Low-Cost Label-Free Biosensing Bimetallic Cellulose Strip with SILAR-Synthesized Silver Core–Gold Shell Nanoparticle Structures

Multi-channel surface-enhanced Raman spectroscopy (SERS) platform for pollutant detection in water fabricated on polydimethylsiloxane

A miniature multi-channel surface-enhanced Raman scattering (SERS) sensor based on polydimethylsiloxane (PDMS) is constructed to achieve rapid delivery of polluted water and specific identification of multiple components. Hg2+, organic pollutants, and sodium nitrite are successfully identified by the multi-channel SERS sensor using Cy5, cyclodextrin, and urea in the corresponding detection area. This multi-channel sensor exhibits excellent sensitivity and specificity, with detection limits of 3.2 × 10-10 M for Hg2+, 1.0 × 10-8 M for aniline, 6.9 × 10-9 M for diphenylamine, 9.1 × 10-8 M for PCB-77, and 7.5 × 10-9 M for pyrene, and 5.0 × 10-7 M for sodium nitrite. Compared with traditional analysis techniques, this method exhibited excellent recovery for the water pollutants ranging from 82.1 to 115.8%. The PDMS-based microchannel allows for simultaneous and rapid identification of multiple environmental pollutants, offering a portable detection method for emergency testing of environmental pollutants and routine determination of water pollutants.

Applications of Metals and Metal Compounds in Improving the Sensitivity of Microfluidic Biosensors - A Review

The enhancement of detection sensitivity in microfluidic sensors has been a continuously explored field. Initially, many strategies for sensitivity improvement involved introducing enzyme cascade reactions, but enzyme-based reactions posed challenges in terms of cost, stability, and storage. Therefore, there is an urgent need to explore enzyme-free cascade amplification methods, which are crucial for expanding the application range and improving detection stability. Metal or metal compound nanomaterials have gained great attention in the exploitation of microfluidic sensors due to their ease of preparation, storage, and lower cost. The unique physical properties of metallic nanomaterials, including surface plasmon resonance, surface-enhanced Raman scattering, metal-enhanced fluorescence, and surface-enhanced infrared absorption, contribute significantly to enhancing detection capabilities. The metal-based catalytic nanomaterials, exemplified by Fe3O4 nanoparticles and metal-organic frameworks, are considered viable alternatives to biological enzymes due to their excellent performance. Herein, we provide a detailed overview of the applications of metals and metal compounds in improving the sensitivity of microfluidic biosensors. This review not only highlights the current developments but also critically analyzes the challenges encountered in this field. Furthermore, it outlines potential directions for future research, contributing to the ongoing development of microfluidic biosensors with improved detection sensitivity.

Serum analysis based on SERS combined with 2D convolutional neural network and Gramian angular field for breast cancer screening

Breast cancer is a significant cause of death among women worldwide. It is crucial to quickly and accurately diagnose breast cancer in order to reduce mortality rates. While traditional diagnostic techniques for medical imaging and pathology samples have been commonly used in breast cancer screening, they still have certain limitations. Surface-enhanced Raman spectroscopy (SERS) is a fast, highly sensitive and user-friendly method that is often combined with deep learning techniques like convolutional neural networks. This combination helps identify unique molecular spectral features, also known as "fingerprint", in biological samples such as serum. Ultimately, this approach is able to accurately screen for cancer. The Gramian angular field (GAF) algorithm can convert one-dimensional (1D) time series into two-dimensional (2D) images. These images can be used for data visualization, pattern recognition and machine learning tasks. In this study, 640 serum SERS from breast cancer patients and healthy volunteers were converted into 2D spectral images by Gramian angular field (GAF) technique. These images were then used to train and test a two-dimensional convolutional neural network-GAF (2D-CNN-GAF) model for breast cancer classification. We compared the performance of the 2D-CNN-GAF model with other methods, including one-dimensional convolutional neural network (1D-CNN), support vector machine (SVM), K-nearest neighbor (KNN) and principal component analysis-linear discriminant analysis (PCA-LDA), using various evaluation metrics such as accuracy, precision, sensitivity, F1-score, receiver operating characteristic (ROC) curve and area under curve (AUC) value. The results showed that the 2D-CNN model outperformed the traditional models, achieving an AUC value of 0.9884, an accuracy of 98.13%, sensitivity of 98.65% and specificity of 97.67% for breast cancer classification. In this study, we used conventional nano-silver sol as the SERS-enhanced substrate and a portable laser Raman spectrometer to obtain the serum SERS data. The 2D-CNN-GAF model demonstrated accurate and automatic classification of breast cancer patients and healthy volunteers. The method does not require augmentation and preprocessing of spectral data, simplifying the processing steps of spectral data. This method has great potential for accurate breast cancer screening and also provides a useful reference in more types of cancer classification and automatic screening.

Necklace-like Te-Au reticula platform with three dimensional hotspots Surface-Enhanced Raman Scattering (SERS) sensor for food hazards analysis

In this work, we combined three-dimensional (3D) necklace-like Te-Au reticula as novel surface-enhanced Raman scattering (SERS) active substrates with oxidation-reduction displacement reactions to construct a molecular machine for SERS detection. The structurally tunable 3D necklace-like spatial structures generated more active 'hot spots' and thus enhanced the sensitivity of SERS signals. Besides, layers of ultrathin nanowires showed high sequence dependence that ensure the repeatability and abundant hotspots at interparticle gaps and guarantee the high SERS performance of the substrate. A better-localized surface plasmon resonance (LSPR) effect of the sensor was verified by finite-difference time-domain (FDTD) analysis in both Raman intensities and electromagnetic field distributions compared to the citrate-stabilized AuNPs and CTAB-protected AuNRs. The proposed strategy can also serve as a universally amplified and sensitive detection platform for monitoring different molecules, thus achieving an amplification detection of 3,3'-diethylthiatricarbocyanine iodide (DTTCI) are 1 nM and R6G with a low limit of detection of 1 pM. Especially, the intensity of the main vibration of R6G from 30 spots of SERS data with excellent reproducibility (relative standard deviation of 6.25 %). High selectivity and accuracy of the SERS sensor were proved by practical analysis melamine (MM) in milk with a linear calibration curve (R2 = 0.9962) and a limit of detection of 0.75 mg/kg. Our research provides a new perspective to construct 3D SERS sensor from integrated structural design.

Bacterial cellulose loaded with silver nanoparticles as a flexible, stable and sensitive SERS-active substrate for detection of the shellfish toxin DTX-1

Diarrheal shellfish toxins are considered one of the most lethal red tide algae toxins in the worldwide. In this work, we propose an Ag NPs-loaded bacterial cellulose membrane (BCM) surface-enhanced Raman scattering (SERS) sensor based on an aptamer (Apt) for the ultrasensitive detection of dinophysistoxin (DTX-1), a type of diarrheal shellfish toxin. During drying, Ag NPs can be further densified on "gel-like" BCM to form high-density SERS "hot spots". We developed the "Apt-SH@Ag NPs@BCM" SERS sensor and used the competition of DTX-1 and complementary base (Cob) in the process of base complementary pairing to achieve SERS detection of DTX-1, with a minimum detection limit of 9.5 × 10^-10 mol/L. Sample assays showed DTX-1 recovery rates ranging from 95.8% and 108.2% and the detection results were comparable to those obtained by LC-MS. Therefore, this work holds great potential for detecting of toxic substances in shellfish products, especially for the oyster (portuguese oyster) and mussel (blue mussel).

Constructing a Ring-like Self-Aggregation SERS Sensor with the Coffee Ring Effect for Ultrasensitive Detection and Photocatalytic Degradation of the Herbicides Paraquat and Diquat

A strategy for building ring-like deposit surface-enhanced Raman scattering (SERS) sensors with the coffee ring effect through the functional modification of the silica nanoparticle surface encapsulated by free-tagged Ag nanoparticles is addressed along with their applications in the SERS-based detection and degradation of target species, including paraquat, diquat, and their free radicals. The nanogap formed by two interparticles with SERS hotspots provides a gigantic amplification signal for the Raman scattering intensity of the analyte molecule located approximately at the hotspots. The enhanced Raman spectrum signals of these target analytes were achieved through the hotspot region of the surface plasmon resonance (SPR) located on the embankment formed by self-aggregation of SiO₂@Ag nanoparticles due to the coffee ring effect. Meanwhile, the intrinsic properties of Ag nanoparticles embedded onto the silica surface were applied to photocatalytically degrade the target analytes by harvesting energy from sunlight. The SERS sensor detected the analytes down to 10^-9 M in the aqueous solution.

A biosensing method for the direct serological detection of liver diseases by integrating a SERS-based sensor and a CNN classifier

Direct serological detection, due to its clinical facility and testing economy, affords prominent clinical values to the early detection of cancer. Surface-enhanced Raman spectroscopy (SERS)-based sensors have shown great promise in realizing this form of detection. Detecting liver cancer early with such a form, especially in terms of monitoring the pathogenic progression from hepatic inflammations to cancer, is the most effective clinical path to reducing the mortality rate. However, the methodology investigation for this purpose remains a formidable challenge. We fabricated a SERS-based sensor, consisting of Au-Ag nanocomplex-decorated ZnO nanopillars on paper. The sensor has an analytic enhancement factor of 1.02 × 107, which is enough to sense the biomolecular information of liver diseases through direct serum SERS analysis. A convolutional neural network (CNN) classifier for recognizing serum SERS spectra was constructed by deep learning. Integrating this sensor with the CNN, we established an intelligent biosensing method and realized direct serological detection of liver diseases within 1 min. As a proof-of-concept, the method achieved a prediction accuracy of 97.78% on an independent test dataset randomly sampled from 30 normal controls, 30 hepatocellular carcinoma (HCC) cases, and 30 hepatitis B (HB) patients. The results suggest this method can be developed for detecting liver diseases clinically and is worthy of exploration as a means of liver cancer surveillance. The presented sensor holds potential for clinical translation to the direct serological detection of diseases.

Label-free breast cancer detection using fiber probe-based Raman spectrochemical biomarker-dominated profiles extracted from a mixture analysis algorithm

We report the development of a label-free, simple, and high efficiency breast cancer detection platform with multimodal biomarker analytic algorithms on a portable 785 nm Raman setup with an endoscopic Raman-lensed fiber optic probe. We propose a multimodal biomarker extraction algorithm (PCMA) implemented by combining a multivariate statistics principal component analysis (PCA) algorithm and a multivariate curve resolution-alternating least squares (MCR-ALS) computational model for extraction of the biomarker information hidden in Raman spectrochemical data. We show that the six Raman spectrochemical peaks at 1009, 1270, 1305/1443, 1658, and 1750 cm-1 assigned to phenylalanine, amide III in proteins, CH2 deformation in lipids, amide I in proteins, and carbonyl, respectively, can be used as a biomarker for breast cancer diagnosis using the biomarker-dominated PCMA spectrochemical spectra of breast tissues. From 20 human breast tissues, the PCMA-linear discriminant analysis (PCMA-LDA) identification method achieved high classification performance with a sensitivity and specificity >99% along with an improvement of approximately 4.5% compared to the performance without the PCMA mixture analysis algorithm. Our label-free breast cancer detection method has the potential for clinical application to diagnose breast cancer in real-time during surgery.

Cellulose-Based Flexible Functional Materials for Emerging Intelligent Electronics

There is currently enormous and growing demand for flexible electronics for personalized mobile equipment, human-machine interface units, wearable medical-healthcare systems, and bionic intelligent robots. Cellulose is a well-known natural biopolymer that has multiple advantages including low cost, renewability, easy processability, and biodegradability, as well as appealing mechanical performance, dielectricity, piezoelectricity, and convertibility. Because of its multiple merits, cellulose is frequently used as a substrate, binder, dielectric layer, gel electrolyte, and derived carbon material for flexible electronic devices. Leveraging the advantages of cellulose to design advanced functional materials will have a significant impact on portable intelligent electronics. Herein, the unique molecular structure and nanostructures (nanocrystals, nanofibers, nanosheets, etc.) of cellulose are briefly introduced, the structure-property-application relationships of cellulosic materials summarized, and the processing technologies for fabricating cellulose-based flexible electronics considered. The focus then turns to the recent advances of cellulose-based functional materials toward emerging intelligent electronic devices including flexible sensors, optoelectronic devices, field-effect transistors, nanogenerators, electrochemical energy storage devices, biomimetic electronic skins, and biological detection devices. Finally, an outlook of the potential challenges and future prospects for developing cellulose-based wearable devices and bioelectronic systems is presented.

Au@ZIF-8 SERS paper for food spoilage detection

Putrescine and cadaverine are important volatile indicators for the evaluation of food spoilage. In this study, a metal-organic framework (MOF)-coated surface-enhanced Raman scattering (SERS) paper platform for the detection of putrescine and cadaverine is developed. Au@ zeolite imidazolate framework-8 (ZIF-8) SERS paper is fabricated by the coating of ZIF-8 layer on a Au nanoparticle-impregnated paper that is prepared by dry plasma reduction. The Au@ZIF-8 SERS paper is characterized by scanning electron microscope, energy-dispersive X-ray spectroscopy, X-ray diffraction, and N₂ sorption isotherm. The ZIF-8 layer enables the accumulation of gaseous molecules and also provides enhancement of SERS signals. The fluorescence, SERS, and simulation results prove the improved detection ability of the Au@ZIF-8 platform for the volatile molecules. For the selective detection of putrescine and cadaverine, the Au@ZIF-8 SERS paper is functionalized with 4-mercatobenzaldehyde (4-MBA). The 4-MBA molecule acts as a Raman reporter and also a specific receptor for the volatile amine molecules. Using the intensity ratiometric detection of 4-MBA-functionalized Au@ZIF-8 SERS paper, putrescine and cadaverine are quantitatively detected with detection limits of 76.99 and 115.88 parts per billion, respectively. Furthermore, the detection of volatile amine molecules released from spoiled salmon, chicken, beef, and pork samples is demonstrated. It is anticipated that the MOF-coated SERS paper platforms will be applicable not only in food safety but other applications including disease diagnosis and environmental monitoring.

A facile wet-chemistry approach to engineer an Au-based SERS substrate and enhance sensitivity down to ppb-level detection

A two-dimensional flexible surface-enhanced Raman scattering (SERS) filter substrate provides an alternative strategy for the highly sensitive portable detection of various toxic molecules and biomaterials. Herein, we developed a solid-liquid interfacial reduction reaction to post-engineer a solid Au nanostructure surface on filter paper to improve the SERS effect. Among four reductants (ascorbic acid, l-dopamine, hydroquinone (HQ), and formaldehyde), HQ possessed a larger oxidation overpotential and facilitated homogeneous growth, forming small Au branch-structure nanoparticles from HAuCl4 solution. Due to the surface effect by exposing abundant -OH groups and intrinsic aromatic rings from TNA/HQ on nano-gold, the SERS effect on positively charged analytes near the plasmonic Au surface was enhanced, while forming a protective layer against severe water interruption. The resulting SERS substrate with branched nano-gold provided several SERS-enhanced sites, increased the enhancement by more than 6 times compared to original SERS sensing, and displayed a 1.4-7.4 × 105 analytical enhancement factor, which leads to a limit of detection down to several ppb. Less than 6% of deviation in the SERS intensity at different sensing sites was observed. We successfully improved the primary SERS substrate using a high overpotential reductant. Owing to its soft and flexible properties, the paper-based SERS substrate can be used conveniently in different sizes, pasting on curved materials, detecting additives in fish, and preventing the coffee-ring effect, showing high practicality and potential commercial value in the future.

SERS Platform Based on Bimetallic Au-Ag Nanowires-Decorated Filter Paper for Rapid Detection of miR-196ain Lung Cancer Patients Serum

Detecting microRNA (miRNA) biomarkers expression is of great significance for the diagnosis and treatment of lung cancer. Surface-enhanced Raman scattering (SERS) has achieved microRNA sensing for the diagnosis of primary liver cancers. In this work, we developed a SERS technology for the rapid detection of lung cancers-related miRNA (miR-196a) using bimetallic Au-Ag nanowire (AgNW@AuNPs) substrates coupled with the target hairpin DNA. The finite-difference time-domain simulation proved that a large number of “hot spots” were generated between the AgNW and AuNPs, which resulted in a huge enhancement of the signal of Raman reporters. Filter paper treated by hexadecenyl succinic anhydride hydrophobic and modified with AgNWs@AuNPs was used as capturing substrate. The detection limits of miR-196a in PBS and serum were as low as 96.58 aM and 130 aM, respectively. Studies on nonspecific sequence and single-base mismatch of miRNA demonstrated that SERS-based platform was highly selective, excellent uniform, and reproducible. Finally, the platform was used to show that the miR-196a expression in the serum of lung cancer patients was much higher than that in healthy people. The detection results indicated that the SERS platform had potential applications in cancer diagnosis and might be a viable alternative to the conventional miRNA detection method, the real-time polymerase chain reaction (RT-PCR) technology.

A Paper-Based Device for Ultrasensitive, Colorimetric Phosphate Detection in Seawater

High concentrations of certain nutrients, including phosphate, are known to lead to undesired algal growth and low dissolved oxygen levels, creating deadly conditions for organisms in marine ecosystems. The rapid and robust detection of these nutrients using a colorimetric, paper-based system that can be applied on-site is of high interest to individuals monitoring marine environments and others affected by marine ecosystem health. Several techniques for detecting phosphate have been reported previously, yet these techniques often suffer from high detection limits, reagent instability, and the need of the user to handle toxic reagents. In order to develop improved phosphate detection methods, the commonly used molybdenum blue reagents were incorporated into a paper-based, colorimetric detection system. This system benefited from improved stabilization of the molybdenum blue reagent as well as minimal user contact with toxic reagents. The colorimetric readout from the paper-based devices was analyzed and quantified using RGB analyses (via ImageJ), and resulted in the detection of phosphate at detection limits between 1.3 and 2.8 ppm in various aqueous media, including real seawater.

Highly reliable SERS substrate based on plasmonic hybrid coupling between gold nanoislands and periodic nanopillar arrays

To improve both sensitivity and reliability, a hybrid SERS substrate of combining gold nanoislands (GNI) with periodic MgF₂ nanopillar arrays was successfully developed. SERS detection performance of the proposed substrates was evaluated in terms of enhancement effect, signal-to-noise ratio (SNR), linearity, reproducibility and repeatability, and compared with the performance of a conventional SERS substrate based on GNI. Experimental and simulation results presented that significant improvement of SERS intensity and SNR by more than 3 times and a notable reduction in relative standard deviation were obtained. We hope that the suggested SERS platform with unique advantages in sensitivity and reliability could be extended to point-of-care detection of a variety of biomolecular reactions.

Glowing gold nanoparticle coating: restoring the lost property from bulk gold

The unique electronic, optical, and catalytic properties of AuNPs caused by localized surface plasmon resonance (LSPR) have attracted many scientists, but the LSPR diminishes the captivating luster of bulk gold. An exciting challenge is the fabrication of golden-colored AuNPs, but a decisive factor for controlling the absorption/reflection of AuNPs remains elusive. We now propose a simple and versatile method for the fabrication of glowing AuNPs to restore the "lost golden color" of AuNPs in combination with the deposition of AuNPs on a cellulose filter or a PET/cotton fabric by the successive ionic layer adsorption and reaction (SILAR) method and simple pencil drawing. The obtained materials exhibited the glowing golden-color on the pencil-drawn surface and common red and blue colors on the other parts. Surprisingly, the golden-colored AuNPs still maintain a catalytic activity different from that of bulk gold and could be used as a catalyst for the reduction of p-nitrophenol, pendimethalin or 2,4-dinitrophenol in the presence of NaBH₄. We believe that the re-endowment of such a property characteristic of bulk gold into gold nanomaterials would lead to further advancement in the arts and culture as well as electronics, optics, and catalysis.

Nanoparticle-based Point of Care Immunoassays for in vitro Biomedical Diagnostics

In resource-limited settings, the availability of medical practitioners and early diagnostic facilities are inadequate relative to the population size and disease burden. To address cost and delayed time issues in diagnostics, strip-based immunoassays, e.g. dipstick, lateral flow assay (LFA) and microfluidic paper-based analytical devices (microPADs), have emerged as promising alternatives to conventional diagnostic approaches. These assays rely on chromogenic agents to detect disease biomarkers. However, limited specificity and sensitivity have motivated scientists to improve the efficiency of these assays by conjugating chromogenic agents with nanoparticles for enhanced qualitative and quantitative output. Various nanomaterials, which include metallic, magnetic and luminescent nanoparticles, are being used in the fabrication of biosensors to detect and quantify biomolecules and disease biomarkers. This review discusses some of the principles and applications of such nanoparticle-based point of care biosensors in biomedical diagnosis.

Paper-based SERS analysis with smartphones as Raman spectral analyzers

We present a smartphone-based portable Raman spectrometer, which has a friendly human–machine interface, easy operation, rapid response time, and most importantly a very small size for on-site use.

Paper-Based Surface-Enhanced Raman Spectroscopy for Diagnosing Prenatal Diseases in Women

We report the development of a surface-enhanced Raman spectroscopy sensor chip by decorating gold nanoparticles (AuNPs) on ZnO nanorod (ZnO NR) arrays vertically grown on cellulose paper (C). We show that these chips can enhance the Raman signal by 1.25 × 10⁷ with an excellent reproducibility of <6%. We show that we can measure trace amounts of human amniotic fluids of patients with subclinical intra-amniotic infection (IAI) and preterm delivery (PTD) using the chip in combination with a multivariate statistics-derived machine-learning-trained bioclassification method. We can detect the presence of prenatal diseases and identify the types of diseases from amniotic fluids with >92% clinical sensitivity and specificity. Our technology has the potential to be used for the early detection of prenatal diseases and can be adapted for point-of-care applications.

A Graphene-Silver Nanoparticle-Silicon Sandwich SERS Chip for Quantitative Detection of Molecules and Capture, Discrimination, and Inactivation of Bacteria

There currently exists increasing concerns on the development of a kind of high-performance SERS platform, which is suitable for sensing applications ranging from the molecular to cellular (e.g., bacteria) level. Herein, we develop a novel kind of universal SERS chip, made of graphene (G)-silver nanoparticle (AgNP)-silicon (Si) sandwich nanohybrids (G@AgNPs@Si), in which AgNPs are in situ grown on a silicon wafer through hydrofluoric acid-etching-assisted chemical reduction, followed by coating with single-layer graphene via a polymer-protective etching method. The resultant chip features a strong, stable, reproducible surface-enhanced Raman scattering (SERS) effect and reliable quantitative capability. By virtues of these merits, the G@AgNPs@Si platform is capable for not only molecular detection and quantification but also cellular analysis in real systems. As a proof-of-concept application, the chip allows ultrahigh sensitive and reliable detection of adenosine triphosphate (ATP), with a detection limit of ∼1 pM. In addition, the chip, serving as a novel multifunctional platform, enables simultaneous capture, discrimination, and inactivation of bacteria. Typically, the bacterial capture efficiency is 54% at 10⁸ CFU mL^-1 bacteria, and the antibacterial rate reaches 93% after 24 h of treatment. Of particular note, Escherichia coli and Staphylococcus aureus spiked into blood can be readily distinguished via the chip, suggesting its high potential for clinical applications.

Label-free monitoring of inflammatory tissue conditions using a carrageenan-induced acute inflammation rat model

Although the confirmation of inflammatory changes within tissues at the onset of various diseases is critical for the early detection of disease and selection of appropriate treatment, most therapies are based on complex and time-consuming diagnostic procedures. Raman spectroscopy has the ability to provide non-invasive, real-time, chemical bonding analysis through the inelastic scattering of photons. In this study, we evaluate the feasibility of Raman spectroscopy as a new, easy, fast, and accurate diagnostic method to support diagnostic decisions. The molecular changes in carrageenan-induced acute inflammation rat tissues were assessed by Raman spectroscopy. Volumes of 0 (control), 100, 150, and 200 µL of 1% carrageenan were administered to rat hind paws to control the degree of inflammation. The prominent peaks at [1,062, 1,131] cm^-1 and [2,847, 2,881] cm^-1 were selected as characteristic measurements corresponding to the C-C stretching vibrational modes and the symmetric and asymmetric C-H (CH₂ ) stretching vibrational modes, respectively. Principal component analysis of the inflammatory Raman spectra enabled graphical representation of the degree of inflammation through principal component loading profiles of inflammatory tissues on a two-dimensional plot. Therefore, Raman spectroscopy with multivariate statistical analysis represents a promising method for detecting biomolecular responses based on different types of inflammatory tissues.