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.

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.

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.

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.

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.

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.

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.

SERS Monitored Kinetic Process of Gaseous Thiophenol Compound in Plasmonic MOF Nanoparticles.

Benefiting from the electromagnetic enhancement of noble metal nanoparticles (NPs) and the capture ability of organic frameworks, plasmonic metal-organic framework (MOF) structures have greatly promoted the development of gas detection by surface-enhanced Raman spectroscopy (SERS). In those detections, the kinetic process of gaseous molecules in plasmonic-MOF structures has a great influence on SERS spectra, which is still lacking intensive investigation in previous reports. In this work, the kinetic processes of gaseous thiophenol compounds (TPC) in the plasmonic Zeolitic Imidazolate Framework (Ag@ZIF) core-shell NPs are studied by SERS spectra. The experimental data demonstrate that the SERS intensities of gaseous TPC could be enhanced once more in an H2 mixed gas environment with different functional groups of TPC. Further results reveal that the two-step enhancement of SERS intensities is not only related to the thicknesses of the MOF shell but also affected by the ambient mixed gas. To understand this novel phenomenon, the binding energy between the gaseous molecule and ZIF is calculated based on first-principles computation. In combination with the plasmonic properties of the Ag core, a molecular collision model is introduced here to show the distribution of gaseous TPC molecules in ZIF, which could be responsible for this interesting two-step enhancement of SERS intensities. Furthermore, the H2 assisted kinetic process of gaseous p-aminothiophenol (PATP) is also analyzed by the classical pseudo-first-order kinetic model, which is consistent with our experimental SERS data. Our work not only reveals the novel phenomenon of plasmonic-MOF structures to improve the gas detection by SERS spectra but also enriches the understanding of the microcosmic process of gaseous molecules in the mixed gas environment to optimize MOF structures for gas capture and storage.

Enhanced response of titanium doped iron(ii) oxalate under electric field.

Electrorheological (ER) fluid, containing polarized particles within an insulating liquid, represents a smart material, the mechanical properties of which can be altered mainly by an electric field. In this work, ER fluids based on cauliflower iron(ii) oxalate doped titanium particles show excellent rheological and wetting properties by the sample co-precipitation method. The morphology of the particles is observed by SEM and the molecular structure within the particles is obtained via XRD and FTIR. The distribution of elements within the particles is obtained by EDS. Owing to a lower current density than pure iron(ii) oxalate, the SEM and optical images show an obvious chain-like structure within the ER fluids with 2 wt% and 5 wt%, respectively, under 2 kV mm-1. Then, the rheological properties of these ER fluids are tested up to 3 kV mm-1 and the results show a gratifying property of resisting shear with different shear rates (0.1-100 s-1), which is attributed to the appearance of a stable chain-like structure. At the same time, the ER efficiency and the switching performance are obtained and the static yield stress fits the relevant electric field strength well. Ultimately, an excellent sedimentation ratio is obtained from 0 h to 600 h.

Mxenes-Au NP Hybrid Plasmonic 2D Microplates in Microfluidics for SERS Detection.

Combined with microfluidics, surface-enhanced Raman spectroscopy (SERS) exhibits huge application prospective in sensitive online detection. In current studies, the design and optimization of plasmonic enhanced structures in microfluidics for SERS detection could be an interesting challenge. In this work, hybrid plasmonic 2D microplates composed of Mxenes (Ti3C2Tx) microplates and in-situ synthesized Au nanoparticles (Au NPs) are fabricated in a microchannel for enhanced structures in SERS microfluidics. Benefiting from the 2D Mxenes microplates with complex distributions, the enhanced areas generated by Au NPs are quite enlarged in a microchannel, which exhibits high sensitivity in SERS detection at 10-10 M for Nile blue (NB) molecules in microfluidics. The mechanism of electromagnetic enhancement (EM) and chemical enhancement (CM) is analyzed. The experimental data indicate the ultrasonic times of Mxenes and the concentration of Au3+ play important roles in the sensitivity of SERS detection, which is confirmed by the simulated electric field distributions. Furthermore, a typical pesticide (thiram) at 100 ppm in water is detected on these SERS microfluidics with hybrid plasmonic enhanced structures, which demonstrates that our work not only strengthens the knowledge of plasmonics but also enlarges the application of SERS.

Optoplasmonic MOFs film for SERS detection.

Optoplasmonic hybrid structures composed of photonic and plasmonic elements with excellent optical properties are of great significance for the development of surface-enhanced Raman spectroscopy (SERS) substrates. In this work, the optoplasmonic hybrid structure is composed of SiO2 microsphere and two-dimensional (2D) plasmonic- metal organic frameworks (MOF) film. Among them, the 2D plasmonic-MOF film is prepared from silver nanoparticles encapsulated by zeolitic imidazole acid framework (AgNP@ZIF-8) by self-assembly method. This optoplasmonic hybrid structure with gas adsorption properties could be used as a SERS substrate for 4-Mercaptophenol (4-MP) gas detection. Experimental data show that this substrate is dependent on the thickness of the ZIF shell and the size of the SiO2 microspheres. In addition, it is confirmed by the electromagnetic field simulation of finite-difference time-domain method (FDTD). The optoplasmonic hybrid microstructures exhibit good uniformity for detection of 4-MP gas molecules. This work not only broadens the understanding of our optoplasmonic hybrid structure, but also has broad application prospects in SERS and gas sensing related fields.

Strong confinement of gap modes induced by the film modified electric and magnetic modes in dielectric nanoparticle dimers.

Nanogaps are one of the most useful systems in nanooptics. The gap modes in a film coupled dielectric nanoparticle dimer system are influenced by both of the film and the electric and magnetic modes of the particles. In this work, strong confinement of gap modes of dielectric (Si) nanoparticle dimer on Au/Si film is investigated. The results show an abnormal electric field enhancement obtained between Si nanoparticle dimer on metal film, which is attributed to film coupled electric and magnetic dipole modes in dielectric nanoparticle dimer. The results are further analyzed with mode hybridization theory. Furthermore, the surface enhanced Raman spectroscopy (SERS) is performed to demonstrate these theoretical analyses. The film induced electromagnetic field redistribution in dielectric nanoparticle dimer not only extend the knowledge of dielectric gap modes but also has tremendous applications, e.g. light manipulating in subwavelength, light harvest, surface enhanced spectrum, etc.

Sandwich optoplasmonic hybrid structure for surface enhanced Raman spectroscopy.

Combined with photonic microstructure and plasmonic nanostructure, the optoplasmonic hybrid structure with fantastic optical properties attracts lots of attentions in recent years. With the help of light enrichment by dielectric photonic microenvironment, the embedded plasmonic nanoantennas generate much greater electromagnetic field enhancement at surface for light harvesting compared to conventional plasmonic nanostructures. In this work, a sandwich optoplasmonic hybrid structure is developed for surface enhanced Raman spectroscopy (SERS) detection, which is consisted of polymethyl methacrylate (PMMA) microspheres array, self-assembled Ag nanoparticles (AgNPs) film and SiO2 microsphere (PMMA@AgNPs@SiO2). The SERS spectra collected on this optoplasmonic substrate point out it has high sensitivity with limit of detection (LOD) at 10 fM. The experimental data demonstrate both the PMMA microarray and SiO2 microsphere play important roles in enrichment of light illuminating at AgNPs for SERS detection, which is confirmed by the simulated electric field distributions. This sandwich optoplasmonic hybrid structure not only enlarges research field of surface plasmon, but also provides a novel SERS subtract for sensitive analysis in chem/bio-field.

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.

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.

A Mobile and Self-Powered Micro-Flow Pump Based on Triboelectricity Driven Electroosmosis.

Electroosmotic pumps have been widely used in microfluidic systems. However, traditional high-voltage (HV)-sources are bulky in size and induce numerous accessional reactions, which largely reduce the system's portability and efficiency. Herein, a motion-controlled, highly efficient micro-flow pump based on triboelectricity driven electroosmosis is reported. Utilizing the triboelectric nanogenerator (TENG), a strong electric field can be formed between two electrodes in the microfluidic channel with an electric double layer, thus driving the controllable electroosmotic flow by biomechanical movements. The performance and operation mechanism of this triboelectric electroosmotic pump (TEOP) is systematically studied and analyzed using a basic free-standing mode TENG. The TEOP produces ≈600 nL min-1 micro-flow with a Joule heat down to 1.76 J cm-3 nL-1 compared with ≈50 nL min-1 and 8.12 J cm-3 nL-1 for an HV-source. The advantages of economy, efficiency, portability, and safety render the TEOP a more conducive option to achieve wider applications in motion-activated micro/nanofluidic transportation and manipulation.

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.

Microdroplet extraction assisted ultrasensitive Raman detection in complex oil.

The Raman detection of trace substances in complex oil is still a great challenge at present because of the strong disturbance of background activity and the suppression of intensity in spectra caused by complicated components. In this work, a simple and robust approach based on microdroplet liquid-liquid extraction for the real-time Raman spectroscopy monitoring of trace substances in complex oil is reported. Based on unbalanced chemical potentials between water and oil on a microfluidic chip, a target trace molecule is extracted from complex mineral oil to a water microdroplet. Benefiting from the real-time fluorescence intensities of fluorescein in a water microdroplet, the extraction performance is investigated and optimized. The optimal water microdroplet is implemented for the Raman detection of furfural in a complex mineral oil, a typical trace performance marker in electric power equipment, and this exhibits excellent sensitivity with a limit of detection (LOD) of 26 ppb. Compared to traditional detection technology for trace substances in complex oil (high performance liquid chromatography, HPLC), this method greatly simplified the process of measurement, reduced the volume of sample required, had a fast measurement time, and exhibited the prospect of real-time monitoring applications with high sensitivity, which not only promotes the development of oil quality but also enlarges existing knowledge related to using Raman spectroscopy in chem-/bio-sensing.

Optoplasmonic film for SERS.

Combining plasmonic and photonic elements, optoplasmonic hybrid structure exhibits excellent optical properties beyond conventional plasmonic or photonic structures. In this work, the optoplasmonic film consists of SiO2 microsphere and Au film without any nanostructures is investigated. With the help of a microsphere, the intensity of surface enhanced Raman spectroscopy (SERS) on Au film is highly enhanced (~1000 times) compared to bare Au film. The simulated electromagnetic field points out the enhancement caused by the optical lens effect of SiO2 microsphere that high light intensity is generated under the microsphere to excite surface plasmon on Au film. Furthermore, our data demonstrates the microsphere lens enhancement is greatly influenced by the size of the SiO2 microsphere and wavelength of incident light. This interesting film with a simple configuration could overcome the challenges in the fabrication and store process induced by nanostructures, which play an important role in SERS application. Our work not only enlarges the knowledge of the optoplasmonic hybrid structure, but also exhibits excellent application prospective in light harvest field e.g. enhanced spectrum, photocatalysis, optothermal effect, and hot electron generation, etc.