Surface-enhanced Raman spectroscopy (SERS) is a powerful technique for discrimination of bimolecules in complex systems. However, its practical applications face challenges such as complicated manufacturing procedures and limited scalability of SERS substrates, as well as poor reproducibility during detection which compromises the reliability of SERS-based analysis. In this study, we developed a convenient method for simultaneous fabrication of massive SERS substrates with an internal standard to eliminate the substrate-to-substrate differences. We first synthesized Au@CN@Au nanoparticles (NPs) which contain embedded internal standard molecules with a single characteristic peak in the Raman-silent region, and then deposited the NPs on 6 mm glass wafers in a 96-well plate simply by centrifugation for 3 min. The one-time obtained 96 SERS substrates have excellent intrasubstrate uniformity and intersubstrate repeatability for SERS detection by using the internal standard (relative standard deviation = 10.47%), and were able to detect both charged and neutral molecules (crystal violet and triphenylphosphine) at a concentration of 10-9 M. Importantly, cells can be directly cultured on glass wafers in the 96-well plate, enabling real time monitoring of the secretes and metabolism change in response to external stimulation. We found that the release of nucleic acids, amino acids and lipids by MDA-MB-231 cells significantly increased under hypoxic conditions. Overall, our approach enables fast and large-scale production of Au@CN@Au NPs-coated glass wafers as SERS substrates, which are homogeneous and highly sensitive for monitoring trace changes of biomolecules.

Among various biomimetic polymer materials, polydimethylsiloxane (PDMS) stands out as an ideal matrix for surface-enhanced Raman scattering (SERS) due to its unique intrinsic Raman signal and tenacity. In order to realize the precise detection of prostate-specific antigen (PSA), we proposed a sandwich-type SERS-active immunostructure composed of PDMS@silver nanoparticles (Ag NPs)@ZIF-67 biomimetic film as the immunosubstrate and gold nanorods (Au NRs) as immunoprobes. Due to the synergistic effect of electromagnetic enhancement facilitated by biomimetic surfaces and chemical enhancement achieved by ZIF-67, this structure enabled an ultrasensitive and selective detection of PSA across a broad range from 10-3 to 10-9 mg/mL. The achieved limit of detection was as low as 3.0 × 10-10 mg/mL. Particularly, the intrinsic Raman signal of PDMS matrix at 2905 cm-1 was employed as a potential internal standard (IS) in the detection, achieving a high coefficient of determination (R2) value of 0.996. This multifunctional SERS substrate-mediated immunoassay holds vast potential for early diagnosis of prostate cancer, offering promising prospects for clinical applications.

Surface-enhanced Raman spectroscopy (SERS) can quickly identify molecular fingerprints and has been widely used in the field of rapid detection. However, the non-uniformity inherent in SERS substrate signals, coupled with the finite nature of the detection object, significantly hampers the advancement of SERS. Nowadays, the existing mature immunochromatographic assay (ICA) method is usually combined with SERS technology to address the defects of SERS detection. Nevertheless, the porous structure of the strip will also affect the signal uniformity during detection. Obviously, a method using SERS-ICA is needed to effectively solve signal fluctuations, improve detection accuracy, and has certain versatility. This paper introduces an internal standard method combining deep learning to predict and process Raman data. Based on the signal fluctuation of single-antigen SERS-ICA test strip, the double-antigen SERS-ICA test strip was constructed. The full spectrum Raman data of double-antigen SERS-ICA test strip was normalized by the sum of two characteristic peaks of internal standard molecules, and then processed by deep learning algorithm. The Relative Standard Deviation (RSD) of Raman data of bisphenol A was compared before and after internal standard normalization of double-antigen SERS-ICA test strip. The RSD processed by this method was increased by 3.8 times. After normalization, the prediction accuracy of Root Mean Square Error (RMSE) is improved by 2.66 times, and the prediction accuracy of R-square (R2) is increased from 0.961 to 0.994. The results showed that RMSE and R2 were used to comprehensively predict the collected data of double-antigen SERS-ICA test strip, which could effectively improve the prediction accuracy. The internal standard algorithm can effectively solve the challenges of uneven hot spots and poor signal reproducibility on the test strip to a certain extent, so as to improve the semi-quantitative accuracy.

Simultaneous, reliable, and ultra-sensitive analysis of promising miRNA biomarkers of colorectal cancer (CRC) in serum is critical for early diagnosis and prognosis of CRC. In this work, we proposed a novel 3D hierarchic assembly clusters-based SERS strategy with dual enrichment and enhancement designed for the ultrasensitive and quantitative analysis of two upregulated CRC-related miRNAs (miR-21 and miR-31). The biosensor contains the following: (1) SERS probe, Au nanocage@Au nanoparticles (AuNC@Au NPs) labeled with Raman reporters (RaRs). (2) magnetic capture unit, Ag-coated Fe3O4 magnetic nanoparticles (AgMNPs) modified with internal standard (IS). (3) signal amplify probes (SA probes) for the formation of hierarchic assembly clusters. Based on this sensing strategy, the intensity ratio IRaRs/IIS with Lg miRNAs presents a wide linear range (10 aM-100 pM) with a limit of detection of 3.46 aM for miR-21, 6.49 aM for miR-31, respectively. Moreover, the biosensor shows good specificity and anti-interference ability, and the reliability and repeatability of the strategy were then verified by practical detection of clinical serum. Finally, the biosensor can distinguish CRC cancer subjects from normal ones and guide the distinct tumor, lymph node, and metastasis (TNM) stages. Overall, benefiting from the face-to-face coupling of hierarchic assembly clusters, rapid magnetic enrichment and IS signal calibration of AgMNPs, the established biosensor achieves ultra-sensitive and simultaneous detection of dual miRNAs and opens potential avenues for prediction and staging of CRC.

Nitrites are ubiquitous in food and pose a serious threat to human health. Therefore, the rapid and accurate determination of nitrite ion concentration in food is a prerequisite for eliminating the damage of nitrites. In this study, a robust, rapid, and sensitive method is proposed for nitrite detection in pickled food, in which Au@Ag nanoparticles are used as a reliable surface-enhanced Raman spectroscopy (SERS) substrate taking advantage of the high enhancement effect of silver and the good stability of gold. Nitrites were anchored to the surface of the SERS substrate by bridging with 4-aminophenylthiophenol (PATP). With Raman scattering cross-section amplification and internal calibration by PATP, a satisfactory linear relationship (R2 = 0.987) was established for nitrite detection in the concentration range of 5.00-100.00 μM, and the limit of detection (LOD) was 0.17 μM. This SERS-based method demonstrated high selectivity, good precision (RSD < 7.00 %), and satisfying recovery rates (101.42-107.35 %) in real samples, thus improving the determination method for nitrites. Therefore, this method has application potential in food safety and supervision.

Biomimetic materials with fascinating natural micro-nano surface structures offer a good choice for the simple fabrication of surface-enhanced Raman scattering (SERS) substrate. This study presented a novel sodium carboxymethylcellulose (NaCMC)-Ag biomimetic substrate which was fabricated through the reverse replication of micro-nano structures from cantaloupe peel. Particularly, silicon nanoparticles (Si NPs) were doped into this flexible biomimetic substrate in its fabrication process. Abundant electromagnetic \"hotspots\" could be effectively excited in this Ag densely covered matrix which maintained numerous protrusions as well as vertical and horizontal grooves. Specifically, the doped Si NPs exhibited a robust intrinsic Raman peak, which could be employed as an internal standard to calibrate the target signal. In this regard, the biomimetic substrate with the optimal electromagnetic enhancement and the quantitative calibration capabilities exhibited a high enhancement factor and a remedied linear relationship in the detection. After a perfect uniformity of signal was proved by the corrected SERS mapping, the biomimetic SERS substrate was finally utilized in the practical analysis of methylene blue (MB) and β-carotene with ultra-low limit of detection, highlighting its importance in practical detection scenarios.

Huanglongbing (HLB) is a citrus infectious disease caused by \'Candidatus Liberibacter\' spp. Recently, it has begun to spread rapidly worldwide, causing significant losses to the citrus industry. Early diagnosis of HLB relies on quantitative real-time PCR assays. However, the PCR inhibitors found in the nucleic acid extracted from plant materials pose challenges for PCR assays because they may result in false-negative results. Internal standard (IS) can be introduced to establish a single-tube duplex PCR for monitoring the influence of the PCR inhibitor, but it also brings the risk of false-negative results because the amplification of IS may compete with the target. To solve this problem, we proposed a mutation-enhanced single-tube duplex PCR (mSTD-PCR) containing IS with mutant-type primers. By introducing the 3\'-terminal mutation in the primer of IS to weaken its amplification reaction and its inhibition of \'Candidatus Liberibacter asiaticus\' (CLas) detection, the sensitivity and quantitative accuracy of CLas detection will not be affected by IS. In evaluating the sensitivity of CLas detection using simulation samples, the mSTD-PCR showed consistent sensitivity at 25 copies per test compared with the single-plex CLas assay. The detection result of 30 leaves and 30 root samples showed that the mSTD-PCR could recognize false-negative results caused by the PCR inhibitors and reduce workload by 48% compared with the single-plex CLas assay. Generally, the proposed mSTD-PCR provides a reliable, efficient, inhibitor-monitorable, quantitative screening method for accurately controlling HLB and a universal method for establishing a PCR assay for various pathogens.

Surface-enhanced Raman scattering (SERS) technique with high sensitivity, reliable specificity, and rapid recognition ability exhibits attractive promise for the effective fast-monitoring of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Herein, a novel SERS-active immunoassay kit for SARS-CoV-2 nucleocapsid (N) protein was prepared by in-situ growing gold (Au) nanowire forests (NFs) onto Ti3C2Tx, which was then modified onto polymethyl methacrylate (PMMA) matrix and encapsulated into kit. It was noted that the Au nanowires with fibrous structures which vertically anchored on Ti3C2Tx served as perfect channels to promote photo-induced charge transfer. The synergistic action of electromagnetic and chemical effects resulted in an enhancement factor (EF) of 1.27 × 107. Furthermore, the unreliable fluctuation of the enhanced signal was eliminated by using the intrinsic Raman signal of the flexible PMMA platform, achieving an improved correlation coefficient (R2) value from 0.950 to 0.990. Moreover, the as-designed immunoassay kit with both high sensitivity and remedied quantitative ability rendered by the Ti3C2Tx@Au NFs-PMMA composite exhibited a powerful performance in the practical detection of N-protein with concentration low to 5.0 × 10-8 mg/mL.

Nicotine is an addictive substance often found in tobacco and cigarette smoke and excessive exposure to it can cause various diseases. Herein, core-molecule-shell gold/4-aminothiophenol/silver nanorods (Au@PATP@Ag NRs) were prepared for quantitative detection of nicotine by using surface-enhanced Raman scattering (SERS) technology. The obtained Au@PATP@Ag NRs showed an outstanding SERS effect due to the plasticity of their morphology and the bimetallic synergistic effect between the excellent stability of Au and the highly enhanced effect of Ag. The Au@PATP@Ag NRs substrate exhibited an extremely high enhancement factor (EF) of 2.17 × 107. In addition, in-situ synthesized PATP was used as an internal standard to correct signal fluctuation and improve the reliability of quantitative nicotine detection. A wide linear dynamic range from 10-8 to 10-3 M was obtained and an ultra-low limit of detection (LOD) was about 3.12 × 10-9 M, which was superior to most of previously reported methods. This work has also been used for determining nicotine content in cigarettes and simulated environmental tobacco smoke by using a portable device. These results indicated that the developed SERS method had many potential applications in the quantitative determination of nicotine in real tobacco samples.

Abnormal uric acid (UA) content in body fluids can fully reflect the status of metabolism and immunity in the body. We have developed a simple, efficient and label-free surface enhanced Raman scattering (SERS) method for UA detection. Briefly, p-aminothiophenol (p-ATP) was used as the internal standard molecule and linking molecule to prepare a glass/p-ATP/Ag NPs SERS substrate. The Raman characteristic peak of p-ATP at 1076 cm-1 can be used as an internal standard molecule to correct the signal fluctuation of UA detection. The results show that the SERS method owns a linear response with a ranging from 5 × 10-6 to 10-3 M of UA characteristic peak of both 693 cm-1 and 493 cm-1 with a determination coefficient (R2) of 0.9878 and 0.9649, respectively. Additionally, the SERS sensor has been further used for the analysis of UA in sweat and good recoveries were obtained for the sensing of sweat. We believe that the developed SERS substrate has potential for applications in healthcare monitoring.