Portable Raman Instrument Research Articles

A double boronic acid affinity "sandwich" SERS biosensor based on magnetic boronic acid controllable-oriented imprinting for high-affinity biomimetic specific recognition and rapid detection of target glycoproteins.

Transferrin (TRF), recognized as a glycoprotein clinical biomarker and therapeutic target, has its concentration applicable for disease diagnosis and treatment monitoring. Consequently, this study developed boronic acid affinity magnetic surface molecularly imprinted polymers (B-MMIPs) with pH-responsitivity as the "capture probe" for TRF, which have high affinity similar to antibodies, with a dissociation constant of (3.82 ± 0.24) × 10-8M, showing 7 times of reusability. The self-copolymerized imprinted layer synthesized with dopamine (DA) and 3-Aminophenylboronic acid (APBA) as double monomers avoided nonspecific binding sites and produced excellent adsorption properties. Taking the gold nanostar (AuNS) with a branch tip "hot spot" structure as the core, the silver-coated AuNS functionalized with the biorecognition element 4-mercaptophenylboronic acid (MPBA) was employed as a surface-enhanced Raman scattering (SERS) nanotag (AuNS@Ag-MPBA) to label TRF, thereby constructing a double boronic acid affinity "sandwich" SERS biosensor (B-MMIPs-TRF-SERS nanotag) for the highly sensitive detection of TRF. The SERS biosensor exhibited a detection limit for TRF of 0.004ng/mL, and its application to spiked serum samples confirmed its reliability and feasibility, demonstrating significant potential for clinical TRF detection. Moreover, the SERS biosensor designed in this study offers advantages in stability, detection speed (40min), and cost efficiency. The portable Raman instrument for SERS detection fulfills the requirements for point-of-care testing.

Multiplex Detection of Foodborne Pathogens using 3D Nanostructure Swab and Deep Learning-Based Classification of Raman Spectra.

Proactive management of foodborne illness requires routine surveillance of foodborne pathogens, which requires developing simple, rapid, and sensitive detection methods. Here, a strategy is presented that enables the detection of multiple foodborne bacteria using a 3D nanostructure swab and deep learning-based Raman signal classification. The nanostructure swab efficiently captures foodborne pathogens, and the portable Raman instrument directly collects the Raman signals of captured bacteria. a deep learning algorithm has been demonstrated, 1D convolutional neural network with binary labeling, achieves superior performance in classifying individual bacterial species. This methodology has been extended to mixed bacterial populations, maintaining accuracy close to 100%. In addition, the gradient-weighted class activation mapping method is used to provide an investigation of the Raman bands for foodborne pathogens. For practical application, blind tests are conducted on contaminated kitchen utensils and foods. The proposed technique is validated by the successful detection of bacterial species from the contaminated surfaces. The use of a 3D nanostructure swab, portable Raman device, and deep learning-based classification provides a powerful tool for rapid identification (≈5 min) of foodborne bacterial species. The detection strategy shows significant potential for reliable food safety monitoring, making a meaningful contribution to public health and the food industry.

Quantitative and rapid detection of nanoplastics labeled by luminescent metal phenolic networks using surface-enhanced Raman scattering

The escalating prevalence of nanoplastics contamination in environmental ecosystems has emerged as a significant health hazard. Conventional analytical methods are suboptimal, hindered by their inefficiency in analyzing nanoplastics at low concentrations and their time-intensive processes. In this context, we have developed an innovative approach that employs luminescent metal–phenolic networks (L-MPNs) coupled with surface-enhanced Raman spectroscopy (SERS) to separate and label nanoplastics, enabling rapid, sensitive and quantitative detection. Our strategy utilizes L-MPNs composed of zirconium ions, tannic acid, and rhodamine B to uniformly label nanoplastics across a spectrum of sizes (50–500 nm) and types (e.g., polystyrene, polymethyl methacrylate, polylactic acid). Rhodamine B (RhB) functions as a Raman reporter within these L-MPNs-based SERS tags, providing the requisite sensitivity for trace measurement of nanoplastics. Moreover, the labeling with L-MPNs aids in the efficient separation of nanoplastics from liquid media. Utilizing a portable Raman instrument, our methodology offers cost-effective, swift, and field-deployable detection capabilities, with excellent sensitivity in nanoplastic analysis and a detection threshold as low as 0.1 μg/mL. Overall, this study proposes a highly promising strategy for the robust and sensitive analysis of a broad spectrum of particle analytes, underscored by the effective labeling performance of L-MPNs when coupled with SERS techniques.

Surface-enhanced Raman scattering detection of thiram and ciprofloxacin using chitosan-silver coated paper substrates.

Fast detection of contaminants of emerging concern (CECs) in water resources is of great environmental interest. Ideally, sustainable materials should be used in water quality monitoring technologies implemented for such purposes. In this regard, the application of bio-based materials aimed at the fabrication of analytical platforms has become of great importance. This research merges both endeavors by exploring the application of chitosan-coated paper, decorated with silver nanoparticles (AgNPs), on surface-enhanced Raman scattering (SERS) spectroscopy studies of two distinct types of CECs dissolved in aqueous samples: an antibiotic (ciprofloxacin) and a pesticide (thiram). Our results indicate the superior SERS performance of biocoated substrates compared to their non-coated paper counterparts. The detection limits achieved for thiram and ciprofloxacin using the biocoated substrates were 0.024 ppm and 7.7 ppm, respectively. The efficient detection of both analytes is interpreted in terms of the role of the biopolymer in promoting AgNPs assemblies that result in local regions of enhanced SERS activity. Taking advantage of these observations, we use confocal Raman microscopy to obtain Raman images of the substrates using ciprofloxacin and thiram as molecular probes. We also demonstrate that these biobased substrates can be promising for on-site analysis when used in conjunction with portable Raman instruments.

Au@Ag Core-Shell Nanoparticles for Colorimetric and Surface-Enhanced Raman-Scattering-Based Multiplex Competitive Lateral Flow Immunoassay for the Simultaneous Detection of Histamine and Parvalbumin in Fish.

Foodborne allergies and illnesses represent a major global health concern. In particular, fish can trigger life-threatening food allergic reactions and poisoning effects, mainly caused by the ingestion of parvalbumin toxin. Additionally, preformed histamine in less-than-fresh fish serves as a toxicological alert. Consequently, the analytical assessment of parvalbumin and histamine levels in fish becomes a critical public health safety measure. The multiplex detection of both analytes has emerged as an important issue. The analytical detection of parvalbumin and histamine requires different assays; while the determination of parvalbumin is commonly carried out by enzyme-linked immunosorbent assay, histamine is analyzed by high-performance liquid chromatography. In this study, we present an approach for multiplexing detection and quantification of trace amounts of parvalbumin and histamine in canned fish. This is achieved through a colorimetric and surface-enhanced Raman-scattering-based competitive lateral flow assay (SERS-LFIA) employing plasmonic nanoparticles. Two distinct SERS nanotags tailored for histamine or β-parvalbumin detection were synthesized. Initially, spherical 50 nm Au@Ag core-shell nanoparticles (Au@Ag NPs) were encoded with either rhodamine B isothiocyanate (RBITC) or malachite green isothiocyanate (MGITC). Subsequently, these nanoparticles were bioconjugated with anti-β-parvalbumin and antihistamine, forming the basis for our detection and quantification methodology. Additionally, our approach demonstrates the use of SERS-LFIA for the sensitive and multiplexed detection of parvalbumin and histamine on a single test line, paving the way for on-site detection employing portable Raman instruments.

From Vibrations to Visions: Raman Spectroscopy’s Impact on Skin Cancer Diagnostics

Raman spectroscopy, a non-invasive diagnostic technique capturing molecular vibrations, offers significant advancements in skin cancer diagnostics. This review delineates the ascent of Raman spectroscopy from classical methodologies to the forefront of modern technology, emphasizing its precision in differentiating between malignant and benign skin tissues. Our study offers a detailed examination of distinct Raman spectroscopic signatures found in skin cancer, concentrating specifically on squamous cell carcinoma, basal cell carcinoma, and melanoma, across both in vitro and in vivo research. The discussion extends to future possibilities, spotlighting enhancements in portable Raman instruments, the adoption of machine learning for spectral data refinement, and the merging of Raman imaging with other diagnostic techniques. The review culminates by contemplating the broader implications of these advancements, suggesting a trajectory that may significantly optimize the accuracy and efficiency of skin cancer diagnostics.

Strong plasmon resonance coupling in micro-extraction SERS membrane for in situ detection of molecular aqueous solutions

In situ surface-enhanced Raman scattering (SERS) detection of trace analytes with high sensitivity, especially in complex aqueous solution environments such as polluted water and biological fluids, remains an urgent task. Herein, a micro-extraction SERS membrane was developed by folding a plasmonic structure composing Au@Ag nanoparticles (NPs) and CuO nanospikes (NSs) via capillary action. In the numerical simulation using COMSOL software, we demonstrated that this structure with a parallel facing state can effectively trap and utilize incident photons to excite multiple plasmon resonance couplings. Moreover, it detected the molecular aqueous solution via capillary action in only 5 min. Benefitting from the dense volumetric hotspots and the simple trace extraction solution method, we utilized the proposed membrane for ultrasensitive in situ SERS detections of microcystin–LR (5 ×10–6 μg/L) toxins in drinking water, adenosine biological fluid (10–10 M), and carcinogenic malachite green (10–11 M) in seafood using a portable Raman instrument. The findings of this study provide promising insights for achieving in situ SERS detection of trace analytes in complex aqueous environments.

Galvanic engineering of interior hotspots in 3D Au/Ag bimetallic SERS nanocavities for ultrasensitive and rapid recognition of phthalate esters

The ecological and health risks of plasticizer accumulation have reached the global public agenda. For early screening of phthalate esters (PAEs), in the present study, we developed ultrasensitive and reliable bimetallic surface-enhanced Raman spectroscopy (SERS) platforms with Ag nanocavities galvanically replaced by Au (Au/AgNCs) at room temperature. A galvanic reaction (GR) coupled with the Kirkendall effect led to a large population of nanoscale hollow regions within 60 s. The interstitials at the bimetallic shells were activated as both interior hotspots for field confinement and diffusion pathways for small target molecules. The GR based on the intrinsic material properties resulted in an Au/AgNC platform with reliable sensing operation, including a relative standard deviation of <10 % at the individual analytic Raman peaks. The qualified and densified interior hotspots of the Au/AgNC platform led to sub-ppm sensitivity (i.e., the average limit of detection of ∼2.1 × 10−2 ppm) for four different PAEs. As a demonstration of the Au/AgNC platform's performance in actual sample environments, the detection and identification of various PAE combinations were also investigated. Subtle differences in the PAEs' spectral characteristics (i.e., peak positions and intensity ratios) were successfully classified using a machine learning algorithm based on a principal component analysis–linear discriminant analysis model. Furthermore, the amount of PAE released from the real sample and evaluated by SERS was consistent with that evaluated by gas chromatography–mass spectrometry. These results indicate that the SERS platforms with Au/AgNC substrates and portable Raman instruments are suitable for on-site safety monitoring of toxic substances in food and the environment.

Ultra-trace SERS detection of cocaine and heroin using bimetallic gold–silver nanostars (BGNS-Ag)

A rapid, in-field, and reliable method for the detection of illegal drugs of abuse in biological fluids without any sample pretreatment would potentially be helpful for law enforcement, drug control officials, and public healthcare. In this study, we presented a cost-effective and highly reproducible solution-based surface-enhanced Raman scattering (SERS) platform utilizing a portable Raman instrument for fast sensitive SERS detection of illegal drugs, such as cocaine, and heroin in human urine without any sample preprocessing. The SERS platform was constructed for the first time by combining the superior SERS enhancement properties of bimetallic silver-coated gold nanostars (BGNS-Ag) and the advantages of suitable alkaline metal salts such as NaI for SERS signal amplification. The effects of the silver thickness of BGNS-Ag and alkaline salts on the SERS performance were investigated in detail; we observed that the maximum SERS enhancement was obtained for BGNS-Ag with the maximum silver thickness (54 ± 5 nm) in presence of NaI salt. Our SERS platform shows ultra-high sensitivity of cocaine and heroin with a limit of detection (LOD) as low as 10 pg/mL for cocaine and 100 pg/mL for heroin, which was 100 times lower than that of the traditional silver nanoparticle-based illegal drug detection. As a demonstration, the platform was further applied to detect cocaine and heroin spiked in human urine without any sample preprocessing achieving a LOD of 100 pg/mL for cocaine and 1 ng/mL for heroin. Overall, our SERS detection platform shows potential for rapid, onsite, ultra-low-cost portable applications for trace detection of illegal drugs and biomarkers.

Comparative assessment of two portable Raman spectrometers for the characterisation of historical natural dye lakes

AbstractThis paper explores the use of portable Raman instruments to characterise natural dye lakes in paint mixtures, as an alternative approach to other Raman techniques (e.g. SERS). Raman spectroscopy has indeed been used extensively to study natural dyes as pure substances or as artistic pigments (dye lakes). However, the examination of these compounds with Raman spectroscopy is particularly challenging because of a strong fluorescence, a relatively weak Raman signal and their occurrence at low concentrations in artefacts. Because of these challenges, the typical way of analysing these materials is through either surface‐enhanced Raman spectroscopy (SERS) or Fourier‐transform (FT) Raman spectroscopy, yet those approaches are not always desirable in art analysis, especially as they often require micro‐sampling. Therefore, this study explores the potential of using commercial mobile instruments, which would open the possibilities for direct in situ analysis. Two dispersive instruments, one using a fibre‐optic probe and a 1064‐nm excitation laser and the other using the subtracted‐shifted excitation (SSE) post‐processing algorithm, have been tested in their feasibility to characterise dye lakes. Raman spectra were acquired from a set of laboratory reproductions of paint mixtures prepared with a chosen set of common lakes, three red (brazilwood, cochineal and madder) and one yellow (weld), mixed with natural proteinaceous and polysaccharide binders. The feasibility has been evaluated, and it is shown that these lakes produce a detectable Raman signal, in spite of the strong interference of the painting support (parchment) and that the two instruments provide significantly different information.

Solution-Based Ultra-Sensitive Surface-Enhanced Raman Scattering Detection of the Toxin Bacterial Biomarker Pyocyanin in Biological Fluids Using Sharp-Branched Gold Nanostars.

There is a critical need for sensitive rapid point-of-care detection of bacterial infection biomarkers in complex biological fluids with minimal sample preparation, which can improve early-stage diagnosis and prevent several bacterial infections and fatal diseases. A solution-based surface-enhanced Raman scattering (SERS) detection platform has long been sought after for low cost, rapid, and on-site detection of analyte molecules, but current methods still exhibit poor sensitivity. In this study, we have tuned the morphology of the surfactant-free gold nanostars (GNSs) to achieve sharp protruding spikes for maximum SERS enhancement. We have controlled the GNS spike morphologies and optimized SERS performance in the solution phase using para-mercaptobenzoic acid as an SERS probe. To illustrate the potential for point-of-care applications, we have utilized a portable Raman instrument for measurements. For pathogenic agent sensing applications, we demonstrated rapid and sensitive detection of the toxin biomarker pyocyanin (PYO) used as the bacterial biomarker model system. Pyocyanin is a toxic compound produced and secreted by the common water-borne Gram-negative bacterium Pseudomonas aeruginosa, a pathogen known for advanced antibiotic resistance and association with serious diseases such as ventilator-associated pneumonia and cystic fibrosis. The limit of detection (LOD) achieved for PYO was 0.05 nM using sharp branched GNSs. Furthermore, as a proof of strategy, this SERS detection of PYO was performed directly in drinking water, human saliva, and human urine without any sample treatment pre-purification, achieving an LOD of 0.05 nM for drinking water and 0.4 nM for human saliva and urine. This work provides a proof-of-principle demonstration for the high sensitivity detection of the bacterial toxin biomarker with minimal sample preparation: the "mix and detect" detection of the GNS platform is simple, robust, and rapid, taking only 1-2 min for each measurement. Overall, our SERS detection platform shows great potential for point-of-need sensing and point-of-care diagnostics in biological fluids.

TiO2 compact layer induced charge transfer enhancement in a three-dimensional TiO2-Ag array SERS substrate for quantitative and multiplex analysis.

A highly sensitive and uniform surface-enhanced Raman scattering (SERS) substrate is the guarantee for reliable quantitative analysis. Herein, a three-dimensional TiO2-Ag SERS substrate was prepared by growing a TiO2 nanorods (NRs) array on a TiO2 compact layer (c-TiO2), followed by modification with Ag nanoparticles (AgNPs). The synergy between the c-TiO2, semiconductor TiO2 NRs and the plasmonic AgNPs collaboratively endowed it with high sensitivity, in which c-TiO2 effectively blocked the recombination of electrons and holes, and the charge transfer enhancement contributed 10-fold improvement over that without the c-TiO2 substrate. Besides the high sensitivity, the TiO2-Ag hybrid array SERS substrate also showed quantitative and multi-component detecting capability. The limit of detection (LOD) for crystal violet (CV) was determined to be 10-9 M even with a portable Raman instrument. The TiO2-Ag composite structure was extended to detect organic pesticides (thiram, triazophos and fonofos), and the LODs for thiram, triazophos and fonofos were measured to be 10-7 M, 10-7 M and 10-6 M, respectively. In addition, the realistic simulation detecting pesticide residues for a real sample of dendrobium was demonstrated. The sensitive, quantitative and multiplex analysis of the TiO2-Ag hybrid array substrate indicated its great potential in the rapid detection of pesticide residues in real samples.

A hybrid plasmonic nanoprobe using polyvinylpyrrolidone-capped bimetallic silver-gold nanostars for highly sensitive and reproducible solution-based SERS sensing.

Practical solution-based assays using surface-enhanced Raman spectroscopy (SERS) with portable instrumentation are currently of particular interest for rapid, efficient, and low-cost detection of analytes. However, current assays still have limited applicability due to their poor sensitivity and reproducibility. Herein, we demonstrate highly stable polyvinylpyrrolidone (PVP)-capped bimetallic silver-coated gold nanostars (BGNS-Ag-PVP) as a solution-based SERS nanoprobe that is capable of producing a strong, uniform, and reproducible SERS signal using a portable Raman instrument. The developed hybrid BGNS-Ag-PVP nanostructure shows tunable optical properties with improved SERS sensitivity and reproducibility as compared to gold nanostars. We have synthesized bimetallic nanoprobes BGNS-Ag-PVP having three different silvers, referred to as BGNS-Ag-PVP-1, BGNS-Ag-PVP-2, and BGNS-Ag-PVP-3. The SERS performance of BGNS-Ag-PVP was studied using methylene blue (Meb) as a probe molecule, and we achieved a detection limit of up to 10 nM indicating the high sensitivity of the solution-based SERS platform. The application of such bimetallic nanoparticles is demonstrated via the sensitive detection of the antithyroid drug methimazole (Mz) used as a model analyte system. We have achieved a detection limit of 1 nM for Mz spiked with human urine indicating three orders of magnitude lower than previously reported solution-based SERS detection methods. Furthermore, the SERS performance was reproducible over 3 months indicating excellent stability and repeatability. The result illustrates the potential of this solution-based SERS detection platform as a promising sensing tool for analytes such as illicit drugs, and biomarkers that have affinity to bind on nanoprobes.

Enhancement of Magnetic Surface-Enhanced Raman Scattering Detection by Tailoring Fe3O4@Au Nanorod Shell Thickness and Its Application in the On-site Detection of Antibiotics in Water.

Surface-enhanced Raman scattering (SERS) has become a promising method for the detection of contaminants or biomolecules in aqueous media. The low interference of water, the unique spectral fingerprint, and the development of portable and handheld equipment for in situ measurements underpin its predominance among other spectroscopic techniques. Among the SERS nanoparticle substrates, those composed of plasmonic and magnetic components are prominent examples of versatility and efficiency. These substrates harness the ability to capture the target analyte, concentrate it, and generate unique hotspots for superior enhancement. Here, we have evaluated the use of gold-coated magnetite nanorods as a novel multifunctional magnetic-plasmonic SERS substrate. The nanostructures were synthesized starting from core-satellite structures. A series of variants with different degrees of Au coatings were then prepared by seed-mediated growth of gold, from core-satellite structures to core-shell with partial and complete shells. All of them were tested, using a portable Raman instrument, with the model molecule 4-mercaptobenzoic acid in colloidal suspension and after magnetic separation. Experimental results were compared with the boundary element method to establish the mechanism of Raman enhancement. The results show a quick magnetic separation of the nanoparticles and excellent Raman enhancement for all the nanoparticles both in dispersion and magnetically concentrated with limits of detection up to the nM range (∼50 nM) and a quantitative calibration curve. The nanostructures were then tested for the sensing of the antibiotic ciprofloxacin, highly relevant in preventing antibiotic contaminants in water reservoirs and drug monitoring, showing that ciprofloxacin can be detected using a portable Raman instrument at a concentration as low as 100 nM in a few minutes, which makes it highly relevant in practical point-of-care devices and in situ use.

Portable Raman Instrumentation with a Digital Micromirror for the Analysis of Mixtures

A portable Raman spectrometer cannot complete homogeneity analysis of mixtures due to its low accuracy which limits the applications. Here a Raman sensing system based on a digital micromirror device (DMD) is presented. Combined with a digital micromirror, the fixed-focus measurement has been changed with high-frequency mobile measurements which expands the applications. Using fast scanning detection by the digital micromirror, the usual fixed focus (generally 50 μm diameter) is upgraded to high-speed surface scanning. The side length of the scanning rectangle may set according to the application or sample size. The point-by-point scanning function further improves the analysis of mixtures through multi-point measurement of samples in a specific area.

Improvement of SERS signal measured by portable Raman instrument using random sampling technique

In recent years, portable Raman spectrometers and commercialized surface-enhanced Raman scattering (SERS) substrates have become increasingly popular. They have turned out to be great tools for both substance detection, identification, and analysis on-site. This work addresses the technique to collect proper Raman spectra using SERS substrates and portable Raman spectrometers. We propose a random sampling technique that gives representative and high-quality spectra with high intensity and good resolution. This technique was tested on a home-built portable Raman spectrometer and SERS substrates based on metal film over nano-sphere (MFON) structure. Experimental results showed that peaks of Raman spectrum collected using random sampling technique are significantly narrower than those of spectra measured in conventional one and prevent samples and SERS substrates from photoinduced degradation. Potentially, this method can promote quantitative SERS and chemical trace analysis using portable Raman spectrometers.

Towards quantitative point of care detection using SERS lateral flow immunoassays

The rapid detection of biomolecules in a point of care (POC) setting is very important for diagnostic purposes. A platform which can provide this, whilst still being low cost and simple to use, is paper-based lateral flow immunoassays (LFIA). LFIA combine immunology and chromatography to detect a target by forming an immunocomplex with a label which traps them in a test zone. Qualitative analysis can be performed using the naked eye whilst quantitative analysis takes place by measuring the optical signal provided by the label at the test zone. There are numerous detection methods available; however, many suffer from low sensitivity and lack of multiplexing capabilities or are poor at providing POC quantitative analysis. An attractive method to overcome this is to use nanoparticles coated in Raman reporters as the labelled species and to analyse test zones using surface-enhanced Raman scattering (SERS). Due to the wide variety of metal nanoparticles, Raman reporter and laser excitations that are available, SERS-based LFIA have been adapted to identify and quantify multiple targets at once. Large Raman microscopes combined with long mapping times have limited the platform to the lab; however, by transferring the analysis to portable Raman instruments, rapid and quantitative measurements can be taken at the POC without any loss in sensitivity. Portable or handheld SERS-LFIA platforms can therefore be used anywhere, from modern clinics to remote and resource-poor settings. This review will present an overview of SERS-based LFIA platforms and the major recent advancements in multiplexing and portable and handheld detection with an outlook on the future of the platform.Graphical abstract

Ultra-high SERS detection of consumable coloring agents using plasmonic gold nanostars with high aspect-ratio spikes.

Solution-based SERS detection by using a portable Raman instrument has emerged as an important tool due to its simplicity, and flexibility for rapid and on-site screening of analyte molecules. However, this method has several shortcomings, including poor sensitivity especially for weak-affinity analyte molecules, where there is no close contact between the plasmonic metal surface and analyte molecule. Examples of weak-affinity molecules include pigment molecules that are commonly used as a consumable coloring agent, such as allura red (AR), and sunset yellow (SY). As high consumption of colorant agents has been shown to cause adverse effects on human health, there is a strong need to develop a simple and practical sensing system with high sensitivity for these agents. Here we present a novel, highly sensitive solution-based SERS detection method for AR, and SY by using CTAC capped gold nanostars (GNS) having different aspect ratios (GNS-2, GNS-4, and GNS-5) without utilizing any aggregating agents which can enhance SERS signal however it reduces batch to batch reproducibility. The influence of the aspect ratio of GNS on SERS properties was investigated. We have achieved a limit of detection (LOD) of AR and SY as low as 0.5 and 1 ppb, respectively by using GNS-5 with the advantages of minimal sample preparation by just mixing the analyte solution into a well plate containing GNS solution. In addition, excellent colloidal stability and reproducibility have further enhanced the applicability in real-world samples. Overall, our results evidence that the solution-based SERS detection platform using high aspect-ratio GNS can be applied for practical application to detect pigment molecules in real samples with satisfactory results.

A portable SERS sensing platform for the multiplex identification and quantification of pesticide residues on plant leaves

The synergetic effect of the ZnO@Co3O4 and the plasmonic AgNPs together with the molecular preconcentration enabled it to ultrasensitively and quantitatively detect organophosphorus pesticides on real samples on a portable Raman instrument.

Implementing machine learning for the identification and classification of compound and mixtures in portable Raman instruments

Portable Raman instruments provide quick, nondestructive analysis of organic and inorganic compounds, making it widely applicable in various disciplines. However, the instrument’s accuracy when analyzing pure, or multiple component mixtures is still an aspect that needs improvement. This study explored machine learning algorithms to classify single compounds, binary, ternary, and quaternary mixtures by the compound name, and the compound’s class, using seized drugs and common diluents as a model. The accuracies were ≥ 93% for most pure, binary mixtures, and quaternary mixtures algorithms. Therefore, incorporating machine learning algorithms in portable instruments, can improve the detection of unknown substances with high accuracies.