Adsorption and recovery of phosphate using sodium carbonate as co-precipitant synthesized La&Zr dual-metal modified material: Adsorption mechanism and practical application.

Adsorption methods offer efficient recovery of phosphorus from water bodies. Modification adsorption materials combining lanthanum (La) and zirconium (Zr) dual-metal immobilized via co-precipitation method have been widely applied in the adsorption and recovery of phosphate. Meanwhile, sodium carbonate (Na2CO3) is gradually replacing sodium hydroxide (NaOH) as the mainstream co-precipitant for immobilizing metals into supporting matrices due to its excellent performance and environmental friendliness. However, the adsorption mechanisms of materials synthesized with different co-precipitants and the synergistic effects between dual-metal components are not well understood, which is not conducive to the further optimization of dual-metal adsorption materials. In this study, anion exchange resin was utilized as the supporting matrices, and La&Zr dual-metal-modified materials, La&Zr-CO32- and La&Zr-OH-, were prepared using Na2CO3 and NaOH as co-precipitants, respectively. The results indicate that La&Zr-CO32- exhibits superior performance in phosphate adsorption and recovery, with adsorption capacity and recovery efficiency reaching 36.28 mg/g and 82.59%, respectively. Additionally, this material demonstrates strong stability in reuse, phosphate selectivity, and a wide pH applicability range. La&Zr-CO32- achieves phosphate adsorption through surface electrostatic affinity, ligand exchange, and intraspherical complexation, whereas La&Zr-OH- primarily relies on electrostatic adsorption on the surface and interior of the material. Synergistic effects between La and Zr result in enhanced adsorption performance of the dual-metal material compared to individual metals. Specifically, phosphate adsorption is predominantly governed by La, while the presence of Zr further enhances ligand exchange between lattice oxygen and metals. Simultaneously, Zr doping enhances the phosphate recovery capacity and reusability of the materials. Continuous flow adsorption results from actual water bodies demonstrate that La&Zr-CO32- is more suitable for the removal and recovery of phosphate in water treatment engineering. This study provides a theoretical basis and technical support for the adsorption and recovery of phosphate using dual-metal-modified materials.

Rapid and Precise Differentiation and Authentication of Agricultural Products via Deep Learning-Assisted Multiplex SERS Fingerprinting.

Accurate and rapid differentiation and authentication of agricultural products based on their origin and quality are crucial to ensuring food safety and quality control. However, similar chemical compositions and complex matrices often hinder precise identification, particularly for adulterated samples. Herein, we propose a novel method combining multiplex surface-enhanced Raman scattering (SERS) fingerprinting with a one-dimensional convolutional neural network (1D-CNN), which enables the effective differentiation of the category, origin, and grade of agricultural products. This strategy leverages three different SERS-active nanoparticles as multiplex sensors, each tailored to selectively amplify the signals of preferentially adsorbed chemicals within the sample. By strategically combining SERS spectra from different NPs, a 'SERS super-fingerprint' is constructed, offering a more comprehensive representation of the characteristic information on agricultural products. Subsequently, utilizing a custom-designed 1D-CNN model for feature extraction from the 'super-fingerprint' significantly enhances the predictive accuracy for agricultural products. This strategy successfully identified various agricultural products and simulated adulterated samples with exceptional accuracy, reaching 97.7% and 94.8%, respectively. Notably, the entire identification process, encompassing sample preparation, SERS measurement, and deep learning analysis, takes only 35 min. This development of deep learning-assisted multiplex SERS fingerprinting establishes a rapid and reliable method for the identification and authentication of agricultural products.

Adjusted Preferential Adsorption of Intermediates via Regulation of the Electronic Structure during the Electrocatalytic CO2 Reduction Process.

The surface electronic structures of catalysts play a crucial role in CO2 adsorption and activation. Here, sulfur vacancies are introduced into CuInS2 nanosheets (Vs-CuInS2) to evaluate the effect of electronic structures at the surface-active sites on the electrochemical CO2 reduction reaction (CO2RR). Vs-CuInS2 exhibits a significant disparity in the highest FEformate/FECO (6.50) compared to that of CuInS2 (1.86). Specifically, the maximum current density (Jmax) of carbon products on Vs-CuInS2 is 78.78 mA cm-2, and a Faraday efficiency of carbon products (FEcarbon products) of ≥80% is achieved in 600 mV wide potential windows. In situ Raman measurements and density functional theory calculations elucidate the origin of the apparent alterations in the carbon product selectivity. The introduction of sulfur vacancies realizes the controllable regulation of the local electronic density around the metal active sites, inducing the transformation of *COOH and *OCHO from competitive adsorption on CuInS2 to specific adsorption on Vs-CuInS2. In addition, the regulation of electronic structures on Vs-CuInS2 inhibits *H adsorption. This work reveals the transfer of adsorption of CO2RR intermediates via regulation of the electronic structure, complementing the understanding of the mechanism for the enhanced CO2RR.

Microwave disinfection strengthened by a biochar-based microwave absorbing material for sewage resource utilization.

Proper disinfection treatment is the basic guarantee for safe utilisation of sewage. However, the commonly used disinfection methods are not suitable for nutrients containing reclaimed water. In this work, the microwave disinfection method assisted by a microwave-absorbing material in recycled water samples was investigated. Magnetic corn stalk biochar (MCSB), the microwave absorbing material, was prepared by high temperature carbonisation of corn stalk particles impregnated with ferrous sulfate. Escherichia coli and fecal coliforms were selected as target microorganisms to investigate the disinfection efficiency of MCSB assisted microwave radiation (MW/MCSB). The addition of microwave absorbing materials significantly improves the disinfection effect of water samples. Compared with the microwave radiation (MW) without MCSB, the bactericidal rate by using 107 CFU/L E. coli suspension increased from 63.5% to 100% at 480 W for 30 s after adding 4 g/L MCSB. Besides, the effects of MCSB dosage, microwave power, microwave radiation time, and initial bacterial concentration on disinfection efficiency were explored. Moreover, the bactericidal efficiency for actual sewage samples was also demonstrated by treating the effluent from septic tank sewage. The residual fecal coliforms in treated water samples met China's farmland irrigation water standard (GB 5084-2021). The result indicates that the proposed method of microwave disinfection strengthened by MCSB has a promising application prospect for reclaimed water disinfection.

Plasmon-Driven Photochemical Reduction Reaction on Silver Nanostructures for Green Fabrication of Superhydrophobic Surface.

Green fabrication of superhydrophobic surface by water-based processing is still challenging, because introduction of the substances with hydrophilic moieties compromises its superhydrophobicity. Herein, a plasmon-driven photochemical reduction reaction under ultraviolet light (UVA) irradiation is first discovered and is applied to deoxygenation of hydrophilic organic adsorbates on rough nano-Ag coating for the formation of stable superhydrophobic surface. A nano-Ag coating with strong localized surface plasmon resonance in the UVA region is prepared by a water-based silver mirror reaction and results in a unique chemical reduction reaction on its surface. Consequently, the low residual hydrophilic functionalities and the formed cross-linked structure of the adsorbate on Ag nanoparticles (NPs) enables the coating to exhibit stable superhydrophobicity against to both air and water. The superhydrophobic Ag NP-coated sandpaper can also be used as a surface-enhanced Raman scattering (SERS) substrate to concentrate aqueous analytes for trace detection.

Multifunctional 3D magnetic carbon aerogel for adsorption separation and highly sensitive SERS detection of malachite green.

This work presents a novel strategy for the synthesis of a recyclable aerogel and its multifunctional application as effective adsorption material for organic pollutants and as a high-quality SERS substrate for on-site detection measurement. Silver nanoparticles (Ag NPs) were uniformly dispersed and adsorbed on the surface of an Fe3C-loaded carbon aerogel, resulting in the formation of a three-dimensional Ag-Fe3C-MCA (magnetic carbon aerogel) composite. The substrate preparation led to Ag-Fe3C-MCA with a mesoporous structure for high adsorption capacity, together with magnetic properties for easy separation capability. The Ag-Fe3C-MCA composite demonstrated an efficient removal ability for malachite green (MG), with an adsorption capacity of 296.7 mg g-1. Moreover, Ag-Fe3C-MCA composite provided ultrasensitive surface-enhanced Raman scattering detection for MG molecules, obtaining a limit of detection (LOD) of 3 × 10-10 M. Aquaculture water samples with spiked MG concentrations were used to simulate practical scenarios. The Ag-Fe3C-MCA presented has a significant potential for the removal of hazardous residues in wastewater, together with an efficient and sensitive method of quantification, all on the same substrate.

Surfactant encapsulating N-doped carbon dots with enhanced optical properties as a selective sensor for Cr(VI) detection.

Detecting hexavalent chromium (Cr(VI)) is important for human health and environmental protection due to its high toxicity, carcinogenicity and persistence, but developing a sensor to selectively detect Cr(VI) remains challenging. Here, we proposed a selective fluorescent sensor for Cr(VI) detection using cetyltrimethylammonium chloride (CTAC) modified N-doped carbon dots (N-CDs-CTAC) synthesized via a post-modification strategy. Specifically, the introduced CTAC molecules could self-assemble into micelles for encapsulating fluorescent N-CDs, causing the aggregation of N-CD particles and then displaying enhanced fluorescence emission owing to the aggregation-induced emission effect. Moreover, the positively charged CTAC can interact with negatively charged Cr(VI) in the form of an anion (Cr2O72-), boosting the ability of the selective recognition of Cr(VI). Thus, a N-CDs-CTAC fluorescent probe was designed to selectively monitor Cr(VI) with an ultralow detection limit down to 40 nM, and was further used for Cr(VI) detection in real environmental samples. The fluorescence quenching mechanism of N-CDs-CTAC by Cr(VI) was attributed to dynamic quenching. The proposed assay opens an avenue for the selective detection of Cr(VI) in the environmental monitoring field.

Industrial wastewater source tracing: The initiative of SERS spectral signature aided by a one-dimensional convolutional neural network.

The spectral fingerprint is a significant concept in nontarget screening of environmental samples to direct identification efforts to relevant and important features. Surface-enhanced Raman scattering (SERS) has long been recognized as an optical method that can provide fingerprint-like chemical information at the single-molecule level. Here, the advanced one-dimensional convolutional neural network (1D-CNN) approach was applied to accurately identify the SERS spectral signature of industrial wastewaters for source tracing. A total of 66,000 SERS spectra were acquired from wastewaters of 22 factories across 10 industrial categories at three excitation wavelengths after data augmentation. The dataset was used to train a 1D-CNN model consisting of three convolutional layers to achieve adequate feature extraction of SERS spectra. As a proof-of-concept, multimixed wastewater samples were used to simulate practical pollution scenarios and evaluate the application potential of the model. The SERS-1D-CNN platform can identify the amount and factory information of wastewaters in multimixed samples, which achieves a recognition accuracy rate of 97.33%. The results suggest that even in a complex and unknown water environment, the 1D-CNN model can accurately identify industrial wastewaters in precollected datasets, exhibiting excellent potential in pollution source tracing.

Iodine-Modified Ag NPs for Highly Sensitive SERS Detection of Deltamethrin Residues on Surfaces.

It is essential to estimate the indoor pesticides/insecticides exposure risk since reports show that 80% of human exposure to pesticides occurs indoors. As one of the three major contamination sources, surface collected pesticides contributed significantly to this risk. Here, a highly sensitive liquid freestanding membrane (FSM) SERS method based on iodide modified silver nanoparticles (Ag NPs) was developed for quantitative detection of insecticide deltamethrin (DM) residues in solution phase samples and on surfaces with good accuracy and high sensitivity. The DM SERS spectrum from 500 to 2500 cm-1 resembled the normal Raman counterpart of solid DM. Similar bands at 563, 1000, 1165, 1207, 1735, and 2253 cm-1 were observed as in the literature. For the quantitative analysis, the strongest peak at 1000 cm-1 that was assigned to the stretching mode of the benzene ring and the deformation mode of C-C was selected. The peak intensity at 1000 cm-1 and the concentration of DM showed excellent linearity from 39 to 5000 ppb with a regression equation I = 649.428 + 1.327 C (correlation coefficient R2 = 0.991). The limit of detection (LOD) of the DM was found to be as low as 11 ppb. Statistical comparison between the proposed and the HPLC methods for the analysis of insecticide deltamethrin (DM) residues in solution phase samples showed no significant difference. DM residue analysis on the surface was mimicked by dropping DM pesticide on the glass surface. It is found that DM exhibited high residue levels up to one week after exposure. This proposed SERS method could find application in the household pesticide residues analysis.

Surface-enhanced Raman scattering integrating with machine learning for green tea storage time identification.

The rapid and accurate identification of complex samples still remains a great challenge, especially for those with similar compositions. In this work, we report an integration strategy consisting of surface-enhanced Raman scattering (SERS) and machine learning to discriminate complex and similar analytes, in this case green tea products with different storage times. Surface-functionalized Ag nanoparticles (NPs) were used as a SERS substrate to reveal the changes in the sensory components of green tea with variable storage time. Principal components analysis (PCA)-based support vector machine (SVM) classification was used to extract the key spectral features and identify green tea with different storage times. The results showed that such an integration strategy achieved high predictive accuracy on time tag discrimination for green tea. The multiclass SVM classifier successfully recognized green tea with different storage times at a prediction accuracy of 95.9%, sensitivity of 96.6%, and specificity of 98.8%. Therefore, this work illustrates that the SERS-based PCA-SVM platform might be a facile and reliable tool for the identification of complex matrices with subtle differentiations.

Ameliorating SERS Sensitivity for Pesticide Malathion Detection with Synergistic Boosting Effect by Hydrogen Cations and Chloride Anions.

Although SERS has been widely recognized as one of the highly sensitive analytical methods that can be deployed in the field with high sensitivity and short analysis time, reports regarding the fast determination of malathion at low concentrations are still scarce. Here, in this work, the solution pH and various halogen co-adsorbates were explored to promote the SERS signal of malathion using the citrate-reduced Ag NPs. It was found that chloride anions were the most efficient signal booster among the three halogen ions screened. Further examination of the SERS profile of the malathion in the presence of different halogen species found that the stretching mode of the P-S bond shifted to a lower frequency with Cl-, which may imply closer (and stronger) binding of malathion to the Ag NPs. This concurs with literature reports that halogen ions could facilitate the adsorption of a certain analyte onto the SERS substrate. In addition, hydrogen ions showed a synergistic effect on SERS signal enhancement when combined with chloride anions. At optimum conditions, the malathion could be detected with a limit of detection (LOD) of 3 ppb. Malathion-spiked cherry tomatoes and oranges were analyzed, and the recovery rates were found to be within 85-100%.

Halogen ion modified Ag NPs for ultrasensitive SERS detection of nitroaromatic explosives.

Trace explosive detection has become one of the hottest topics in scientific communities because homeland security is one of the top priorities for countries all around the world. In this work, Ag NPs prepared with different reducing reagents were modified with various halogen ions for the SERS detection of nitroaromatic explosives (2,4-DNT and 2,4,6-TNT). It was proposed that halogen ions probably replaced the surface adsorbates on Ag NPs, i.e., citrate ions, and gave surface access to target analytes, which in turn enhanced the SERS signal. The LOD values for TNT and 2,4-DNT were found to be only 2 femtomoles. Given its facile and the highly sensitive process, the method that we demonstrated can serve as a promising analytical technology for the ultrasensitive SERS detection of nitroaromatic explosives.

Halogen ion-modified silver nanoparticles for ultrasensitive surface-enhanced Raman spectroscopy detection of polycyclic aromatic hydrocarbons.

Rapid ultrasensitive detection of trace polycyclic aromatic hydrocarbons (PAHs) is essential and significant for pollution control due to their hazard, persistence, and the wide distribution in the environment. Therefore, rapid detection of PAHs is critical for controlling pollution and protecting the ecology. Considering the advantages of surface-enhanced Raman spectroscopy (SERS), a simple and reliable SERS method was proposed in this work for detecting PAHs in water. Three chemicals, namely NaCl, KBr, and KI, were chosen to modify Ag nanoparticles (NPs) for phenanthrene (Phe) detection, and Ag NPs modified with KBr (Ag-Br NPs) showed the best SERS response. The mixing sequence and the concentration of KBr were optimized. The addition order of mixing KBr and Ag NPs before Phe solution was the best sequence, and the optimal concentration of KBr was 20 mM. Under optimal conditions, the limits of quantification for Phe, pyrene (Pyr), and anthracene (Ant) were 10-6  M, 10-7  M, and 10-7  M, respectively. Mixed PAHs (Phe, Pyr, and Ant) in spiked water samples were identified and quantified successfully. The proposed method has good application prospects in environmental pollution monitoring.

A SERS pH sensor for highly alkaline conditions and its application for pH sensing in aerosol droplets.

Surface-Enhanced Raman Scattering (SERS) has been widely used in pH sensing. However, SERS sensors capable of stably analysing pH under highly alkaline conditions are still scarce. In this work, a SERS pH sensor employing Alizarin Yellow R as the molecular probe was carefully developed for strong alkaline solutions. The results showed that the probe presented excellent sensing performance in the pH range of 10.04-14.04, including desirable stability and reversibility. Raman band assignments of the probe molecules with the protonated and deprotonated forms were calculated using Gaussian 09. To demonstrate the application, we measured the centroid pH of the phosphate buffer (PB) droplet and compared it to the value obtained with 4-mercaptobenzoic acid (4-MBA) as a probe.

Assessing the effect of different pH maintenance situations on bacterial SERS spectra.

Phenotyping of bacteria with vibrational spectroscopy has caught much attention in bacteria-related research. It is known that many factors could affect this process. Among them, solution pH maintenance is crucial, yet its impact on the bacterial SERS spectra is surprisingly neglected. In this work, we focused on two situations related to pH maintenance: the effect of the same buffer on the SERS spectra of bacteria under different pH values, and the influence of different buffers on the SERS spectra of bacteria under the same pH value. Specifically, Britton-Robison (BR) buffer was used to evaluate the effect of pH value on bacteria SERS spectra thanks to its wide pH range. Four different buffers, namely BR buffer, acetate buffer, phosphate buffer, and carbonate buffer, were used to illustrate the impact of buffer types on SERS spectra of bacteria. The results showed that the intensity and number of characteristic peaks of the SERS spectra of Gram-negative (G -) bacteria changed more significantly than Gram-positive (G +) bacteria with the change of pH value. Furthermore, compared with phosphate buffer and carbonate buffer, BR buffer could bring more characteristic SERS bands with better reproducibility, but slightly inferior to acetate buffer. In conclusion, the influence of the pH and types of the buffer on the SERS spectra of bacteria are worthy of further discussion.

Boosting bacteria differentiation efficiency with multidimensional surface-enhanced Raman scattering: the example of Bacillus cereus.

Surface-enhanced Raman scattering (SERS) is a powerful tool for constructing biomolecular fingerprints, which play a vital role in differentiation of bacteria. Due to the rather subtle differences in the SERS spectra among different bacteria, artificial intelligence is usually adopted and enormous amounts of spectral data are required to improve the differentiation efficiency. However, in many cases, large volume data acquisition on bacteria is not only technical difficult but labour intensive. It is known that surface modification of SERS nanomaterials can bring additional dimensionality (difference) of the SERS fingerprints. Here in this work, we show that the concept could be used to improve the bacteria differentiation efficiency. Ag NPs were modified with 11-mercaptoundecanoic acid, 11-mercapto-1-undecanol, and 1-dodecanethiol to provide additional dimensionality. The modified NPs then were mixed with cell lysate from different strains of Bacillus cereus (B. cereus). Even by applying a simple PCA process to the resulting SERS spectra data, all the three modified Ag NPs showed superior differentiation results compared with bare Ag NPs, which could only separate Staphylococcus aureus (S. aureus) and B. cereus. It is believed that the multidimensional SERS could find great potential in bacteria differentiation.

Phenotyping Bacteria through a Black-Box Approach: Amplifying Surface-Enhanced Raman Spectroscopy Spectral Differences among Bacteria by Inputting Appropriate Environmental Stress.

Surface-enhanced Raman spectroscopy (SERS) stands out in the field of microbial analysis due to its rich molecular information, fast analysis speed, and high sensitivity. However, achieving strain-level differentiation is still challenging because numerous bacterial species inevitably have very similar SERS profiles. Here, a method inspired by the black-box theory was proposed to boost the spectral differences, where the undifferentiated bacteria was considered as a type of black-box, external environmental stress was used as the input, and the SERS spectra of bacteria exposed to the same stress was output. For proof of the concept, three types of environmental stress were explored, i.e., ethanol, ultraviolet light (UV), and ultrasound. Enterococcus faecalis (E. faecalis) and three types of Escherichia coli (E. coli) were all subjected to the stimuli (stress) before SERS measurement. Then the collected spectra were processed only by simple principal component analysis (PCA) to achieve differentiation. The results showed that appropriate stress was beneficial to increase the differences in bacterial SERS spectra. When sonication at 490 W for 60 s was used as the input, the optimal differentiation of bacteria at the species (E. faecalis and E. coli) and strain-level (three E. coli) can be achieved.

Fluorescence immunoassay rapid detection of 2019-nCoV antibody based on the fluorescence resonance energy transfer between graphene quantum dots and Ag@Au nanoparticle.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which has dramatically changed the world, is a highly contagious virus. The timely and accurate diagnosis of SARS-CoV-2 infections is vital for disease control and prevention. Here in this work, a fluorescence immunoassay was developed to detect 2019 Novel Coronavirus antibodies (2019-nCoV mAb). Fluorescent graphene quantum dots (GQDs) and Ag@Au nanoparticles (Ag@AuNPs) were successfully synthesized and characterized. Fluorescence resonance energy transfer (FRET) enables effective quenching of GQDs fluorescence by Ag@AuNPs. With the presence of 2019-nCoV mAb, a steric hindrance was observed between the Ag@AuNPs-NCP (2019-nCoV antigen) complex and GQDs, which reduced the FRET efficiency and restored the fluorescence of GQDs. The fluorescence enhancement efficiency has a satisfactory linear relationship with the logarithm of the 2019-nCoV mAb in a concentration range of 0.1 pg mL-1-10 ng mL-1, and the limit of detection was 50 fg mL-1. The method has good selectivity. When the serum sample was spiked with 2019-nCoV mAb, the recovery rate was between 90.8% and 103.3%. The fluorescence immunosensor demonstrates the potential to complement the existing serological assays for COVID-19 diagnosis.

Highly sensitive SERS detection of residual nitrofurantoin and 1-amino-hydantoin in aquatic products and feeds.

Nitrofurantoin (NFT), a typical highly effective nitrofuran antibiotic drug, has been prohibited but still widely found in animal food products. It can be metabolized in animals to form 1-amino-hydantoin (AHD) that can then form stable and toxic metabolite-protein adducts. Hence, the detection of NFT and AHD in aquatic products and feeds is very important. However, there are limited reports concerning NFT detection and none about AHD by using surface-enhanced Raman spectroscopy (SERS) method. Herein, potassium bromide (KBr) decorated silver (Ag) nanoparticles (Ag-BrNPs)-based SERS approach was proposed for NFT and AHD detection. The limit of detection (LOD) for NFT was 1 µg/L. The detection of NFT residues in sea cucumber and fish feeds was also realized with the LOD of 1 and 50 ng/g, respectively. More importantly, the sensing of AHD was easily realized with the SERS approach for the first time. After the derivatization with 2-nitrobenzaldehyde (2-NBA), Ag-BrNPs were also successfully utilized for AHD detection in sea cucumber with the LOD of 5 ng/g.

Quantitative detection of 6-thioguanine in body fluids based on a free-standing liquid membrane SERS substrate.

The adverse reactions caused by 6-thioguanine (6-TG) in anti-cancer treatment are closely related to the dose, leading to the urgent need for clinical monitoring of its concentration. In this work, a highly reproducible free-standing liquid membrane (FLM) surface-enhanced Raman spectroscopy (SERS) substrate was developed to detect 6-TG in human urine and serum quantitatively. Briefly, a prepared sample was adjusted to pH 2 and mixed with concentrated core-shell bimetallic nanoparticle (AgcoreAushell NP) suspension. The Au/Ag ratio of the AgcoreAushell NPs was optimized. Then the mixture was formed into an FLM using a custom mold. The relative standard deviation (RSD) of the experimental results can be stabilized below 10% (n ≥ 10). The R2 of the calibration curve in the range of 10 ~ 100 µg kg-1 was 0.988. In addition, the limit of detection (LOD) (3σ/k) of 6-TG was 5 µg kg-1. The FLM SERS platform has been successfully applied to the rapid and reliable analysis of 6-TG spiked in body fluids.