In this study, we report the development and application of a plasmonic nanostructure-based surface-enhanced Raman spectroscopy (SERS) platform, integrated with electrochemical deposition and a convolutional neural network (CNN), for the sensitive and precise detection and quantification of kidney disease biomarkers in urine. A gold nanodimple (AuND) substrate was subjected to electrochemical deposition to produce a highly reproducible and sensitive SERS substrate for protein analysis. Evaluation of the performance of the platform through reproducibility tests and quantitative analysis of Cytochrome C protein demonstrated that it has a high sensitivity and a low detection limit of 8.2 pg/mL. The system was then applied to multiplexed analysis of three kidney disease-related proteins: albumin, transferrin (TrF), and immunoglobulin G (IgG). A combination of ANOVAbased feature selection and CNN classification models achieved high accuracy, with classification accuracies of 93.8 % for albumin, 96.8 % for TrF, and 96.8 % for IgG. Subsequently, CNN-based regression models were utilized to quantify protein concentrations in urine samples, demonstrating robust performance with R2 values of 0.9321 for albumin, 0.9848 for TrF, and 0.9957 for IgG. The method also exhibited excellent diagnostic feasibility, successfully detecting and quantifying target proteins in a urine matrix. The proposed platform thus offers a highly sensitive, reliable and non-invasive approach for early diagnosis of kidney diseases.

The impacts of toxic environmental substances such as plasticizers on human health have been intensively studied in recent years. For ultrasensitive and reliable detection of plasticizers, in the present work, we developed a bimetallic surface-enhanced Raman spectroscopy (SERS) platform in which Ag in Ag-coated cotton fabric nanopillars were subjected to GR with Au (Au@Ag/CFNPs) in the presence of analyte molecules. The hollow regions, which originated from the imbalanced stoichiometric ratio between Au and Ag, functioned as the plasmonic interior hotspots and molecular pathways. The spontaneous galvanic reaction (GR), based on the intrinsic material properties ( i.e. , reduction potential), exhibited repeatable SERS signals with relative standard deviation values of < 10%. For the plasticizers such as benzyl butyl phthalate (BBP) and bisphenol A (BPA), the proposed platform demonstrated sub-ppm sensitivity and a linear relationship between signal intensity and analyte concentration. To investigate the feasibility of the proposed platform in practical applications, a test solution extracted from an actual plastic sample containing BPA was measured using the Au@Ag/CFNP platform. Based on an in-vitro diagnostic cotton fabric (CF) used as a base material, a lateral flow assay (LFA) kit templated with Ag/CFNPs was prepared. With the injection of BBP molecules and Au precursor, the SERS signals could be detected from the LFA-SERS kit down to a BBP concentration of 10 ppm. The results indicate that the Au@Ag/CFNP platform with GR-induced interior hotspots could serve as an alternative to existing standard techniques ( e.g. , pyrolysis, gas chromatography–mass spectrometry) for on-site early screening of plasticizers in food, daily products, and environments.

Conformational transitions of neuroproteins are closely associated with neurological disorders and represent key biomarkers for diagnosis and disease monitoring. A reliable and label-free method for detecting and tracking these structural changes is critical for effective clinical application. In this work, a galvanic molecular entrapment (GME) strategy is presented to combine with surface-enhanced Raman scattering (SERS) for localization and label-free detection of neuroproteins. This approach integrates galvanic replacement with in situ Au surface growth to enable precise molecular entrapment and plasmonic hotspot formation directly around target analytes. Such spatial confinement optimizes analyte positioning within the electromagnetic field, thereby enhancing SERS signal intensity and overcoming geometric mismatches that typically limit sensitivity. The GME method successfully profiled neuroprotein conformational states and demonstrated strong potential for proteomic analysis. A logistic regression model applied to the spectral dataset enabled accurate classification of saliva samples from individuals with epilepsy, schizophrenia, Parkinson's disease, and healthy controls, achieving high sensitivity, specificity, and diagnostic accuracy. This integrated platform offers a non-invasive tool for neurological diagnostics and holds significant promise for advancing personalized healthcare in the management of neurological disorders.

Infections caused by bacteria remain a persistent global concern, highlighting the pressing need for rapid and highly sensitive diagnostic tools. Here, we report a dual-strategy sensing system comprising a 4-mercaptophenylboronic acid (4-MPBA)-functionalized nanopillar Surface-Enhanced Raman Spectroscopy (SERS) substrate and an electrochemical SERS (EC-SERS) platform for label-free and ultrasensitive identification of bacterial pathogens. The 4-MPBA functionalization enables specific recognition of glycan moieties on bacterial cell walls, promoting surface enrichment of bacterial cells on the substrate within 2 h. Subsequent electrochemical gold deposition within 10 min generates uniform plasmonic hot spots directly on bacterial surfaces, effectively bridging the spatial mismatch between bacterial dimensions and SERS-active nanogaps. This approach enabled detection of diverse bacterial species at concentrations ranging from 1 to 100 CFU/mL, representing two to four orders of magnitude improvement over unmodified substrates. Furthermore, eight bacterial species were clearly differentiated using principal component analysis (PCA) and support vector classification (SVC). Taken together, the 4-MPBA-functionalized SERS substrate and EC-SERS platform represent a promising strategy for rapid, sensitive, and label-free identification of diverse bacterial species in complex biological environments.

Respiratory viruses, such as influenza A/B, RSV, SARS-CoV-2 and its variants, continue to be a major global health threat, highlighting the need for rapid and accurate variant-level diagnostics. Herein, we have developed a diagnostic platform for several respiratory viruses by integrating surface-enhanced Raman scattering (SERS) signals from three-dimensional (3D) plasmonic nanopillar substrates with interpretability-driven deep learning. The 3D plasmonic nanopillar array enables robust and reproducible capture of viral components, enhancing the SERS signal for virus-specific molecular fingerprinting. A one-dimensional convolutional neural network (1D-CNN) has been trained on SERS spectra from 13 respiratory virus types, including SARS-CoV-2 variants and sublineages, achieving over 98 % classification accuracy. To further improve model transparency, gradient-weighted class activation mapping (Grad-CAM) has been applied, revealing consistent Raman shift regions critical for virus discrimination across various media conditions. The platform has demonstrated reliable performance even in complex clinical samples, confirming its applicability for real-world diagnostics. The present approach offers a scalable and label-free solution for rapid virus detection, with potential for point-of-care applications and epidemiological surveillance.

Ag layers with high optical transparency and electrical conductivity are ideal for use as conducting electrodes. Achieving robust adhesion between Ag layers and the underlying glass and polymer substrates is crucial for enhancing the performance and reliability of the electrodes. Conventional methods that use metallic adhesion interlayers, such as Ti and Cr interlayers, often degrade the electrical and optical properties of Ag electrodes, limiting their effectiveness. This study introduces a novel method for significantly improving adhesion by incorporating an ultrathin SiOx interlayer between the Ag layers and underlying glass and polymer substrates. Our results demonstrate that Ag layers on substrates with a 5 nm-thick SiOx interlayer exhibit exceptionally strong adhesion, achieving strengths up to 50 N, which exceeds the maximum adhesion strength (< 20 N) achieved using conventional Ti and Cr interlayers. These findings challenge the prevailing belief that strong adhesion between Ag and dielectric oxides cannot be easily achieved. Density functional theory calculations reveal that the enhanced adhesion arises from the segregation and bonding of interfacial O atoms at the Ag-SiOx interface. Our study indicates that the dielectric SiOx interlayer is a promising adhesion promoter, enabling the integration of Ag electrodes with strong adhesion to various substrates.

Significant public health concerns have been raised regarding humans' ubiquitous exposure to bisphenol A (BPA), an endocrine-disrupting chemical, through dietary and environmental pathways. In this study, for the sensitive detection of BPA, we developed three different electrochemical sensors (i.e., Au film, Au nanodimple (AuND), and Au nanopillar (AuNP)) and investigated the influence of electroactive surface area on electrochemical sensing performance. The supporting polymeric nanostructures (i.e., NDs and NPs) were developed using facile plasma treatment processes. Cyclic voltammetry and electrochemical impedance spectroscopy were used to evaluate the electrodes' electroactivity. Compared with the other electrode materials (i.e., Au film and AuNDs), the AuNPs, which exhibited a high density and high aspect ratio, showed excellent redox behaviors and low charge transfer resistance. A quantitative investigation of BPA was conducted using differential pulse voltammetry. Under optimal experimental conditions, the AuNP sensors demonstrated a linear response (R2 = 0.98), nanomolar sensitivity, and high reproducibility (relative standard deviation <= 3.1 %) in the dynamic BPA concentration range from 2 to 1000 nM. The viability of the AuNP sensors in practical applications was also examined with BPA-spiked artificial tear and urine samples. The results highlight that the electrochemical sensors implanted with AuNP platforms are suitable for monitoring BPA in contaminated water and biofluids.

Epigenetic DNA methylations are linked to the activation of oncogenes and inactivation of tumor suppressor genes. A reliable and label-free method to quantitatively measure DNA methylation levels is essential for diagnosing and monitoring methylation-related diseases. Herein, plasmonic molecular entrapment (PME) method assisted SERS as facile strategy for trapping and label-free sensing of DNA methylation, utilizing in situ surface growth of plasmonic particle in the presence of target analytes, are developed. This highly sensitive and adaptable technique forms hotspot sites around target analytes, overcoming mismatch geometrical properties and producing a strong electromagnetic field that leads to significant SERS signal enhancement. The PME method effectively profiles and quantifies DNA methylation, demonstrating robust capabilities for DNA analysis. A logistic regression (LR)-based machine learning accurately quantifies and classifies methylation levels in clinical serum samples of colorectal cancer and normal patients with high sensitivity, specificity, and accuracy, highlighting the feasibility of this technique. The developed PME method combined with machine learning offers promising sensing techniques for disease screening and diagnosis, marking a significant advancement in disease detection and patient care.

Early and accurate diagnosis of Alzheimer's disease (AD) is a major stride toward pharmacological interventions to delay the onset or progression of the disease in patients with mild symptoms. In this study, we developed a silk fibroin-templated surface-enhanced Raman spectroscopy (SERS)-activated double-sandwich immunoassay (immunoSERS) platform that enhances plasmonic hotspot formation for the ultrasensitive detection of biomarkers. Silk fibroin, acting as a natural etching mask, facilitates the direct fabrication of Au nanocavity (AuNC) substrates and enables the immunoSERS platform to achieve attomolar-level detection (limit of detection: 35.8 aM) with high reproducibility (relative standard deviation: ∼2.5 %) due to its unique structural characteristics. This platform effectively detects four core AD biomarkers-amyloid beta 42 (Aβ42), total tau (t-tau), phosphorylated tau (p-tau), and brain-derived neurotrophic factor (BDNF)-in human plasma. Moreover, by introducing a k-nearest neighbors (KNN)-based machine learning algorithm, the suggested platform could classify disease progression stages with 94.0 % accuracy. These results indicate that this silk fibroin-driven immunoSERS platform is a viable alternative to existing diagnostic techniques for the effective early screening of AD and are a potential therapy to delay AD incidence in clinical practice.

Phthalate esters (PAEs) are widely employed as plasticizers to enhance the durability, flexibility, and processability of polymer-based materials. Because PAEs do not form covalent bonds with polymer matrices, they can readily migrate into the environment. This raises significant ecological issues and human health concerns such as endocrine disruption and developmental toxicity. In this study, we developed a surface-enhanced Raman spectroscopy (SERS)-based analytical platform integrated with deep learning algorithms for the rapid and accurate identification and classification of seven representative PAEs. Plasmonic gold nanopillar (AuNP) substrates were engineered to form vertically and horizontally oriented nanogap structures that generate intense localized electromagnetic hotspots, substantially amplifying the Raman signals of adsorbed PAEs. Comprehensive spectral datasets were collected through SERS detection using a portable Raman spectrometer. Deep neural network (DNN) models trained on these data achieved robust classification performance with an accuracy of 99.4 % across all PAE species. Furthermore, SHapley Additive exPlanations (SHAP) analysis provided interpretable insights into the most discriminative Raman spectral features driving the model predictions. The platform was successfully applied to detect PAEs in commercially available consumer products at concentrations near the regulatory threshold of 0.1 % (w/w) established by regulatory bodies such as the European Union's RoHS Directive and the US CPSIA. These findings demonstrate that the integration of nanostructure-enhanced SERS and deep learning constitutes a powerful, high-throughput, and field-deployable approach for reliable PAE detection, with broad applicability in environmental monitoring, consumer product safety evaluation, and regulatory compliance.

The trade-off relationship between cost and performance is a major challenge in the development of surface-enhanced Raman spectroscopy (SERS) sensors for practical applications. We propose a roll-to-roll system with incorporated vacuum sputtering to manufacture Ag-coated nanodimples (Ag/NDs) on A4-scale films in a single step. The Ag/ND SERS platforms were prepared via O2 ion beam sputtering and Ag sputtering deposition. The concave three-dimensional spaces in the Ag/NDs functioned as hotspots, and their optimal fabrication conditions were investigated with two variables: moving speed and Ag thickness. The entire process was automated, which resulted in highly consistent optical responses (i.e., relative standard deviation of ∼10%). The activation of plasmonic hotspots was demonstrated by electric-field profiles calculated via the finite-difference time-domain method. The wavelength dependency of the Ag/ND platforms was also examined by dark-field microscopy. The results indicate that the developed engineering technique for the large-scale production of Ag/ND plasmonic chips would likely be competitive in the commercial market.

Public attention has been emerging for the prevention and assessment of healthcare risks from phthalate esters (PAEs) in daily life. For the effective detection of small environmentally hazardous materials, we suggest two major points: i) the formation of high-density hotspots and ii) the delivery of molecules to the activated plasmonic regions. In this study, we developed a method for laser-induced Marangoni flow in cotton fabric (CF) nanopillars (NPs) deposited with Au (Au/CFNPs) for the detection of PAEs by surface-enhanced Raman spectroscopy (SERS). We optimized the performance of the Au/CFNP platforms by adjusting both the maskless plasma etching time and the Au deposition thickness. Narrow (similar to 8 nm) and populated hotspots were achieved at an Ar plasma treatment time of 30 s and a Au thickness of 150 nm. The molecular behaviors were observed via real-time monitoring of SERS signals. Under continuous laser illumination, Marangoni radial convection flow became predominant over the outward capillary force, resulting in molecular accumulation in the detection areas. For small probe dyes and PAEs, the maximum intensities were achieved within 80 s after the injection of analytes and their signal fluctuations were estimated to have a relative standard deviation of < 10 %, which indicated sensitive and reproducible sensing. The prediction of PAEs extracted from the actual samples was investigated on the basis of a model used to quantitatively fit reference samples and the results were compared with those obtained by high-performance liquid chromatography. The results demonstrated that the combination of densified hotspots on three-dimensional porous substrates and laser-induced Marangoni flow is highly desirable for on-site early screening assays for low-quantity harmful materials present in surrounding environments.

Phthalates are prevalent toxic substances that disrupt the reproductive and endocrine systems of organisms. However, conventional assay tools such as gas chromatography–mass spectrometry, pyrolysis, and high-performance liquid chromatography require prolonged analytic times and complicated procedures. In this study, we developed internal hollow regions in plasmonic nanodimple (ND) substrates through a galvanic reaction method. The dual function ( i.e. , interior hotspots and molecular diffusion paths) of defined areas enabled the surface-enhanced Raman spectroscopy (SERS) detection of phthalates in a rapid, sensitive, reproducible, and reliable manner. The Au@Ag/AuND SERS platforms are highly suitable for early screening of hazardous materials in actual industrial fields.

Public attention to the health and environmental risks from plasticizer exposure in daily life has led to legal restrictions on the use of plasticizers in products. However, the lack of rapid, sensitive, and reliable analytic methods hinders the effective screening of toxic materials. In the present work, we propose a novel electrochemical surface-enhanced Raman spectroscopy (EC-SERS) technique for the detection of plasticizers such as phthalate esters (PAEs) and bisphenol A. The interior hotspots were realized through the EC deposition onto metal nanopillar (ECOMP) platforms in the presence of analytes within 2.5 min. The tiny molecules surrounded by Au produced sufficient amplification of SERS signals, leading to sub-parts-per-billion sensitivity for all the intended specimens. The ECOMP platforms with reproducibility (relative standard deviation of <10%) and linear proportion (R2 ≥ 0.93) were highly suitable for reliable quantitative investigation. A principal component analysis method recognized their slight differences in spectral features. The plasticizer extracted from an actual polyvinyl chloride sample was successfully detected using the proposed technique. The interaction of PAEs with ethanol and the delocalization of PAEs in ECOMP platforms were also discussed. The results demonstrate the feasibility of plasticizer–ECOMP platforms being widely used for onsite testing in industrial fields such as chemical and drug safety.

Microplastics (MPs) are present not only in the environment but also in drinking water, food, and consumer products. These MPs being toxic, carcinogenic, endocrine disrupting, and genetic risk creators cause several diseases. Despite various approaches, the development of onsite applicable, facile, and quick MP detection methods is still challenging. Here, 3D‐plasmonic gold nanopocket (3D‐PGNP) nanoarchitecture is formed on a paper substrate for simultaneous MP filtration and detection. The paper‐based 3D‐PGNP is integrated with a syringe filter device, and then, MP‐containing solutions are injected through the syringe. Subsequent detection of the MPs using the surface‐enhanced Raman scattering (SERS) successfully identifies the MPs without pretreatment. The interface and volumetric hotspot generation of 3D‐PGNP around the captured MPs significantly improves the sensitivity, which is confirmed by finite‐difference time‐domain simulation. Then, the SERS mapping images obtained from a portable Raman spectrometer are transformed into digital signals via machine learning (ML) technique to identify and quantify the MP distribution. The developed SERS‐ML‐based MP detection method is applied for mixture MPs and for real matrix samples, demonstrating that the method provides improved accuracy. This system is expected to be used for various MPs detection and for environmentally hazardous substances, such as bacteria, viruses, and fungi.

To develop onsite applicable cancer diagnosis technologies, a noninvasive human biofluid detection method with high sensitivity and specificity is required, available for classifying cancer from the normal group. Herein, a three-dimensional evolutionary gold nanoarchitecture (3D-EGN) is developed by forming Au nanosponge (AuS) on a 96-well plate, followed by a decoration of Au nanoparticles (AuNPs) evolved with Au nanolamination (AuNL) for high-throughput urine sensing in liquid phase. The 3D-EGN exhibits not only strong electromagnetic field generated from numerous hotspot regions between AuNPs and further enhanced light scattering from multigrain boundaries after lamination process, but also highly volumetric field due to nanoporous structure of AuS, which is advantageous for sensitive liquid-phase SERS detection. SERS activity of the 3D-EGN platform is characterized using malachite green, showing a limit detection of 1.23 × 10-9M in liquid phase, and excellent uniformities both within single well and well-to-well with relative standard deviation (RSD) values of about 10%. The 3D-EGN platform has been demonstrated for the detection of whole clinical human urine samples, proving effective molecular sensing in the presence of Brownian motion from liquid medium. Subsequently, cancer metabolite candidates are investigated to verify the metabolic alternation of multicancer, including pancreatic, prostate, lung, and colorectal cancers, simultaneously classifying them into five different groups, including normal with an accuracy of 95.6%, using machine-learning methods. The integration of nanomaterials with the conventional clinical platform provides rapid and high-throughput multicancer diagnostic system and opens a new era for noninvasive diseases diagnosis using clinical human biofluids.

Point-of-care testing (POCT) for low-concentration protein biomarkers remains challenging due to limitations in biosensor sensitivity and platform integration. This study addresses this gap by presenting a novel approach that integrates a metal-enhanced fluorescence (MEF) biosensor within a capillary flow-driven microfluidic cartridge (CFMC) for the ultrasensitive detection of the Parkinson's disease biomarker, aminoacyl-tRNA synthetase complex interacting multi-functional protein 2 (AIMP-2). Crucial point to this approach is the orientation-controlled immobilization of capture antibody on a nanodimple-structured MEF substrate within the CFMC. This strategy significantly enhances fluorescence signals without quenching, enabling accurate quantification of low-concentration AIMP-2 using a simple digital fluorescence microscope with a light-emitting diode excitation source and a digital camera. The resulting platform exhibits exceptional sensitivity, achieving a limit of detection in the pg/mL range for AIMP-2 in human serum. Additionally, the CFMC design incorporates a capillary-driven passive sample transport mechanism, eliminating the need for external pumps and further simplifying the detection process. Overall, this work demonstrates the successful integration of MEF biosensing with capillary microfluidics for point-of-care applications.

AbstractTo develop a field applicable hazardous molecular detection system, highly sensitive and multiplex detection capability is required for practical utilization. Here, a paper‐based 3D spiky needle‐clustered gold grown on silver (Ag@Au) plasmonic nanoarchitecture (3D‐SNCP) is fabricated through whole solution process. The developed substrate is investigated by scanning electron microscopy (SEM), transmission electron microscopy (TEM) and X‐ray diffraction (XRD) to find out morphological development mechanism. Also, finite‐domain time difference (FDTD) simulation is conducted for the observation of electromagnetic field (E‐field) distribution. After surface‐enhanced Raman scattering (SERS) characterization, the 3D‐SNCP is utilized for ultra‐sensitive and multiplex hazardous molecular detection, such as bipyridine pesticides including paraquat (PQ), diquat (DQ), and difenzoquat (DIF). Then, each of pesticide molecular Raman signals are trained by a machine learning technique of multinomial logistic regression (MLR), followed by multiplex classificationf of blank, PQ, DQ, DIF, and four mixture types of each pesticide, spiked in real agricultural matrix. The developed 3D‐SNCP substrate combined with the machine learning method successfully verifies the multiple pesticides and it is expected to be applied for various hazardous molecular detection in much complicated matrix environments.

Flexible and transparent thin-film silicon solar cells were fabricated and optimized for building-integrated photovoltaics and bifacial operation. A laser lift-off method was developed to avoid thermal damage during the transfer of light-scattering structures onto colorless polyimide substrates and thus enhance front-incidence photocurrent, while a dual n-type rear window layer was introduced to reduce optical losses, facilitate electron transport for rear incidence, and thus enhance performance during bifacial operation. The introduction of the window layer increased the rear-to-front power conversion efficiency ratio to ~86%. The optimized bifacial power conversion efficiency for front and rear irradiances of 1 and 0.3 sun, respectively, equaled 6.15%, and the average transmittance within 500–800 nm equaled 36.9%. Additionally, the flexible and transparent solar cells fabricated using laser lift-off exhibited good mechanical reliability (i.e., sustained 500 cycles at a bending radius of 6 mm) and were therefore suitable for building-integrated photovoltaics.