Search Results (130)

The lateral flow immunoassay (LFIA) is a widely utilized, rapid diagnostic technique characterized by its short analysis duration, cost efficiency, visual result interpretation, portability and suitability for point-of-care applications. However, conventional LFIAs have limited sensitivity, a challenge that can be overcome by the introduction of gold nanoparticles, which provide enhanced sensitivity and selectivity (compared, for example, to latex beads or carbon nanoparticles) for the detection of target analytes, due to their optical properties, chemical stability and ease of functionalization. In this work, gold nanoparticle-based LFIAs are developed for the detection of aflatoxin B1, and the relative performance of different morphology particles is evaluated. LFIA using gold nano-labels allowed for aflatoxin B1 detection over a range of 0.01 ng/mL–100 ng/mL. Compared to spherical gold nanoparticles and gold nano-flowers, star-shaped gold nanoparticles show increased antibody binding efficiency of 86% due to their greater surface area. Gold nano-stars demonstrated the highest sensitivity, achieving a limit of detection of 0.01ng/mL, surpassing the performance of both spherical gold nanoparticles and gold nano-flowers. The use of star-shaped particles as nano-labels has demonstrated a five-fold improvement in sensitivity, underscoring the potential of integrating diverse nanostructures into LFIA for significantly improving analyte detection. Moreover, the robustness and feasibility of gold nano-stars employed as labels in LFIA was assessed in detecting aflatoxin B1 in a wheat matrix. Improved sensitivity with gold nano-stars holds promise for applications in food safety monitoring, public health diagnostics and rapid point-of-care diagnostics. This work opens the pathway for further development of LFIA utilizing novel nanostructures to achieve unparallel precision in diagnostics and sensing. Full article

Real-time, high-resolution monitoring of chemically diverse water pollutants remains a critical challenge for smart water management. Here, we report a fully integrated, multi-modal nano-sensor array, combining graphene field-effect transistors, Ag/Au-nanostar surface-enhanced Raman spectroscopy substrates, and CdSe/ZnS quantum dot fluorescence, coupled to an edge-deployable CNN-LSTM architecture that fuses raw electrochemical, vibrational, and photoluminescent signals without manual feature engineering. The 45 mm × 20 mm microfluidic manifold enables continuous flow-through sampling, while 8-bit-quantised inference executes in 31 ms at <12 W. Laboratory calibration over 28,000 samples achieved limits of detection of 12 ppt (Pb²⁺), 17 pM (atrazine) and 87 ng L⁻¹ (nanoplastics), with R² ≥ 0.93 and a mean absolute percentage error <6%. A 24 h deployment in the Cherwell River reproduced natural concentration fluctuations with field R² ≥ 0.92. SHAP and Grad-CAM analyses reveal that the network bases its predictions on Dirac-point shifts, characteristic Raman bands, and early-time fluorescence-quenching kinetics, providing mechanistic interpretability. The platform therefore offers a scalable route to smart water grids, point-of-use drinking water sentinels, and rapid environmental incident response. Future work will address sensor drift through antifouling coatings, enhance cross-site generalisation via federated learning, and create physics-informed digital twins for self-calibrating global monitoring networks. Full article

Herein, a novel, highly efficient electrochemical adsorption method is introduced for detection of the potent anti-inflammatory synthetic corticosteroid, dexamethasone (DEX). Unlike conventional electrochemical techniques that rely on high reduction potentials, the proposed sensor offers an alternative adsorption-based mechanism with a gold nanostar-modified glassy carbon electrode (AuNS|GCE). This enables DEX detection at a less negative or moderate reduction potential of +200 mV, circumventing potential window limitations of a GCE and providing a suitable microenvironment for detection in biological media. DEX is known to effectively prevent or suppress symptoms of inflammation due to its small applied dosage; however, an overdose thereof in the human body could lead to adverse drug effects such as gastrointestinal perforation, seizures, and heart attacks. Therefore, a sensitive method is essential to monitor DEX concentration in biofluids such as urine. NMGA-capped AuNSs were leveraged to enhance the active surface area of the sensing platform and allow adsorption of DEX onto the gold surfaces through its highly electronegative fluorine atom. Under optimized experimental conditions, the developed AuNS|GCE sensor showed excellent analytical performance with a remarkably low limit of detection (LOD) of 1.11 nM, a good sensitivity of 0.187 µA.nM⁻¹, and a high percentage recovery of 92.5% over the dynamic linear range of 20–120 nM (linear regression of 0.995). The favourable electrochemical performance of this sensor allowed for successful application in the sensitive determination of DEX in synthetic urine (20% v/v in PBS, pH 7). Full article

Malachite green (MG) is a highly toxic dye commonly used in industries and aquaculture, leading to significant environmental contamination and health hazards. Therefore, sensitive and selective detection of MG in real samples is urgently needed. This study presents the development of a molecularly imprinted surface-enhanced Raman spectroscopy (MI-SERS) plasmonic sensor for the rapid and sensitive detection of MG. The sensor consists of a gold nanostar (Au NS) layer as the SERS substrate and an imprinted polydopamine layer containing specific recognition sites for MG. Taking full advantage of the plasmonic effect of SERS and selective recognition capability of imprinted materials, under optimized conditions, the sensor demonstrated high sensitivity, with a detection limit of 3.5 × 10⁻³ mg/L, excellent selectivity against interference from other organic dyes, and robust performance with recoveries of 90.2–114.2% in real seawater samples. The MI-SERS sensor also exhibited good reproducibility, stability, and reusability. These findings suggest that the MI-SERS sensor is a promising tool for real-time monitoring of MG contamination in complicated samples. Full article

Gold nanostars (AuNSTs) stabilized with adenosine monophosphate (AMP) were synthesized using a scalable method, achieving a 30-fold yield increase compared to previous studies using AMP as a shaping agent, while also reducing the reaction time to 3 h. The AuNSTs were coated with mesoporous silica (mSiO₂) via a robust approach, producing the AuNSTs@mSiO₂ nanoparticles (NPs) with tunable thicknesses and consistent optical properties for a range of morphologies. The NPs were additionally coated with platinum (Pt) before synthesizing the mSiO₂ layer, facilitating a comparative analysis of catalytic activity. The catalytic performance of the bare AuNSTs, the AuNSTs@mSiO₂, and the AuNSTs@Pt@mSiO₂ was evaluated through methylene blue reduction, confirming the gold core as the primary catalytic source. The AuNSTs@Pt@mSiO₂ exhibited enhanced activity, highlighting the potential of the mSiO₂ coatings. Additionally, solid-phase catalytic tests using 3,3′,5,5′-tetramethylbenzidine (TMB) on cellulose discs demonstrated the effectiveness of these NPs under diverse conditions. These findings showcase the versatility and broad catalytic potential of silica-coated NPs for solution- and solid-phase applications. Full article

Simple and accurate analysis of cancer-related biomarkers is very important for disease screening and auxiliary diagnosis. This study proposed a facile immunoassay that used gold nanostar-labeled rabbit anti-AFP as a capture antibody and gold nanoparticle-conjugated goat anti-rabbit IgG as an enhance antibody for the construction of a detection strategy for AFP analysis. Investigations indicated that the 50 nm diameter GNS-labeled capture antibody can specifically catch AFPs by direct detection profile or by further signal amplification through AuNP-tagged enhance antibody combination. Results showed that the developed method holds 8.6 ng/mL sensitivity, 20.0–110.0 ng/mL detection range, acceptable precision and fine accuracy, as well as favorable specificity. Results of application to real serum determination by the proposed method are highly related to those of the ECLIA method (correlation coefficient is 0.931). The proposed method has simple-operation merit and is very suitable for clinical screening of large-scale serum samples of cancers. Full article

Stroke affects approximately 12 million individuals annually, necessitating swift diagnosis to avert fatal outcomes. Current hospital imaging protocols often delay treatment, underscoring the need for portable diagnostic solutions. We have investigated silver nanostars (AgNS) incubated with human plasma, deposited on a simple aluminum foil substrate, and utilizing Surface-Enhanced Raman Spectroscopy (SERS) combined with machine learning (ML) to provide a proof-of-concept for rapid differentiation of stroke types. These are the seminal steps for the development of low-cost pre-hospital diagnostics at point-of-care, with potential for improving patient outcomes. The proposed SERS assay aims to classify plasma from stroke patients, differentiating hemorrhagic from ischemic stroke. Silver nanostars were incubated with plasma and spiked with glial fibrillary acidic protein (GFAP), a biomarker elevated in hemorrhagic stroke. SERS spectra were analyzed using ML to distinguish between hemorrhagic and ischemic stroke, mimicked by different concentrations of GFAP. Key innovations include optimized AgNS–plasma incubates formation, controlled plasma-to-AgNS ratios, and a low-cost aluminum foil substrate, enabling results within 15 min. Differential analysis revealed stroke-specific protein profiles, while ML improved classification accuracy through ensemble modeling and feature engineering. The integrated ML model achieved rapid and precise stroke predictions within seconds, demonstrating the assay’s potential for immediate clinical decision-making. Full article

The core@shell nanostars composed of star-like Au nanocores with TiO₂ shells (Au@TiO₂ NSs) are synthesized in a one-pot reaction without any reducing or surface-controlling agents. The Au@TiO₂ NSs exhibit strong absorption in the UV region based on the interaction between the Au nanocore and the TiO₂ shell, and this optochemical property leads to the efficient laser desorption/ionization time-of-flight mass spectrometry (LDI-TOF-MS) analysis of small molecules with low background interference and high reproducible mass signals compared with spherical Au nanoparticles (NPs). The limit of detection and dynamic range values of various analytes also improved with Au@TiO₂ NSs compared with those obtained with spherical Au NPs. Our findings successfully demonstrate that Au@TiO₂ NSs are a promising matrix for the LDI-TOF-MS analysis of various small molecules as well as synthetic polymers. Full article

Monitoring the progression of Alzheimer’s disease (AD) is crucial for mitigating dementia symptoms, alleviating pain, and improving mobility. Traditionally, AD biomarkers like amyloid plaques are predominantly identified in cerebrospinal fluid (CSF) due to their concentrated presence. However, detecting these markers in blood is hindered by the blood–brain barrier (BBB), resulting in lower concentrations. To address this challenge and identify pertinent AD biomarkers—specifically amyloid plaques and apolipoprotein E4 (ApoE4)—in blood plasma, we propose an innovative approach. This involves enhancing a screen-printed carbon electrode (SPCE) with an immobilization matrix comprising gold nanostars (AuNSs) coated with chitosan. Morphological and electrical analyses confirmed superior dispersion and conductivity with 0.5% chitosan, supported by UV–Vis spectroscopy, cyclic voltammetry, and Nyquist plots. Subsequent clinical assays measured electrical responses to quantify amyloid-β 42 (Aβ42) (15.63–1000 pg/mL) and APoE4 levels (0.41 to 40 ng/mL) in human blood plasma samples. Differential pulse voltammetry (DPV) responses exhibited peak currents proportional to biomarker concentrations, demonstrating high linear correlations (0.985 for Aβ42 and 0.919 for APoE4) with minimal error bars. Cross-reactivity tests with mixed solutions of amyloid-β 40 (Aβ40), Aβ42, and ApoE4 indicated minimal interference between biomarkers (<3% variation), further confirming the high specificity of the developed sensor. Validation studies demonstrated a strong concurrence with the gold-standard enzyme-linked immunosorbent assay (ELISA), while interference tests indicated a minimal variation in peak currents. This improved device presents promising potential as a point-of-care system, offering a less invasive, cost-effective, and simplified approach to detecting and tracking the progression of AD. The substantial surface binding area further supports the efficacy of our method, offering a promising avenue for advancing AD diagnostics. Full article

This paper reports the successful application of a self-developed, miniaturized, low-power nano-star tracker for precise attitude measurement of a 5-m-long satellite extension boom. Such extension booms are widely used in space science missions to extend and support payloads like magnetometers. The nano-star tracker, based on a CMOS image sensor, weighs 150 g (including the baffle), has a total power consumption of approximately 0.85 W, and achieves a pointing accuracy of about 5 arcseconds. It is paired with a low-cost, commercial lens and utilizes automated calibration techniques for measurement correction of the collected data. This system has been successfully applied to the precise attitude measurement of the 5-m magnetometer boom on the Chinese Advanced Space Technology Demonstration Satellite (SATech-01). Analysis of the in-orbit measurement data shows that within shadowed regions, the extension boom remains stable relative to the satellite, with a standard deviation of 30′′ (1σ). The average Euler angles for the “X-Y-Z” rotation sequence from the extension boom to the satellite are [−89.49°, 0.08°, 90.11°]. In the transition zone from shadow to sunlight, influenced by vibrations and thermal factors during satellite attitude adjustments, the maximum angular fluctuation of the extension boom relative to the satellite is approximately ±2°. These data and the accuracy of the measurements can effectively correct magnetic field vector measurements. Full article

Since its discovery, the phenomenon of Surface Enhanced Raman Scattering (SERS) has gradually become an important tool for analyzing the composition and structure of substances. As a trace technique that can efficiently and nondestructively detect single molecules, the application of SERS has expanded from environmental and materials science to biomedical fields. In the past decade or so, the explosive development of nanotechnology and nanomaterials has further boosted the research of SERS technology, as nanomaterial-based SERS substrates have shown good signal enhancement properties. So far, it is widely recognized that the morphology, size, composition, and stacking mode of nanomaterials have a very great influence on the strength of the substrate SERS effect. Herein, an overview of methods for the preparation of surface-enhanced Raman scattering (SERS) substrates is provided. Specifically, this review describes a variety of common SERS substrate preparation methods and explores the potential and promise of these methods for applications in chemical analysis and biomedical fields. By detailing the influence of different nanomaterials (e.g., metallic nanoparticles, nanowires, and nanostars) and their structural features on the SERS effect, this article aims to provide a comprehensive understanding of SERS substrate preparation techniques. Full article

Three-dimensional plasma nanostructures with high light–thermal conversion efficiency show the prospect of industrialization in various fields and have become a research hotspot in areas of light–heat utilization, solar energy capture, and so on. In this paper, a simple chemical synthesis method is proposed to prepare gold nanoparticles, and the electrophoretic deposition method is used to assemble large-area three-dimensional gold nanostructures (3D-GNSs). The light–thermal water evaporation monitoring and surface-enhanced Raman scattering (SERS) measurements of 3D-GNSs were performed via theoretical simulation and experiments. We reveal the physical processes of local electric field optical enhancement and the light–thermal conversion of 3D-GNSs. The results show that with the help of the efficient optical trapping and super-hydrophilic surface properties of 3D-GNSs, they have a significant effect in accelerating water evaporation, which was increased by nearly eight times. At the same time, the three-dimensional SERS substrates based on gold nanosphere particles (GNSPs) and gold nanostar particles (GNSTs) had limited sensitivities of 10⁻¹⁰ M and 10⁻¹² M to R6G molecules, respectively. Therefore, 3D-GNSs show strong competitiveness in the fields of solar-energy-induced water purification and the Raman trace detection of organic molecules. Full article

Convenient, rapid, highly sensitive and on-site iron determination is important for environmental safety and human health. We developed a sensing system for the detection of Fe(III) in water based on 7-mercapto-4-methylcoumarine (MMC)-stabilized silver-coated gold nanostars (GNS@Ag@MMC), exploiting a redox reaction between the Fe(III) cation and the silver shell of the nanoparticles, which causes a severe transformation of the nanomaterial structure, reverting it to pristine GNSs. This system works by simultaneously monitoring changes in the Localized Surface Plasmon Resonance (LSPR) and Surface-Enhanced Raman Spectroscopy (SERS) spectra as a function of added Fe(III). The proposed sensing system is able to detect the Fe(III) cation in the 1.0 × 10⁻⁵–1.5 × 10⁻⁴ M range, and its selectivity of the GNS@Ag@MMC sensor toward iron has been verified monitoring the LSPR and the SERS response to other cations with a clear selectivity toward Fe(III). Full article

Market trends in the space sector suggest a notable increase in satellite operations and market value for the coming decade. In parallel, there has been a shift in the industrial and academic sectors from traditional Document-Based System Engineering to Model-based systems engineering (MBSE) combined with Concurrent engineering (CE) practices. Due to growing demands, the drivers behind this change have been the need for quicker and more cost-effective design processes. A key challenge in this transition remains to determine how to effectively formalize and exchange data during all design stages and across all discipline-specific tools; as representing systems through models can be a complex endeavor. For instance, during the Preliminary design (PD) phase, the integration of system models with external mathematical models for simulations, analyses, and system budgeting is crucial. The introduction of CubeSats and their standard has partly addressed the question of standardization and has aided in reducing overall development time and costs in the space sector. Nevertheless, questions about how to successfully exchange data endure. This paper focuses on formalizing a CubeSat model for use across various stages of the PD phase. The entire process is conducted with the exclusive use of open-source tools, to facilitate the transparency of data integration across the PD phases, and the overall life cycle of a CubeSat. The paper has two primary outcomes: (i) developing a generic CubeSat model using Systems modeling language (SysML) that includes data storage and visualization through the application of Unified modeling language (UML) stereotypes, streamlining in parallel information exchange for integration with various simulation and analysis tools; (ii) creating an end-to-end use case scenario within the Nanostar software suite (NSS), an open-source framework designed to streamline data exchange across different software during CE sessions. A case study from a theoretical academic space mission concept is presented as the illustration of how to utilize the proposed formalization, and it serves as well as a preliminary validation of the proposed formalization. The proposed formalization positions the CubeSat SysML model as the central data source throughout the design process. It also supports automated trade-off analyses by combining the benefits of SysML with effective data instantiating across all PD study phases. Full article

Chlorpyrifos is one of the most widely used broad-spectrum insecticides in agriculture. Given its potential toxicity and residue in food (e.g., tea), establishing a rapid and reliable method for the determination of chlorpyrifos residue is crucial. In this study, a strategy combining surface-enhanced Raman spectroscopy (SERS) and intelligent variable selection models for detecting chlorpyrifos residue in tea was established. First, gold nanostars were fabricated as a SERS sensor for measuring the SERS spectra. Second, the raw SERS spectra were preprocessed to facilitate the quantitative analysis. Third, a partial least squares model and four outstanding intelligent variable selection models, Monte Carlo-based uninformative variable elimination, competitive adaptive reweighted sampling, iteratively retaining informative variables, and variable iterative space shrinkage approach, were developed for detecting chlorpyrifos residue in a comparative study. The repeatability and reproducibility tests demonstrated the excellent stability of the proposed strategy. Furthermore, the sensitivity of the proposed strategy was assessed by estimating limit of detection values of the various models. Finally, two-tailed paired t-tests confirmed that the accuracy of the proposed strategy was equivalent to that of gas chromatography–mass spectrometry. Hence, the proposed method provides a promising strategy for detecting chlorpyrifos residue in tea. Full article