Centrifugal Extraction-Assisted Fiber-Enhanced Raman Spectroscopy for Online Detection of Trace Furfural in Oil Power Equipment.

Oil-paper insulated equipment is integral in power conversion and supports low-loss electricity transport. As a characteristic byproduct of the oil-paper insulation system, the realization of efficient detection of furfural in oil is crucial to the safe operation of the power grid. We proposed a novel approach using dual-enhanced Raman spectroscopy for sensing trace liquid components. This method employs a centrifugal extractor to separate and enrich the targeted components, achieving selective enhancement. The optimal phase ratio was determined to be 30:1. A liquid-core fiber was used to optimize the laser transmission efficiency and Raman signal collection efficiency, resulting in a nonselective signal enhancement of 44.86. It also investigated the impact of intermolecular interactions on the shift of Raman spectra, identifying the reasons for the differences in Raman signals between pure furfural, furfural in oil, and furfural in water. A batch of samples with furfural dissolved in insulation oil was measured using this system and achieved a limit of detection of 0.091 mg/L. The stability of the dual-enhanced Raman platform was experimentally verified with a spectral intensity fluctuation of 0.68%. This method is fast, stable, adaptable, and suitable for the detection of a wide range of liquid ingredients.

Plasmon-driven catalytic reactions in optoplasmonic sandwich hybrid structure.

As an interesting phenomenon in the field of surface enhanced Raman spectroscopy (SERS), the plasmon-driven catalytic reaction (PDSC) induced by plasmonic hot electrons has great value in the research of novel properties of surface plasmons and accuracy of SERS applications. In this work, an optoplasmonic sandwich hybrid structure is proposed for studying PDSC of p-aminothiophenol (PATP) molecules, which is composed of Au film, metal organic frameworks (MOFs) nanoparticles, zeolithic imidazolate (ZIF-8), and single S i O 2 microsphere (Au f i l m@M O F s@S i O 2). In order to analyze the novel, to the best of our knowledge, phenomenon of the PDSC in this micro-nano structure, the hot electron generation in the MOF without the plasmonic core is carried out by combining the plasmonic enhancement of gold film with the light concentration of microspheres. Experimental data show that the PDSC reactions is dependent on the size of the MOFs nanoparticle and the size of the S i O 2 microsphere, which is confirmed by the electromagnetic field simulation of the finite-difference time-domain method (FDTD). Our work not only strengthens the understanding of surface plasmon in optoplasmonic hybrid structures but also has broad application prospects in the SERS and plasmon-driven catalytic fields.

Plasmon-Driven Interfacial Catalytic Reactions in Plasmonic MOF Nanoparticles.

Benefiting from the noble metal nanoparticle core and organic porous nanoshell, plasmonic metal-organic frameworks (MOFs) become a nanostructure with great enhancement of the electromagnetic field and a high density of reaction sites, which has fantastic optical properties in surface plasmon-related fields. In this work, the plasmon-driven interfacial catalytic reactions involving p-aminothiophenol to 4,4'-dimercaptoazobenzene (trans-DMAB) in both the liquid and gaseous phases are studied in plasmonic MOF nanoparticles, which consist of a Ag nanoparticle core and an organic shell (ZIF-8). The surface-enhanced Raman spectroscopy (SERS) spectra recorded at the plasmonic MOF in an aqueous environment demonstrate that the reversible plasmon-driven interfacial catalytic reactions could be modulated by a reductant (NaBH4) or oxidant (H2O2). Also, the situ SERS spectra also point out that plasmonic MOF (AgNP@ZIF-8) nanoparticles exhibit much better catalytic performance in the H2O2 solution compared to pure Ag nanoparticles for the anti-oxidation caused by the MOF shell. It is surprising that although there is greater SERS enhancement obtained at pure Ag nanoparticles, the plasmon-driven interfacial catalytic reactions only occur at plasmonic AgNP@ZIF-8 nanoparticles in the gaseous phase. This interesting phenomenon is further confirmed and analyzed by simulated electromagnetic field distributions, which could be understood by the effective capture of gaseous molecules by the organic porous nanoshell. Our work not only explores the plasmonic MOF nanoparticles with unique optical properties but also strengthens the understanding of plasmon-driven interfacial catalytic reactions.

Microfluidic Transport of Hybrid Optoplasmonic Particles for Repeatable SERS Detection.

For its ultrahigh sensitivity, the microfluidic system combined with surface-enhanced Raman spectroscopy (SERS) becomes one of the most interesting topics in integrated online monitoring related fields. In previous reports, the commonest surface plasmon-enhanced substrates in microfluidics consist of immobilized metal nanostructures on the channel surface to overcome the disturbance of Brownian motion. In this work, a hybrid optoplasmonic microfluidic conveyer is developed, in which the movable, highly ordered optoplasmonic particles are delivered to the detection spot for SERS detection. Here, the optoplasmonic particle is the SiO2 microsphere with in situ photochemical reduced Ag nanoparticles on the surface. Because of the converged light at the SiO2 microsphere surface, the SERS spectra collected at this optoplasmonic particle in the channel exhibit excellent performance, which is confirmed by the simulated electric field distribution. In addition, the experimental data also demonstrate that the quantitative analysis is achieved at 1 nM in this optoplasmonic microfluidic conveyer. Furthermore, the used optoplasmonic particle can be ejected from the microfluidic channel by modulating the velocity of injected fluid such that the new optoplasmonic particle will be delivered to the detection spot for repeatable SERS detection in the same channel. The dynamic process of optoplasmonic particle transport is investigated in this microconveyer, and the built theoretical model to predict the particle release is highly identical with the experimental data. These data point out that our hybrid optoplasmonic microfluidic conveyer has repeatable enhanced substrates with the high SERS sensitivity to overcome the cross-contamination of different target molecules in repeatable detection.

Au nanobowtie on a SiO2 microsphere for optoplasmonic trapping.

The combination of photonic and plasmonic elements with complementary optical properties has stimulated the development of optoplasmonic hybrid systems, in which photonic and plasmonic elements can interact synergistically, breaking through the limitations of traditional structures. In this paper, a new optoplasmonic tweezer is theoretically proposed by using the Au nanobowtie and SiO2 microsphere. The finite-difference time-domain simulation is used to study the influence of the size of the SiO2 microsphere and the SiO2 hemisphere in polydimethylsiloxane on the optical potential well. The simulation results show that the electric field intensity of the structure is increased by 6 times compared with the Au nanobowtie structure, and the gradient force and the trapping potential are also significantly improved.

Deterministic Scheme for Two-Dimensional Type-II Dirac Points and Experimental Realization in Acoustics.

Low-energy electrons near Dirac/Weyl nodal points mimic massless relativistic fermions. However, as they are not constrained by Lorentz invariance, they can exhibit tipped-over type-II Dirac/Weyl cones that provide highly anisotropic physical properties and responses, creating unique possibilities. Recently, they have been observed in several quantum and classical systems. Yet, there is still no simple and deterministic strategy to realize them since their nodal points are accidental degeneracies, unlike symmetry-guaranteed type-I counterparts. Here, we propose a band-folding scheme for constructing type-II Dirac points, and we use a tight-binding analysis to unveil its generality and deterministic nature. Through realizations in acoustics, type-II Dirac points are experimentally visualized and investigated using near-field mappings. As a direct effect of tipped-over Dirac cones, strongly tilted kink states originating from their valley-Hall properties are also observed. This deterministic scheme could serve as a platform for further investigations of intriguing physics associated with various strongly Lorentz-violating nodal points.

Subwavelength topological edge states based on localized spoof surface plasmonic metaparticle arrays.

Plasmonic cluster arrays have demonstrated rich physics in topological photonics, but they are seriously affected by the material loss and limited by the requirement of high-precision machining. Here, we propose a kind of ultra-thin metaparticle arrays which can mimic the coupled localized plasmonic resonances at lower frequency ranges and so that can overcome the loss and fabrication problems in real metal plasmonic systems. The metaparticle is a metallic disk with circuitous grooves that can support both spoof electric and magnetic localized resonances, and these resonances can be pushed to a subwavelength region through tuning the geometric parameters. In virtue of the highly field confinement of these localized resonances, it is thought to be an ideal experimental platform to be an analogy with various near-field interactions in topological materials. As a first proof-of-concept study to show this feasibility, the subwavelength topological edge states at the zigzag metaparticle chain boundaries are numerically and experimentally demonstrated at microwave ranges. Moreover, the subwavelength topological edge states in this zigzag chain can be excited simply by the plane wave incidence, and the edge modes at two ends can be selectively excited by controlling the polarization direction. Therefore, this kind of metaparticle array not only provides an ideal platform to experimentally study various near-filed interaction dominated topological systems but may also find massive potential applications.

Nanowire assisted repeatable DEP-SERS detection in microfluidics.

Surface enhanced Raman spectroscopy (SERS) detection in microfluidics is an interesting topic for its high sensitivity, miniaturization and online detection. In this work, a SERS detection in microfluidics with the help of the Ag nanowire aggregating based on dielectrophoresis (DEP) is reported. The Raman intensities of molecule in microfluidics is greatly enhanced in the naturally generated nanogaps of Ag nanowire aggregating modulated by DEP. Firstly, the influence of DEP voltage and time on Ag nanowire aggregating is investigated to figure out the optimal condition for SERS. And then, the SERS intensities of methylene blue and rhodamine6G at various concentration with high reproducibility and uniformity are studied. Furthermore, the experiment data demonstrate this DEP-SERS system could be repeated used for different molecule detections. At last, the SERS of melamine is measured to explore its application on food safety. Our work anticipates this nanowire assisted repeatable DEP-SERS detection in microfluidics with high sensitivity could meet the emerging needs in environmental pollution monitoring, food safety evaluation, and so on.

On-chip 3D SERS materials produced by self-assemble of copper microparticle and galvanic replacement reaction.

Surface-enhanced Raman scattering (SERS) has powerful capabilities in the field of environmental analysis and biological diagnostics because of its instinctive properties of high sensitivity and label-free detection. However, the fabrication of SERS substrate requires complicated processes and expensive equipment. This paper proposes a simple method approach to synthesize a 3D SERS substrate via electroless galvanic replacement reaction inside a microfluidic channel. Copper microparticles are assembled in a microfluidic channel, and silver nitrate solution is pumped into the channel to in situ produce the silver nanoparticles (Ag NPs) on the surface of copper microparticles. Because the copper particles occupy the channel by stack, the 3D Cu@AgNP SERS substrate can be obtained. The probing molecule (methylene blue) was utilized to investigate with the limit of detection (1×10-7 M). The biological molecules (urea and melamine) have been used to demonstrate its benefits in medical applications, and cancer cell detection has been implemented to demonstrate its benefits in cell biology. In addition, the device can filter and wash cells, forming a simple and fast filter. Our work on this simple fabrication method of active SERS substrate has great value for medical and biological applications.

Type-II Dirac Photons at Metasurfaces.

Topological characteristics of energy bands, such as Dirac and Weyl nodes, have attracted substantial interest in condensed matter systems as well as in classical wave systems. Among these energy bands, the type-II Dirac point is a nodal degeneracy with tilted conical dispersion, leading to a peculiar crossing dispersion in the constant-energy plane. Such nodal points have recently been found in electronic materials. The analogous topological feature in photonic systems remains a theoretical curiosity, with experimental realization expected to be challenging. Here, we experimentally realize the type-II Dirac point using a planar metasurface architecture, where the band degeneracy point is protected by the underlying mirror symmetry of the metasurface. Gapless edge modes are found and measured at the boundary between the different domains of the symmetry-broken metasurface. Our Letter shows that metasurfaces are simple and practical platforms for realizing electromagnetic type-II Dirac points, and their planar structure is a distinct advantage that facilitates applications in two-dimensional topological photonics.

Plasmonic nano-tweezer based on square nanoplate tetramers.

The research fields of trapping nanoparticles have experienced a huge development in recent years, which mainly benefits from the unique field enhancement in plasmonic nanomaterials. Since the large field enhancement originates from the excited localized surface plasmon at the metal surface, exploring novel metal nanostructures with high trapping efficiency is always the main goal in this field. In this work, the plasmonic trapping of nanoparticles based on the gold periodic square tetramers (PST) was investigated through full-wave simulations using the finite-difference time-domain (FDTD) method. The electric field and surface charge distributions on the surface of PST indicate that both the trapping position and efficiency are influenced by orientations of the square nanoplates. The maximum electromagnetic enhancement is achieved when all square nanoplates rotate 45° along the z axis. Therefore, the gradient force and trapping potential of this PST with optimal orientation were further studied, and the results indicate that a dielectric nanoparticle of 15 nm radius can be stably captured. Furthermore, the calculation results show that the plasmonic trapping with this PST exhibits strong polarization dependence. It is easy to change the trapping position and the field intensity by tuning the polarization of the incident wave. Our work enables a deeper understanding of this kind of plasmonic trapping and could have potential applications in biomedical research and life science.

Plasmon-driven surface catalysis on photochemically deposited-based SERS substrates.

For the virtues of convenience and repeatability, photochemically deposited nanoparticles (NPs) as ferroelectric-based surface-enhanced Raman scattering (SERS) substrates have great potential in the surface-plasmon-related applications. In this work, the plasmon-driven surface catalysis (PDSC) reaction is investigated on lithium niobate (LiNbO3) film with photochemically deposited Au NPs. The SERS spectra indicate that the performance of PDSC reaction on a substrate with various Au3+ concentrations in photochemical deposition is obviously different. Combining structure characterization and electromagnetic field simulation, this result is mainly attributed to the surface plasmon coupling between Au NPs. Furthermore, the results also point out that the exposure time in photochemical deposition plays an important role in PDSC reactions. Our studies on photochemically deposited Au NP substrates provide strong support and further understanding to the research on PDSC reactions and also to other surface-plasmon-related fields.

Fano resonance properties of gold nanocrescent arrays.

The Fano resonance induced by symmetry breaking could improve the sensitivity of localized surface plasmon resonance sensors. In this work, the spectra of gold nanocrescent arrays are measured and confirmed by simulation results through the finite element method (FEM). The Fano resonance presented in the spectra could be modulated by the symmetry breaking with different waist widths, which are understood through plasmonic hybridization theory with the help of surface charge distribution. Our results indicate the Fano lineshape is generated by the coherent coupling of the quadrupole plasmon mode QH of nanohole and the antibonding plasmon mode D(AB) of nanocrescent. Finally, the high figure of merit (FoM=1.6-3.5) of the Q mode in the visible region illustrates this nanocrescent Fano sensor is of great value in the biological and chemical scientific fields.

Gold crescent nanodisk array for nanoantenna-enhanced sensing in subwavelength areas.

Benefitting from the antenna effect and localized surface plasmon resonance (LSPR), a metal nanoparticle with a designed morphology has the amazing ability to confine light energy into the required extremely small volume, whose refractive index largely affects the optical properties of the whole metal nanoparticle. In this work, the optical spectra and near-field distribution of a gold nanocrescent array were investigated both experimentally and theoretically. To find out the LSPR wavelength and the enhancement using different morphologies of sharp tips, the spectra of gold nanocrescent arrays with different waist widths (d) were first measured, which were then confirmed and analyzed using the finite difference time-domain method and the hybridization theory. At last, the LSPR of this array with 100 nm diameter dielectric nanodisks was studied for sensing in subwavelength areas. Our results showed that because of its giant nanoantenna-enhanced electromagnetic field at the two tips, this gold nanocrescent array could be a suitable local senor to sense the variation of a local medium in a subwavelength area.

Plasmonic cellulose microfilament assisted SERS detection in microfluidics.

Limited by the narrow enhanced area of nanoscale on the metal surface, the sensitivity of surface-enhanced Raman spectroscopy (SERS) detection in solution is usually much lower than the detection in a solid substrate, which is dramatic in microfluidics for online detection. In this work, a cellulose microfilament embraced by Ag nanoparticles, called plasmonic cellulose microfilament, is located in a microchannel for SERS detection in microfluidics. Benefiting from the congestion caused by the plasmonic cellulose microfilament in a microchannel, the trace molecule in the solution is much easier to gather in Ag nanoparticles for Raman enhancement. To obtain high sensitivity, the structure of plasmonic cellulose microfilament is optimized. The SERS spectra collected in this novel microfluidics demonstrate that the plasmonic cellulose microfilament presents a high sensitivity at 10-13 M and good reproducibility in SERS detection. In addition, automatic identification of urea presence or absence was achieved based on deep learning (DL) here. The results show excellent diagnostic accuracy (99 %), which suggests that a fast, label-free urea screening tool can be developed. These results point out this SERS microfluidics with plasmonic cellulose microfilament has a great application prospective in online SERS detection with high sensitivity.

Deep Learning Enabled SERS Identification of Gaseous Molecules on Flexible Plasmonic MOF Nanowire Films.

Through the capture of a target molecule at the metal surface with a highly confined electromagnetic field induced by surface plasmon, surface enhanced Raman spectroscopy (SERS) emerges as a spectral analysis technology with high sensitivity. However, accurate SERS identification of a gaseous molecule with low density and high velocity is still a challenge due to its difficulty in capture. In this work, a flexible paper-based plasmonic metal-organic framework (MOF) film consisting of Ag nanowires@ZIF-8 (AgNWs@ZIF-8) is fabricated for SERS detection of gaseous molecules. Benefiting from its micronanopores generated by the nanowire network and ZIF-8 shell, the effective capture of the gaseous molecule is achieved, and its SERS spectrum is obtained in this paper-based flexible plasmonic MOF nanowire film. With optimal structure parameters, spectra of gaseous 4-aminothiophenol, 4-mercaptophenol, and dithiohydroquinone demonstrate that this film has good SERS performance, which could maintain obvious Raman signals within 30 days during reproducible detection. To realize SERS identification of gaseous molecules, deep learning is performed based on the SERS spectra of the mixed gaseous analyte obtained in this flexible porous film. The results point out that an artificial neural network algorithm could identify gaseous aldehydes (gaseous biomarker of colorectal cancer) in simulated exhaled breath with high accuracy at 93.7%. The integration of the flexible paper-based film sensors with deep learning offers a promising new approach for noninvasive colorectal cancer screening. Our work explores SERS applications in gaseous analyte detection and has broad potential in clinical medicine, food safety, environmental monitoring, etc.

SERS-based AI diagnosis of lung and gastric cancer via exhaled breath.

Distinct diagnosis between Lung cancer (LC) and gastric cancer (GC) according to the same biomarkers (e.g. aldehydes) in exhaled breath based on surface-enhanced Raman spectroscopy (SERS) remains a challenge in current studies. Here, an accurate diagnosis of LC and GC is demonstrated, using artificial intelligence technologies (AI) based on SERS spectrum of exhaled breath in plasmonic metal organic frameworks nanoparticle (PMN) film. In the PMN film with optimal structure parameters, 1780 SERS spectra are collected, in which 940 spectra come from healthy people (n = 49), another 440 come from LC patients (n = 22) and the rest 400 come from GC patients (n = 8). The SERS spectra are trained through artificial neural network (ANN) model with the deep learning (DL) algorithm, and the result exhibits a good identification accuracy of LC and GC with an accuracy over 89 %. Furthermore, combined with information of SERS peaks, the data mining in ANN model is successfully employed to explore the subtle compositional difference in exhaled breath from healthy people (H) and L/GC patients. This work achieves excellent noninvasive diagnosis of multiple cancer diseases in breath analysis and provides a new avenue to explore the feature of disease based on SERS spectrum.

Wearable intelligent sweat platform for SERS-AI diagnosis of gout.

For the past few years, sweat analysis for health monitoring has attracted increasing attention benefiting from wearable technology. In related research, the sensitive detection of uric acid (UA) in sweat with complex composition based on surface-enhanced Raman spectroscopy (SERS) for the diagnosis of gout is still a significant challenge. Herein, we report a visualized and intelligent wearable sweat platform for SERS detection of UA in sweat. In this wearable platform, the spiral channel consisted of colorimetric paper with Ag nanowires (AgNWs) that could capture sweat for SERS measurement. With the help of photos from a smartphone, the pH value and volume of sweat could be quantified intelligently based on the image recognition technique. To diagnose gout, SERS spectra of human sweat with UA are collected in this wearable intelligent platform and analyzed by artificial intelligence (AI) algorithms. The results indicate that the artificial neural network (ANN) algorithm exhibits good identification of gout with high accuracy at 97%. Our work demonstrates that SERS-AI in a wearable intelligent sweat platform could be a feasible strategy for diagnosis of gout, which expands research on sweat analysis for comfortable and noninvasive health monitoring.

Early-stage oral cancer diagnosis by artificial intelligence-based SERS using Ag NWs@ZIF core-shell nanochains.

Surface-enhanced Raman spectroscopy (SERS) has great potential in the early diagnosis of diseases by detecting the changes of volatile biomarkers in exhaled breath, because of its high sensitivity, rich chemical molecular fingerprint information, and immunity to humidity. Here, an accurate diagnosis of oral cancer (OC) is demonstrated using artificial intelligence (AI)-based SERS of exhaled breath in plasmonic-metal organic framework (MOF) nanoparticles. These plasmonic-MOF nanoparticles were prepared using a zeolitic imidazolate framework coated on Ag nanowires (Ag NWs@ZIF), which offers Raman enhancement from the plasmonic nanowires and gas enrichment from the ZIF shells. Then, the core-shell nanochains of Ag NWs@ZIF prepared with 0.5 mL Ag NWs were selected to capture gaseous methanethiol, which is a tumor biomarker, from the exhalation of OC patients. The substrate was used to collect a total of 400 SERS spectra of exhaled breath of simulated healthy people and simulated OC patients. The artificial neural network (ANN) model in the AI algorithm was trained with these SERS spectra and could classify them with an accuracy of 99%. Notably, the model predicted OC with an area under the curve (AUC) of 0.996 for the simulated OC breath samples. This work suggests the great potential of the combination of breath analysis and AI as a method for the early-stage diagnosis of oral cancer.

Prediction for odor gas generation from domestic waste based on machine learning.

Domestic waste is prone to produce a variety of volatile organic compounds (VOCs), which often has unpleasant odors. A key process in treating odor gases is predicting the production of odors from domestic waste. In this study, four factors of domestic waste (weight, wet composition, temperature, and fermentation time) were adopted to be the prediction indicators in the prediction for domestic waste odor gases. Machine learning models (Random Forest, XGBoost, LightGBM) were established using the odor intensity values of 512 odor gases from domestic waste. Based on these data, the regression prediction with supervised machine learning was achieved, in which three different algorithmic models were evaluated for prediction performance. A Random Forest model with a R2 value of 0.8958 demonstrated the most accurate prediction of the production of domestic waste odor gas based on our data. Furthermore, the prediction results in the Random Forest model were further discussed based on the microbial fermentation of domestic waste. In addition to enhancing our knowledge of the production of odor from domestic waste, we also explore the application of machine learning to odor pollution in our study.