ACS Measurement Science Au最新文献

In nanoscale sensors, understanding and predicting sensor sensitivity is challenging as the physical phenomena that govern the transduction mechanism are often highly nonlinear and highly coupled. The sensitivity of a sensor is related to both the magnitude of the analyte-caused signal change and the random error-caused fluctuation of the sensor's output. The extent to which these can be controlled, by carefully designing either the geometric or operating conditions of the sensor, determines the difference in signal output between the presence and absence of the analyte, as well as the impact of random errors on the distribution of these signal outputs. Herein, we use ion-current-rectifying nanopore sensors as a simplified case study to show how geometric and operating parameters can enable sensitivity optimization. Finite element analysis is used to obtain distributions of the sensor output, and then, Sobol analysis is used to highlight the most important contributions to sensor output errors. Furthermore, the magnitude of the signal change is considered alongside the spread of the output to calculate and optimize the sensor sensitivity. We highlight that the most important parameters contributing to the output variance are geometric. We observed that as the sensor is operated at smaller pore radii and lower electrolyte concentrations, the influence of the cone angle errors increases, the influence of the pore radius errors decreases, and the output becomes broader. We also show that the highest sensitivity is expected for larger pores operated at low electrolyte concentrations, and our simulation results are validated by experimental results. Recommendations to achieve optimum sensitivity are given for a range of nanopore scenarios in which ion-rectifying nanopore sensors may be used. This work aims to provide a framework for the nanoscale community to optimize sensitivity using simulations, as the analysis highlighted herein is viable for any system that can be modeled using continuum physics.

Mass spectrometry (MS) has changed our understanding of health, disease, and the environment through untargeted analyses where entire molecular classes are investigated. These techniques generate huge amounts of data which when processed by statistical tools can identify important molecular features or biomarkers. The complexities of these samples are not compatible with direct introduction to the MS system and require a high-resolution separation step, typically low flow liquid chromatography (LC), prior to MS. LC columns that can produce adequate linear velocities at these low flow rates are small in volume making their results susceptible to resolution loss in extra-column volumes. Here, we investigate the implications of the extra-column effects in five LC-MS systems with triple quadrupole and orbitrap mass analyzers. The extra-column volume of these systems in their standard configuration ranged from 26.4 to 78.1 μL which we reduced to 9.57 to 18.7 μL by optimizing the fluidics. The effects of this volume reduction were assessed by studying a hydrolyzed protein sample in a proteomics environment where the intensity of the largest MS peak was improved by 1.8-3.8×. Additionally, the number of molecular features detected in the protein sample improved by up to 7.5×. The relationship between extra-column volumetric variance and flow rate shows that broadening will become much larger for MS detectors at higher flow rates, unlike a traditional small volume UV detector. The methods, applications, and theoretical insights in this work can be used to improve the mass spectrometric results of any LC-MS system.

Diabetes affects over 8.8% of the global population, driving demand for noninvasive glucose detection methods. Traditional enzymatic assays are sensitive but face challenges such as high cost, complex preparation, low stability, and enzyme denaturation. This study aimed to enhance glucose detection sensitivity with a noninvasive easy-to-use technique using a fluorescent cellulose film. Lignin-derived carbon dots (LCDs) were synthesized as cost-effective, stable nanozymes for fluorescence-based glucose sensing. It was found that doping noble metal Ru onto Pt/LCDs synthesized in water mimicked peroxidase enzyme and could enhance the reactivity and sensitivity to ultralow levels for glucose detection at room temperature. To fabricate a wearable sensor, a transparent cellulose film embedded with PtRu/LCDs and glucose oxidase (GOx) was fabricated for biocompatible glucose sensing. The film achieved sensitive detection in the range of 0.05-1.0 mM (R ² = 0.94) with a detection limit of 50 μM, suitable for noninvasive glucose detection in saliva, tears, and sweat. This study highlights the potential of the PtRu/LCD-based cellulose film for highly sensitive, wearable glucose sensors compatible with smartphone applications, offering a simple, real-time, noninvasive, fast, and chemical reagent-free glucose sensing for preventive healthcare.

We investigated the influence of particle size on the electrochemical behavior of Fe₃O₄ magnetite nanoparticles (MNPs) electrostatically adsorbed onto graphite electrodes modified with a preadsorbed poly-(ethylenimine) polycation layer. Three hydrodynamic sizes (50, 100, and 200 nm) were selected to assess size-dependent differences in electrochemical response using cyclic voltammetry under well-controlled adsorption and measurement conditions. The 50 nm MNPs exhibited the highest electroactive response and peroxidase-like electrocatalytic currents, which are consistent with greater surface area-to-volume ratios. Qualitative image analysis from atomic force microscopy and scanning electron microscopy revealed closer particle spacing and more extended surface contact for the smaller MNPs, in contrast to isolated aggregates formed by larger particles. These surface-level differences were reflected in the electrochemical signals, where the 50 nm particles yielded higher electroactive surface coverage. The study demonstrates how particle size and interfacial organization influence electrochemical readouts, underscoring the utility of correlating microscopy with electrochemical data to evaluate nanoparticle-based sensing interfaces.

Extracellular vesicles (EVs) are nanosized particles that are associated with various physiological and pathological functions. They play a key role in intercell communication and are used as transport vehicles for various cell components. In human milk, EVs are believed to be important for the development of acquired immunity. State-of-the-art analysis methods are not able to provide label-free chemical information at the single-vesicle level. We introduce a protocol to profile the structure and composition of individual EVs with the help of atomic force microscopy infrared spectroscopy (AFM-IR), a nanoscale chemical imaging technique. The protocol includes the immobilization of EVs onto a silicon surface functionalized with anti-CD9 antibodies via microcontact printing. AFM-IR measurements of immobilized EVs provide size information and mid-infrared spectra at subvesicle spatial resolution. The received spectra compare favorably to bulk reference spectra. A key part of our protocol is a technique to acquire spectral information about a large number of EVs through hyperspectral imaging combined with image processing to correct for image drift and select individual vesicles.