Role of Functional Groups in Tuning Luminescence Signature of Solution-Processed Graphene Quantum Dots: Experimental and Theoretical Insights.

Zero-dimensional graphene quantum dots (GQDs) present unique optoelectronic properties in the large-spectrum range from UV to visible. However, the origin of luminescence in GQDs is still a debatable question. Therefore, the present work investigates the features of trap-mediated and edge-state-functionalized group-associated luminescence enhancement of GQDs. The attached functional groups' involvement in the upsurge of photoluminescence has been discussed theoretically as well as experimentally. In addition, the role of the aromatic ring, the functional group attached, and their positions of attachment to the aromatic ring to tune the emission wavelength and Raman modes have been elucidated theoretically as well as experimentally. We found that in the case of the -OH group attached outside of the aromatic ring, the long-range π hybridization dominates, which suggests that the emission from this model can be dictated by long-range π hybridization. In particular, we found that oxygen-containing functional groups attached outside of the aromatic ring are the main source of the luminescence signature in GQDs. Furthermore, density functional theory (DFT) indicates that the -OH functional group attached outside of the aromatic ring perfectly matched with our experimental results, as the experimental bandgap (2.407 eV) is comparable with the theoretical simulated bandgap (2.399 eV) of the -OH group attached outside of the aromatic ring.

ZnO@Ag-Functionalized Paper-Based Microarray Chip for SERS Detection of Bacteria and Antibacterial and Photocatalytic Inactivation.

Bacterial infections caused by pathogenic microorganisms have become a serious, widespread health concern. Thus, it is essential and required to develop a multifunctional platform that can rapidly and accurately determine bacteria and effectively inhibit or inactivate pathogens. Herein, a microarray SERS chip was successfully synthesized using novel metal/semiconductor composites (ZnO@Ag)-ZnO nanoflowers (ZnO NFs) decorated with Ag nanoparticles (Ag NPs) arrayed on a paper-based chip as a supporting substrate for in situ monitoring and photocatalytic inactivation of pathogenic bacteria. Typical Gram-positive Staphylococcus aureus and Gram-negative Escherichia coli and Vibrio parahemolyticus were selected as models. Partial least-squares discriminant analysis (PLS-DA) was performed to minimize the dimensionality of SERS spectra data sets and to develop a cost-effective identification model. The classification accuracy was 100, 97.2, and 100% for S. aureus, E. coli, and V. parahemolyticus, respectively. The antimicrobial activity of ZnO@Ag was proved by the microbroth dilution method, and the minimum inhibitory concentrations (MICs) of S. aureus, E. coli, and V. parahemolyticus were 40, 50, and 55 µg/mL, respectively. Meanwhile, it demonstrated remarkable photocatalytic performance under natural sunlight for the inactivation of pathogenic bacteria, and the inactivation rates for S. aureus, E. coli, and V. parahemolyticus were 100, 97.03 and 97.56%, respectively. As a result, the microarray chip not only detected the bacteria with high sensitivity but also confirmed the antibacterial and photocatalytic sterilization properties. Consequently, it offers highly prospective strategies for foodborne diseases caused by pathogenic bacteria.

Advances in surface-enhanced Raman spectroscopy technology for detection of foodborne pathogens.

Rapid control and prevention of diseases caused by foodborne pathogens is one of the existing food safety regulatory issues faced by various countries and has received wide attention from all sectors of society. The development of rapid and reliable detection methods for foodborne pathogens remains a hot research area for food safety and public health because of the limitations of complex steps, time-consuming, low sensitivity, or poor selectivity of commonly used methods. Surface-enhanced Raman spectroscopy (SERS), as a novel spectroscopic technique, has the advantages of high sensitivity, selectivity, rapid and nondestructive detection and has exhibited broad application prospects in the determination of pathogenic bacteria. In this study, the enhancement mechanisms of SERS are briefly introduced, then the characteristics and properties of liquid-phase, rigid solid-phase, and flexible solid-phase are categorized. Furthermore, a comprehensive review of the advances in label-free or label-based SERS strategies and SERS-compatible techniques for the detection of foodborne pathogens is provided, and the advantages and disadvantages of these methods are reviewed. Finally, the current challenges of SERS technology applied in practical applications are listed, and the possible development trends of SERS in the field of foodborne pathogens detection in the future are discussed.

SERS Sensors Based on Aptamer-Gated Mesoporous Silica Nanoparticles for Quantitative Detection of Staphylococcus aureus with Signal Molecular Release.

This work describes a simple and novel biosensor for the quantitative determination of Staphylococcus aureus (S. aureus) based on target-induced release of signal molecules from aptamer-gated aminated mesoporous silica nanoparticles (MSNs) coupled with surface-enhanced Raman scattering (SERS) technology. MSNs were synthesized and then modified with amino groups by (3-aminopropyl) triethoxysilane to make them positively charged. Next, signal molecules (4-aminothiophenol, 4-ATP) were loaded into the pores of MSNs. Then, negatively charged aptamers of S. aureus were assembled on the surface of MSNs through electrostatic interactions. Upon the addition of S. aureus, the assembled aptamers were specifically bound to the bacteria. Consequently, the "gates" were opened, resulting in the release of 4-ATP from the pores of MSNs. The released molecules were measured by a Raman spectrometer, and the intensity of 4-ATP at 1071 cm-1 was linearly related to the S. aureus concentration. A silver nanoflower silica core-shell structure (Ag NFs@SiO2) was prepared and it served as a SERS substrate. Under optimized experimental conditions, a good linear relationship (y = 2107.93 + 1536.30x, R2 = 0.9956) in the range from 4.7 × 10 to 4.7 × 108 cfu/mL was observed with a limit of detection of 17 cfu/mL. The method was successfully applied for the analysis of S. aureus in fish samples and the recovery rate was 91.3-109%.

Green synthesis of silver and gold nanoparticles using Crataegus oxyacantha extract and their urease inhibitory activities.

This study reports the green synthesis and urease inhibitory activities of Ag and Au nanoparticles (NPs) using Crataegus oxyacantha extract. The synthesized NPs were characterized by UV-visible, FT-IR spectroscopy, atomic force microscopy, and scanning electron microscopy. The obtained NPs were spherical in shape, and their size was around 85 nm. A strong correlation between the phytochemicals present in the extract and their capability for the synthesis of NPs was observed. Furthermore, the shape, size, stability, and bioactivity of the NPs were strongly influenced by the stabilizing phytochemicals. The experimental analysis suggested that these NPs have substantial stability in a diverse range of physiological conditions such as pH, salinity, and temperature. The NPs exhibited potent urease enzyme inhibitory activities with percent inhibition of 99.25 and IC50 value of 1.38 ± 0.3, comparable to the standard (thiourea percent inhibition, that is, 98.2% and IC50 value 5.3 ± 0.04). These results suggested that the proposed NPs could be used in the homeopathic and pharmaceutical industries for biomedical applications.

SERS-based lateral flow immunoassay for sensitive and simultaneous detection of anti-SARS-CoV-2 IgM and IgG antibodies by using gap-enhanced Raman nanotags.

The lateral flow immunoassay (LFIA) has played a crucial role in early diagnosis during the current COVID-19 pandemic owing to its simplicity, speed and affordability for coronavirus antibody detection. However, the sensitivity of the commercially available LFIAs needs to be improved to better prevent the spread of the infection. Here, we developed an ultra-sensitive surface-enhanced Raman scattering-based lateral flow immunoassay (SERS-based LFIA) strip for simultaneous detection of anti-SARS-CoV-2 IgM and IgG by using gap-enhanced Raman nanotags (GERTs). The GERTs with a 1 nm gap between the core and shell were used to produce the "hot spots", which provided about 30-fold enhancement as compared to conventional nanotags. The COVID-19 recombinant antigens were conjugated on GERTs surfaces and replaced the traditional colloidal gold for the Raman sensitive detection of human IgM and IgG. The LODs of IgM and IgG were found to be 1 ng/mL and 0.1 ng/mL (about 100 times decrease was observed as compared to commercially available LFIA strips), respectively. Moreover, under the condition of common nano-surface antigen, precise SERS signals proved the unreliability of quantitation because of the interference effect of IgM on IgG.

Nickel nanoparticles induce hepatotoxicity via oxidative and nitrative stress-mediated apoptosis and inflammation.

Nickel nanoparticles (Ni NPs) are utilized extensively in various industrial applications. However, there are increasing concerns about potential exposure to Ni NPs and consequent health effects. The aim of this study was to assess Ni NPs-induced liver toxicity in Sprague Dawley rats. Twenty-five rats were exposed to Ni NPs via intraperitoneal injection at doses of 15, 30, and 45 mg/kg per body weight for 28 days. Results from ICP-MS analysis showed an increase in the concentration of Ni NPs in a dose-dependent manner. The liver dysfunction was indicated by considerable production of ALT, AST, ALP, LDH, and TB in Ni NPs-treated rats. Histological examination demonstrated liver injuries (inflammatory cells, congestion, necrosis, and pyknosis) in exposed rats with dose-dependent severity of pathologies by semi-quantitative histograding system. To explore the toxicological pathways, we examined oxidative stress biomarkers and detected Ni NPs significantly elevated the levels of MDA and LPO while decreasing the levels of CAT and GSH. All the changes in biomarkers were recorded in a dose-dependent relationship. In addition, we found upregulated NF-kß indicating activation of inflammatory cytokines. ELISA results of serum revealed a remarkable increase of nitrative stress markers (iNOS and NO), ATPase activity, inflammatory cytokine (IL-6, IL-1ß, and TNF-α), and apoptotic mediators (caspase-3 and caspase-9) in Ni NPs-treated groups than the control. In summary, the result of this study provided evidence of hepatotoxicity of Ni NPs and insightful information about the involved toxic pathways, which will help in health risk assessment and management, related preventive measures for the use of Ni-NPs materials.

Recent progress on graphene quantum dots-based fluorescence sensors for food safety and quality assessment applications.

The versatile photophysicalproperties, high surface-to-volume ratio, superior photostability, higher biocompatibility, and availability of active sites make graphene quantum dots (GQDs) an ideal candidate for applications in sensing, bioimaging, photocatalysis, energy storage, and flexible electronics. GQDs-based sensors involve luminescence sensors, electrochemical sensors, optical biosensors, electrochemical biosensors, and photoelectrochemical biosensors. Although plenty of sensing strategies have been developed using GQDs for biosensing and environmental applications, the use of GQDs-based fluorescence techniques remains unexplored or underutilized in the field of food science and technology. To the best of our knowledge, comprehensive review of the GQDs-based fluorescence sensing applications concerning food quality analysis has not yet been done. This review article focuses on the recent progress on the synthesis strategies, electronic properties, and fluorescence mechanisms of GQDs. The various GQDs-based fluorescence detection strategies involving Förster resonance energy transfer- or inner filter effect-driven fluorescence turn-on and turn-off response mechanisms toward trace-level detection of toxic metal ions, toxic adulterants, and banned chemical substances in foodstuffs are summarized. The challenges associated with the pretreatment steps of complex food matrices and prospects and challenges associated with the GQDs-based fluorescent probes are discussed. This review could serve as a precedent for further advancement in interdisciplinary research involving the development of versatile GQDs-based fluorescent probes toward food science and technology applications.

SERS-based rapid detection of 2,4-dichlorophenoxyacetic acid in food matrices using molecularly imprinted magnetic polymers.

In order to remove the limitations of natural antibodies or enzymes, a nano-magnetic biomimetic platform based on a surface-enhanced Raman scattering (SERS) sensor has been developed for highly sensitive capture and detection of 2,4-dichlorophenoxyacetic acid (2,4-D) in food and water samples. Magnetic-based molecular imprinted polymer nanoparticles (Mag@MIP NPs) were constructed to capture the target 2,4-D molecule via biomimetic recognition, and gold nanoparticles (Au NPs) served as SERS-based probes, which are bound to the Mag@MIP NPs by electrostatic adsorption. The as-prepared SERS-MIP sensor for sensing of 2,4-D achieved a good linear relationship with a low detection limit (LOD) of 0.00147 ng/mL within 2 h and exhibited high sensitivity. The sensor was successfully applied to detect 2,4-D in milk and tap water and achieved good recoveries ranging from 93.5 to 102.2%. Moreover, the designed sensor system exhibited satisfactory results (p > 0.05) compared to HPLC by validation analysis. Hence, the findings demonstrated that the proposed method has significant potential for practical application in food safety and environmental protection. Graphical abstract .

Recent Advancements and Unexplored Biomedical Applications of Green Synthesized Ag and Au Nanoparticles: A Review.

Green synthesis of silver (Ag) and gold (Au) nanoparticles (NPs) has acquired huge popularity owing to their potential applications in various fields. A large number of research articles exist in the literature describing the green synthesis of Ag and Au NPs for biomedical applications. However, these findings are scattered, making it time-consuming for researchers to locate promising advancements in Ag and Au NPs synthesis and their unexplored biomedical applications. Unlike other review articles, this systematic study not only highlights recent advancements in the green synthesis of Ag and Au NPs but also explores their potential unexplored biomedical applications. The article discusses the various synthesis approaches for the green synthesis of Ag and Au NPs highlighting the emerging developments and novel strategies. Then, the article reviews the important biomedical applications of green synthesized Ag and Au NPs by critically evaluating the expected advantages. To expose future research direction in the field, the article describes the unexplored biomedical applications of the NPs. Finally, the articles discuss the challenges and limitations in the green synthesis of Ag and Au NPs and their biomedical applications. This article will serve as a valuable reference for researchers, working on green synthesis of Ag and Au NPs for biomedical applications.

Calibration transfer via filter learning.

BACKGROUND: Calibration transfer is an essential activity in analytical chemistry in order to avoid a complete recalibration. Currently, the most popular calibration transfer methods, such as piecewise direct standardization and dynamic orthogonal projection, require a certain amount of standard or reference samples to guarantee their effectiveness. To achieve higher efficiency, it is desirable to perform the transfer with as few reference samples as possible. RESULTS: To this end, we propose a new calibration transfer method by using a calibration database from a master instrument (source domain) and only one spectrum with known properties from a slave instrument (target domain). We first generate a counterpart of this spectrum in the source domain by a multivariate Gaussian kernel. Then, we train a filter to make the response function of the slave instrument equivalent to that of the master instrument. To avoid the need for labels from the target domain, we also propose an unsupervised way to implement our method. Compared with several state-of-the-art methods, the results on one simulated dataset and two real-world datasets demonstrate the effectiveness of our method. SIGNIFICANCE: Traditionally, the demand for certain amounts of reference samples during calibration transfer is cumbersome. Our approach, which requires only one reference sample, makes the transfer process simple and fast. In addition, we provide an alternative for performing unsupervised calibration transfer. As such, the proposed method is a promising tool for calibration transfer.

Automatic detection of ischemic necrotic sites in small intestinal tissue using hyperspectral imaging and transfer learning.

Acquiring large amounts of hyperspectral data of small intestinal tissue with real labels in the clinic is difficult, and the data shows inter-patient variability. Building an automatic identification model using a small dataset presents a crucial challenge in obtaining a strong generalization of the model. This study aimed to explore the performance of hyperspectral imaging and transfer learning techniques in the automatic identification of normal and ischemic necrotic sites in small intestinal tissue. Hyperspectral data of small intestinal tissues were collected from eight white rabbit samples. The transfer component analysis (TCA) method was performed to transfer learning on hyperspectral data between different samples and the variability of data distribution between samples was reduced. The results showed that the TCA transfer learning method improved the accuracy of the classification model with less training data. This study provided a reliable method for single-sample modelling to detect necrotic sites in small intestinal tissue .

Ag@Au core-shell nanoparticle-based surface-enhanced Raman scattering coupled with chemometrics for rapid determination of chloramphenicol residue in fish.

The alarming increase in drug-resistant bacteria in fish resulting from the misuse of antibiotics poses a significant threat to ecosystems and human health. Therefore, the development of a reliable approach for detecting antibiotic residues in fish is crucial. In this study, a rapid and simple method for detecting chloramphenicol (CAP) residue in tilapia was developed using surface-enhanced Raman scattering (SERS) combined with chemometric algorithms. Silver and gold core-shell nanoparticles (Ag@Au CSNPs) were used as SERS nanosensors to achieve strong signal amplification with an enhancement factor of 2.67 × 106. The results demonstrated that the variable combination population analysis-partial least square (VCPA-PLS) model combined with the standard normal variable transformation pretreatment method exhibited the best predictive performance with a detection limit of 1 × 10-5 µg/mL. Thus, an SERS technique was established based on Ag@Au CSNPs combined with VCPA-PLS to rapidly detect CAP in tilapia.

Hyperspectral imaging combined with blood oxygen saturation for in vivo analysis of small intestinal necrosis tissue.

Acute mesenteric ischemia (AMI) is a clinically significant vascular and gastrointestinal condition, which is closely related to the blood supply of the small intestine. Unfortunately, it is still challenging to properly discriminate small intestinal tissues with different degrees of ischemia. In this study, hyperspectral imaging (HSI) was used to construct pseudo-color images of oxygen saturation about small intestinal tissues and to discriminate different degrees of ischemia. First, several small intestine tissue models of New Zealand white rabbits were prepared and collected their hyperspectral data. Then, a set of isosbestic points were used to linearly transform the measurement data twice to match the reference spectra of oxyhemoglobin and deoxyhemoglobin, respectively. The oxygen saturation was measured at the characteristic peak band of oxyhemoglobin (560 nm). Ultimately, using the oxygenated hemoglobin reflectance spectrum as the benchmark, we obtained the relative amount of median oxygen saturation in normal tissues was 70.0 %, the IQR was 10.1 %, the relative amount of median oxygen saturation in ischemic tissues was 49.6 %, and the IQR was 14.6 %. The results demonstrate that HSI combined with the oxygen saturation computation method can efficiently differentiate between normal and ischemic regions of the small intestinal tissues. This technique provides a powerful support for internist to discriminate small bowel tissues with different degrees of ischemia, and also provides a new way of thinking for the diagnosis of AMI.

Identification of near-infrared characteristic bands of small bowel necrosis based on cellwise detection algorithm.

The near-infrared spectroscopy is often used to distinguish small bowel necrosis due to necrotizing enterocolitis (NEC). The characteristic bands of small bowel necrosis, as an important basis for evaluating the confidence of the differentiation results, are challenging to identify quickly. In this study, we proposed to identify characteristic bands of lesion samples based on hyperspectral imaging (HSI) and cellwise outlier detection. Rabbits were used as an animal model to simulate the clinical symptoms of NEC. The rabbits were detected at intervals of 10, 30, 60, and 90 min. The characteristic bands were identified within the same rabbit, between different rabbits and at different times. The result showed the bands near 763 nm, corresponding to the absorption peak of deoxyhemoglobin, were the characteristic bands separating samples with NEC. The identification result was plausible because hypoxia was the main cause of NEC. The method was easy to perform.

Facile synthesis of fluorescence-SERS dual-probe nanocomposites for ultrasensitive detection of sulfur-containing gases in water and beer samples.

A highly structured fluorescent-SERS dual-probe nanocomposites were synthesized for the determination of sulfur-containing gases in water and beer samples. Initially, Au@Ag NPs were prepared by growing the Ag shell on the Au core in situ, modified with surfactant and fabricated with Zn2+. Then, MOF-5-NH2 assembled Au@Ag NPs were obtained through coordination between Zn sites and 2-aminoterephthalic acid. The principle was based on redox reaction between H2S and Au@Ag NPs, and the fluorescence turn-on effects were due to the charge transfer between SO2 and amino groups. The SERS intensity was related to the concentration of H2S (5 âˆ¼ 60 nM), and an ultra-low detection limit of 2.26 nM was achieved. Importantly, the fluorescence performance was applied for SO2 analysis and exhibited good linear response. Moreover, the platform for H2S and SO2 in real samples revealed satisfactory results (95.6 âˆ¼ 101.6% and 99.0 âˆ¼ 104.4%). Therefore, the proposed system offered a precise detection of H2S/SO2 in food/environmental settings.

Simultaneous quantitation of free fatty acid in rice by synergetic data fusion of colorimetric sensor arrays, NIR, and MIR spectroscopy.

This study evaluated the feasibility of colorimetric sensor array (CSA), near-infrared (NIR) and mid-infrared (MIR) spectroscopy for quantitation of free fatty acids in rice using data fusion. Purposely, different data sets of low-level (CSA-NIRLL, CSA-MIRLL, and NIR-MIRLL) and mid-level (CSA-NIRML, CSA-MIRML, and NIR-MIRML) fusion were adopted to enhance the statistical parameters. The model performance was evaluated using coefficient of determination for prediction, (R2p), root mean square error of prediction (RMSEP) and residual predictive deviation (RPD). Synergetic low-level and mid-level fusion model yielded 0.7707 ≤ R2p ≤ 0.8275, 14.4 ≤ RMSEP ≤ 16.3 and 2.19 ≤ RPD ≤ 2.48; and 0.7788 ≤ R2p ≤ 0.8571, 12.4 ≤ RMSEP ≤ 16.8 and 2.12 ≤ RPD ≤ 2.88, respectively. The CSA-NIRML model delivered an optimal performance for prediction of free fatty acid. The integration of CSA, NIR and MIR was feasible and could improve the prediction accuracy of free fatty acids in rice.

Antimicrobial efficacy of Mentha piperata-derived biogenic zinc oxide nanoparticles against UTI-resistant pathogens.

Misuse of antibiotics leads to the worldwide spread of antibiotic resistance, which motivates scientists to create new antibiotics. The recurring UTI due to antibiotics-resistant microorganism's challenges scientists globally. The biogenic nanoparticles have the potential to meet the escalating requirements of novel antimicrobial agents. The green synthesis of nanoparticles (NPs) gained more attention due to their reliable applications against resistant microbes. The current study evaluates the biogenic ZnO NPs of Mentha piperata extract against resistant pathogens of urinary tract infections by agar well diffusion assay. The biogenic ZnO NPs revealed comparatively maximum inhibition in comparison to synthetic antibiotics against two bacterial strains (Proteus mirabilis, Pseudomonas aeruginosa) and a fungal strain (Candida albicans).The synthesized biogenic ZnO NPs alone revealed maximum activities than the combination of plant extract (PE) and ZnO NPs, and PE alone. The physiochemical features of ZnO NPs characterized through UV-Vis spectroscopy, FTIR, XRD, SEM, and EDX. The UV-Vis spectroscopy revealed 281.85 nm wavelengths; the XRD pattern revealed the crystalline structure of ZnO NPs. The FTIR analysis revealed the presence of carboxylic and nitro groups, which could be attributed to plant extract. SEM analysis revealed spherical hollow symmetry due to electrostatic forces. The analysis via EDX confirmed the presence of Zn and oxygen in the sample. The physiochemical features of synthesized ZnO NPs provide pivotal information such as quality and effectiveness. The current study revealed excellent dose-dependent antimicrobial activity against the pathogenic isolates from UTI-resistant patients. The higher concentration of ZnONPs interacts with the cell membrane which triggers oxidative burst. They may bind with the enzymes and proteins and brings epigenetic alteration which leads to membrane disruption or cell death.

A robust method to improve the regression accuracy of LIBS data: determination of heavy metal Cu in Tegillarca granosa.

Tegillarca granosa (T. granosa) is susceptible to contamination by heavy metals, which poses potential health risks for consumers. Laser-induced breakdown spectroscopy (LIBS) combined with the classical partial least squares (PLS) model has shown promise in determining heavy metal concentrations in T. granosa. However, the presence of outliers during calibration can compromise the model's integrity and diminish its predictive capabilities. To address this issue, we propose using a robust method for partial least squares, RSIMPLS, to improve the accuracy of Cu prediction in T. granosa. The RSIMPLS algorithm was employed to analyze and process the high-dimensional LIBS data and utilized diagnostic plots to identify various types of outliers. By selectively eliminating certain outliers, a robust calibration method was achieved. The results showed that LIBS spectroscopy has the potential to predict Cu in T. granosa, with a coefficient of determination (Rp2) of 0.79 and a root mean square error of prediction (RMSEP) of 11.28. RSIMPLS significantly improved the prediction accuracy of Cu concentrations with a 43% decrease in RMSEP compared to the PLS. These findings validated the effectiveness of combining LIBS data with the RSIMPLS algorithm for the prediction of Cu concentrations in T. granosa.

Recent Advances in Silver and Gold Nanoparticles-Based Colorimetric Sensors for Heavy Metal Ions Detection: A Review.

Silvetr and gold nanoparticles-based colorimetric sensors (Ag/Au-NPs-CSns) allow potential prospects for the development of efficient sensors owing to their unique shape- and size-dependent optical properties. In this review, recent (2020) advances in morphology-controllable synthesis, shape/size-dependent performance, sensing mechanism, challenges and prospects of Ag/Au-NPs-CSns for the detection of heavy metals are discussed. The size/shape-controlled synthesis of innovative Ag/Au-NPs-CSns is reviewed critically and the possible role of different parameters like temperature, time, pH, stabilizing/capping agents, reducing agents and concentration/nature of precursors are discussed. Then, we highlighted how the shape, size, optimum composition, functionalization, stabilizing/capping agents, surface structure and synergism influence the optical properties and sensing efficiency of Ag/Au-NPs-CSns. This review attempted to accentuate the efficiency of novel multimetallic Ag/AuNPs-CSns as compare to their monometallic counterparts and explained how the incorporation of multi-metals affects their performance. Besides, the sensing mechanisms of Ag/Au-NPs-CSns with special reference to recently published studies are discussed. Finally, challenges and prospects in the controllable-synthesis and practical applications of these sensors are studied. This mechanistic approach and timely review can be promisingly considered as a valuable reference and will help fuel new ideas for the development of novel colorimetric sensors. HighlightsA review of recent advances in Ag/Au-NPs-CSns for heavy metal ions detectionMorphology of Ag/Au-NPs-CSns regulate their optical properties/sensing efficiencyPromising Ag/Au-NPs-CSns can be synthesized by adjusting experimental parametersHybrid and functionalized Ag/Au-NPs-CSns have superior sensing performanceSize/shape transformation, aggregation/anti- and oxidation are sensing mechanisms.