Herein,a reusable and portable surface-enhanced Raman spectroscopy(SERS)sandpaper was successfully synthesized for the sensitive detection of S-fenvalerate in foods.Commercial sandpapers were decorated with Ag@SiO2@Au...Herein,a reusable and portable surface-enhanced Raman spectroscopy(SERS)sandpaper was successfully synthesized for the sensitive detection of S-fenvalerate in foods.Commercial sandpapers were decorated with Ag@SiO2@Au nanoarrays via a liquid-liquid interface self-assembly method.The capacity of sandpaper to float directly on the cyclohexane-water interface allows nanoarrays to be formed directly on it,thereby minimizing stacking issues typically associated with nanoarray assemblies and significantly enhancing the sensitivity of S-fenvalerate detection.Moreover,the SERS sandpaper was reusable and portable due to its strong adhesion of the nanoarrays.Under optimized testing conditions,the developed SERS sandpaper method was capable of detecting S-fenvalerate,demonstrating a strong linear response within a concentration range of 10^(–7)–10^(3)μmol/L,with a limit of detection of 1.92×10^(−8)μmol/L.The analysis of spiked food samples containing S-fenvalerate using the developed SERS sandpaper afforded excellent recoveries(92.2%−109.7%).Additionally,the SERS sandpaper was successfully applied to quantify S-fenvalerate in real food samples,with results consistent with analyses conducted using gas chromatography.展开更多
Surface-enhanced Raman spectroscopy(SERS)has evolved from a laboratory technique to a practical tool for ultra-sensitive detection,particularly in the biomedical field,where precise molecular identification is crucial...Surface-enhanced Raman spectroscopy(SERS)has evolved from a laboratory technique to a practical tool for ultra-sensitive detection,particularly in the biomedical field,where precise molecular identification is crucial.Despite significant advancements,a gap remains in the literature,as no comprehensive review systematically addresses the high-precision construction of SERS substrates for ultrasensitive biomedical detection.This review fills that gap by exploring recent progress in fabricating high-precision SERS substrates,emphasizing their role in enabling ultrasensitive bio-medical sensors.We carefully examine the key to these advancements is the precision engineering of substrates,including noble metals,semiconductors,carbon-based materials,and two-dimensional materials,which is essential for achieving the high sensitivity required for ultrasensitive detection.Applications in biomedical diagnostics and molecular analysis are highlighted.Finally,we address the challenges in SERS substrate preparation and outline future directions,focusing on improvement strategies,design concepts,and expanding applications for these advanced materials.展开更多
This article presented a facile fabrication process for polydimethylsiloxane(PDMS)composite gold nanotris⁃octahedra(Au NTOH)for a flexible SERS sensor with high sensitivity.Specifically,Au NTOH with excellent SERS beh...This article presented a facile fabrication process for polydimethylsiloxane(PDMS)composite gold nanotris⁃octahedra(Au NTOH)for a flexible SERS sensor with high sensitivity.Specifically,Au NTOH with excellent SERS behaviors was synthesized using a seed-mediated growth method and the dimensions of the Au NTOH was easily tuned.In addition,the influence of size on the SERS performance of their monolayers was systematically investigated,and the Au NTOH with the size of 61 nm possessed the best SERS performance.Importantly,a hydrophilic-substrateassisted interfacial self-assembled monolayer transfer technique was proposed to transfer Au NTOH onto PDMS films,resulting in forming flexible and transparent Au NTOH@PDMS substrates.Furthermore,the excellent signal homoge⁃neity of this substrate was demonstrated and the sensitivity was verified by a measurement of crystal violet(CV)as low as 1×10^(-8) mol/L.As a result,this SERS sensor is progressing for applying in the identification of trace contaminants in broad fields.展开更多
Bacterial infection is a major threat to global public health,and can cause serious diseases such as bacterial skin infection and foodborne diseases.It is essential to develop a new method to rapidly diagnose clinical...Bacterial infection is a major threat to global public health,and can cause serious diseases such as bacterial skin infection and foodborne diseases.It is essential to develop a new method to rapidly diagnose clinical multiple bacterial infections and monitor food microbial contamination in production sites in real-time.In this work,we developed a 4-mercaptophenylboronic acid gold nanoparticles(4-MPBA-AuNPs)-functionalized hydrogel microneedle(MPBA-H-MN)for bacteria detection in skin interstitial fluid.MPBA-H-MN could conveniently capture and enrich a variety of bacteria within 5 min.Surface enhanced Raman spectroscopy(SERS)detection was then performed and combined with machine learning technology to distinguish and identify a variety of bacteria.Overall,the capture efficiency of this method exceeded 50%.In the concentration range of 1×10_(7) to 1×10^(10) colony-forming units/mL(CFU/mL),the corresponding SERS intensity showed a certain linear relationship with the bacterial concentration.Using random forest(RF)-based machine learning,bacteria were effectively distinguished with an accuracy of 97.87%.In addition,the harmless disposal of used MNs by photothermal ablation was convenient,environmentally friendly,and inexpensive.This technique provided a potential method for rapid and real-time diagnosis of multiple clinical bacterial infections and for monitoring microbial contamination of food in production sites.展开更多
Deep learning neural network incorporating surface enhancement Raman scattering technique(SERS)is becoming as a powerful tool for the precise classifications and diagnosis of bacterial infections.However,the large amo...Deep learning neural network incorporating surface enhancement Raman scattering technique(SERS)is becoming as a powerful tool for the precise classifications and diagnosis of bacterial infections.However,the large amount of sample requirement and time-consuming sample collection severely hinder its applications.We herein propose a spectral concatenation strategy for residual neural network using nonspecific and specific SERS spectra for the training data augmentation,which is accessible to acquiring larger training dataset with same number of SERS spectra or same size of training dataset with fewer SERS spectra,compared with pure non-specific SERS spectra.With this strategy,the training loss exhibit rapid convergence,and an average accuracy up to 100%in bacteria classifications was achieved with50 SERS spectra for each kind of bacterium;even reduced to 20 SERS spectra per kind of bacterium,classification accuracy is still>95%,demonstrating marked advantage over the results without spectra concatenation.This method can markedly improve the classification accuracy under fewer samples and reduce the data collection workload,and can evidently enhance the performance when used in different machine learning models with high generalization ability.Therefore,this strategy is beneficial for rapid and accurate bacteria classifications with residual neural network.展开更多
基金financially supported by the Key R&D Program of Shandong Province,China(2023CXGC010712).
文摘Herein,a reusable and portable surface-enhanced Raman spectroscopy(SERS)sandpaper was successfully synthesized for the sensitive detection of S-fenvalerate in foods.Commercial sandpapers were decorated with Ag@SiO2@Au nanoarrays via a liquid-liquid interface self-assembly method.The capacity of sandpaper to float directly on the cyclohexane-water interface allows nanoarrays to be formed directly on it,thereby minimizing stacking issues typically associated with nanoarray assemblies and significantly enhancing the sensitivity of S-fenvalerate detection.Moreover,the SERS sandpaper was reusable and portable due to its strong adhesion of the nanoarrays.Under optimized testing conditions,the developed SERS sandpaper method was capable of detecting S-fenvalerate,demonstrating a strong linear response within a concentration range of 10^(–7)–10^(3)μmol/L,with a limit of detection of 1.92×10^(−8)μmol/L.The analysis of spiked food samples containing S-fenvalerate using the developed SERS sandpaper afforded excellent recoveries(92.2%−109.7%).Additionally,the SERS sandpaper was successfully applied to quantify S-fenvalerate in real food samples,with results consistent with analyses conducted using gas chromatography.
基金supported by the projects funded by the Education Department of Shaanxi Provincial Government(NO.23JP116)the Natural Science Fund of Shaanxi Province(NO.2024JC-YBMS-396)+3 种基金the National Natural Science Foundation of China(NO.52171191,52371198,U22A20137)the Constructing National Independent Innovation Demonstration Zones(XM2024XTGXQ05)Shenzhen Science and Technology Innovation Program(JCYJ20220818102215033,GJHZ20210705142542015,JCYJ20220530160811027)Guangdong HUST Industrial Technology Research Institute,Guangdong Provincial Key Laboratory of Manufacturing Equipment Digitization(2023B1212060012).
文摘Surface-enhanced Raman spectroscopy(SERS)has evolved from a laboratory technique to a practical tool for ultra-sensitive detection,particularly in the biomedical field,where precise molecular identification is crucial.Despite significant advancements,a gap remains in the literature,as no comprehensive review systematically addresses the high-precision construction of SERS substrates for ultrasensitive biomedical detection.This review fills that gap by exploring recent progress in fabricating high-precision SERS substrates,emphasizing their role in enabling ultrasensitive bio-medical sensors.We carefully examine the key to these advancements is the precision engineering of substrates,including noble metals,semiconductors,carbon-based materials,and two-dimensional materials,which is essential for achieving the high sensitivity required for ultrasensitive detection.Applications in biomedical diagnostics and molecular analysis are highlighted.Finally,we address the challenges in SERS substrate preparation and outline future directions,focusing on improvement strategies,design concepts,and expanding applications for these advanced materials.
基金The National Natural Science Foundation of China(12274055)the Fundamental Research Funds for the Central Universities(04442024072)the Training Program of Innovation and Entrepreneurship for Undergraduates in Dalian Minzu University(202312026063)。
文摘This article presented a facile fabrication process for polydimethylsiloxane(PDMS)composite gold nanotris⁃octahedra(Au NTOH)for a flexible SERS sensor with high sensitivity.Specifically,Au NTOH with excellent SERS behaviors was synthesized using a seed-mediated growth method and the dimensions of the Au NTOH was easily tuned.In addition,the influence of size on the SERS performance of their monolayers was systematically investigated,and the Au NTOH with the size of 61 nm possessed the best SERS performance.Importantly,a hydrophilic-substrateassisted interfacial self-assembled monolayer transfer technique was proposed to transfer Au NTOH onto PDMS films,resulting in forming flexible and transparent Au NTOH@PDMS substrates.Furthermore,the excellent signal homoge⁃neity of this substrate was demonstrated and the sensitivity was verified by a measurement of crystal violet(CV)as low as 1×10^(-8) mol/L.As a result,this SERS sensor is progressing for applying in the identification of trace contaminants in broad fields.
基金supported by the National Natural Science Foundation of China(Grant Nos.:82204340,82173954,and 82073815)the Natural Science Foundation of Jiangsu Province,China(Grant No.:BK20221048)+1 种基金the Jiangsu Funding Program for Excellent Postdoctoral Talent,China(Grant No.:2022ZB295)Key Laboratory Project of Quality Control of Chinese Herbal Medicines and Decoction Pieces,Gansu Institute for Drug Control,China(Grant No.:2024GSMPA-KL02).
文摘Bacterial infection is a major threat to global public health,and can cause serious diseases such as bacterial skin infection and foodborne diseases.It is essential to develop a new method to rapidly diagnose clinical multiple bacterial infections and monitor food microbial contamination in production sites in real-time.In this work,we developed a 4-mercaptophenylboronic acid gold nanoparticles(4-MPBA-AuNPs)-functionalized hydrogel microneedle(MPBA-H-MN)for bacteria detection in skin interstitial fluid.MPBA-H-MN could conveniently capture and enrich a variety of bacteria within 5 min.Surface enhanced Raman spectroscopy(SERS)detection was then performed and combined with machine learning technology to distinguish and identify a variety of bacteria.Overall,the capture efficiency of this method exceeded 50%.In the concentration range of 1×10_(7) to 1×10^(10) colony-forming units/mL(CFU/mL),the corresponding SERS intensity showed a certain linear relationship with the bacterial concentration.Using random forest(RF)-based machine learning,bacteria were effectively distinguished with an accuracy of 97.87%.In addition,the harmless disposal of used MNs by photothermal ablation was convenient,environmentally friendly,and inexpensive.This technique provided a potential method for rapid and real-time diagnosis of multiple clinical bacterial infections and for monitoring microbial contamination of food in production sites.
基金supported by the National Key Research and Development Program of China(No.2023YFC3402900)the National Nature Science of Foundation(No.61875131)+1 种基金Shenzhen Key Laboratory of Photonics and Biophotonics(No.ZDSYS20210623092006020)Shenzhen Science and Technology Innovation Program(No.20231120175730001)。
文摘Deep learning neural network incorporating surface enhancement Raman scattering technique(SERS)is becoming as a powerful tool for the precise classifications and diagnosis of bacterial infections.However,the large amount of sample requirement and time-consuming sample collection severely hinder its applications.We herein propose a spectral concatenation strategy for residual neural network using nonspecific and specific SERS spectra for the training data augmentation,which is accessible to acquiring larger training dataset with same number of SERS spectra or same size of training dataset with fewer SERS spectra,compared with pure non-specific SERS spectra.With this strategy,the training loss exhibit rapid convergence,and an average accuracy up to 100%in bacteria classifications was achieved with50 SERS spectra for each kind of bacterium;even reduced to 20 SERS spectra per kind of bacterium,classification accuracy is still>95%,demonstrating marked advantage over the results without spectra concatenation.This method can markedly improve the classification accuracy under fewer samples and reduce the data collection workload,and can evidently enhance the performance when used in different machine learning models with high generalization ability.Therefore,this strategy is beneficial for rapid and accurate bacteria classifications with residual neural network.
