surface-bound, conformationally adaptive SERS receptor that exploits the coexistence of gauche and trans states as an intrinsic sensing dimension. Upon anchoring to a plasmonic silver surface, ion binding selectively redistributes the populations of coexisting conformations rather than inducing a single static complex. This conformation-dependent population modulation generates analyte-specific spectral responses governed by relative binding preferences instead of absolute interaction strength. Using this mechanism, we achieve accurate differentiation of 17 inorganic salts comprising cations spanning charges from +1 to +3 and both monatomic and polyatomic anions, with classification accuracies of 99.6% at micromolar concentrations, substantially outperforming a conformationally rigid control receptor. Beyond single-analyte identification, the bidirectional conformational response enables reliable individual and multiplex quantification of mixed ionic systems, with correlation coefficients exceeding 0.92. In addition, spectral augmentation based on experimentally resolved cation- and anion-specific responses enables accurate identification of previously unobserved cation-anion combinations. Collectively, this work establishes dynamic conformational redistribution as a general sensing principle in which population ratio modulation encodes rich interaction information that enables scalable, broad-range ion sensing.

Surface-enhanced Raman scattering (SERS) is a powerful spectroscopic tool for molecular analysis, in which molecular–surface interactions, adsorption energetics, and electronic coupling govern activation, selectivity, and sensitivity. Despite this, SERS interpretation and design have been dominated by electromagnetic field enhancement, leaving chemical enhancement and interfacial understanding limited and fragmented, with surface chemistry often treated as a black box in which ligands and coatings are applied empirically with little mechanistic insight. In this perspective, we reemphasize SERS as an interfacial chemical phenomenon, viewing SERS substrates as chemically active interfaces with tunable energetic, geometric, and electronic states. We introduce a SERS interfacial chemical phase diagram that organizes substrate–analyte interactions across these dimensions, enabling mechanistic understanding, rational control, and predictive design. Furthermore, we evaluate emerging strategies for actively tuning interfacial states through chemical functionalization, porous and compartmentalized materials, adaptive architectures, and hybrid systems, and highlight opportunities to integrate computation, standardized interfacial metadata, and interpretable machine learning to advance SERS into a chemically designable and predictive platform for targeted analysis and operando studies.

Extending surface-enhanced Raman spectroscopy (SERS) from small molecules to macromolecules is important for various applications, yet it remains challenging due to the "surface selection rule". Molecular segments that bind closer to the substrate surface experience stronger signal enhancement and thus dominate the spectrum, resulting in an incomplete or biased SERS response that does not represent the overall molecular structure. DNA oligonucleotides exemplify this challenge, where SERS struggles to extract sequence-level information such as length and primary sequence due to relevant vibrational features being overshadowed by dominant peaks and noise, restricting complete molecular characterization. In this work, we introduce an electrochemical modulation strategy that dynamically reorients oligonucleotides near plasmonic hotspots to enrich information for SERS profiling, achieving comprehensive decoding of oligonucleotide's composition, length, and sequence. By tuning the surface potential of the substrate, we force adsorbed DNA oligonucleotides through distinct configurations and sequentially bring different types and numbers of bases into hotspots. Combining these configuration-specific spectra realizes SERS superprofiles that enrich vibrational information, showcasing a good resolution of 98.4% classification accuracy for 44 oligonucleotides. Importantly, these superprofiles exhibit chemistry-grounded relationships with key sequence characteristics, where integration with a stepwise machine learning (ML) framework enables accurate prediction of unseen oligonucleotides with an average 3.4% difference for base composition, 0.9 bases difference for length, and 100% accuracy for primary sequence. Overall, our electrochemical reorientation strategy overcomes the orientation- and surface-selection biases in static SERS for reliable structural prediction of unknown DNA oligonucleotides. We envisage expanding these dynamic SERS profiles to a vast range of analytes and substrates with diverse chemical functionalities and surface potentials, realizing an extensive space of analyte-surface configurations for future molecular characterization across various fields.

Machine learning analysis of vibrational spectra is often closed-set, performing well for known molecular classes but degrading sharply when test molecules fall outside the training library. Herein, we introduce a SERS-based domain-informed foundation model for fatty acid-derived lipids that replaces categorical assignments with vector matching. The model is trained exclusively on primitive single-chain free fatty acids, with each structural attribute encoded by five orthogonal molecular vectors in a multidimensional space: (1) carbon number, (2) number of C═C bonds, (3) C═C position, (4) C═C geometry, and (5) number of carbon chains. This modular ensemble enables zero-shot prediction of all five vectors in parallel from previously unseen spectra, allowing untargeted molecular reconstruction by proximity in this space rather than by class labels. Despite being trained only on free fatty acids, the model generalizes to complex, multichain lipids absent from the training set, achieving accuracies of 91.7% for withheld free fatty acids, 85.5% for fatty acid esters of hydroxy fatty acids, and 80.0% for triglycerides. To enhance interpretability, we utilize density functional theory to provide a mechanistic basis for the spectral features underlying each prediction. We further demonstrate matrix-tolerant multiplex quantitation in artificial sweat and urine, recovering mixture ratios with 2–9% error across broad composition ranges. Collectively, this strategy enables extrapolative, interpretable spectral-to-structure prediction from SERS across adjacent chemical spaces.