ACS Measurement Science Au最新文献

Hydrogels have emerged as a versatile platform technology for analyte sensing, offering unique advantages in tunable chemistry, for loading with sensors across multiple length scales, and biocompatibility. These smart materials undergo predictable changes in optical properties, conductivity, swelling, and porosity upon analyte interaction, enabling their function as biosensors. While hydrogels can respond to a variety of stimuli, their responses are most effectively quantified through optical and electrical readouts, which enable direct, real-time, and quantitative sensing in complex biological fluids. Optical approaches leverage fluorescence, chemiluminescence, and colorimetry, whereas electrical approaches leverage conductive fillers or redox-active groups. Hybrid platforms integrate multiple readout mechanisms, enhancing sensitivity, robustness, and multiplexing capabilities. Many of these systems were validated in various biological matrices, such as interstitial fluid, sweat, and wound exudates. Beyond technical advances, we discuss translational challenges including selectivity, stability, nonreversibility, signal standardization, device portability, and regulatory approval, as well as emerging opportunities in coupling hydrogel sensors with artificial intelligence for improved data interpretation and clinical integration. Together, these developments position hydrogel-based diagnostics as promising candidates for next-generation, real-time, point-of-care biosensing.

Natural extracts of rose are frequently subject to adulteration due to their high production costs and strong demand within the flavor and fragrance industry. Gas chromatography coupled with mass spectrometry (GC-MS) serves as a critical analytical tool for detecting adulterants and quantifying the relative concentrations of key rose alcohols. In this study, rose fragrances were analyzed by using GC-MS to assess compositional integrity. Standard accords consisted of six synthetic compounds with varying molecular weights, boiling points, and polarities, commonly found in rose essential oils and perfumes. Each component was baseline resolved on a nonpolar column and detectable at low concentrations (3-5%). Calibration curves were developed for phenyl ethyl alcohol, citronellol, and geraniol based on representative concentrations in commercial samples. Principal component analysis (PCA) was performed to further characterize and differentiate the accords according to their chemical profiles. Additionally, headspace solid-phase microextraction (HS-SPME) was used to sample the volatile profile of each sample on a fragrance strip. Several commercial samples, including two natural rose extracts (absolute and oil) and a commercial perfume, and a peony flower, were analyzed using liquid direct injection and/or HS-SPME. These results demonstrate the utility of GC-MS and HS-SPME for both quantitative and qualitative analysis of rose alcohols in complex fragrance matrices.

Carbonyl compounds generated through lipid peroxidation and Maillard-type reactions are relevant markers of food quality and safety due to their potential toxicity and prevalence in processed plant-based beverages. This work presents the development and validation of a green, ultrasound-assisted dispersive liquid-liquid microextraction (UA-DLLME) method using sustainable extractant (isobutyl acetate) and dispersant (isopropanol) solvents for the simultaneous determination of 12 carbonyl compounds across multiple chemical families (aldehydes, ketones, furans, and dicarbonyls). The method integrates in situ derivatization with O-(2,3,4,5,6-pentafluorobenzyl) hydroxylamine hydrochloride and gas chromatography-mass spectrometry (GC-MS) analysis. Method optimization was performed using a multivariable approach using an asymmetrical 2⁶·3¹ screening design and a central composite design for response surface methodology. Analytical performance was validated according to Food and Drug Administration guidelines, achieving great linearity (r ² ≥ 0.9991), accuracy (90-107% recovery), precision (<7.5% RSD) and detection limits suitable for trace-level analysis in complex matrixes. The approach was applied to 51 commercial plant-based beverage samples (almond, soy, oat, rice, coconut, seed and mixed formulations). Greenness assessment was performed using AGREEprep (0.69/1) and BAGI (77.5/100) metrics, confirming the method's alignment with green chemistry principles. This workflow provides a reliable and sustainable strategy for routine monitoring of carbonyls in food systems, with relevance to both quality control and exposure assessment.

Due to their high economic value, essential oils (EOs) are increasingly subject to adulteration, posing significant challenges for quality assessment and control. Owing to its excellent molecular specificity, Raman spectroscopy has been widely employed for the analysis and evaluation of EO products. In this study, we collected a total of 2700 Raman spectra comprising rose essential oil (REO, n = 900), geranium essential oil (GEO, n = 900), and their mixtures (n = 900). We first constructed a conventional convolutional neural network to distinguish spectra corresponding to varying EO mixing ratios. Interpretability analysis revealed that spectral peaks at 800, 1000, 1002, 1028, 1200, 1378, and 1668 cm^-1, which show notable intensity variations in the averaged spectra of REO and GEO, also played a critical role in model decision-making, indicating that these spectral features can serve as discriminative markers for different EO ratios. Subsequently, we developed a channel attention residual feature extraction network (CARFENet), which employs spectral capturing and spectral separation modules to deconstruct mixed EO spectra into their constituent pure components. CARFENet demonstrated robust performance on the validation set and yielded predicted spectra that closely resembled the true spectra in an external test set, with similarity indices exceeding 0.99. These findings indicate that CARFENet enables the effective quantitative analysis of pure EO components within mixed Raman spectra.

Nitrate (NO₃ ^-) and nitrite (NO₂ ^-) contamination, primarily from agricultural fertilizers, food additives, and industrial effluents, poses a significant threat to the environment and public health. While current detection methods are sensitive, they often rely on complex instrumentation, trained personnel, and time-consuming procedures, limiting their applicability in field settings. To overcome these limitations, we developed a portable, IoT-integrated, paper-based fluorescent sensor that meets all WHO's REASSURED criteria for ideal sensing devices. The sensor employs phenylene-diamine-derived carbon quantum dots (CQDs) as fluorescent probes with NO₂ ^- inducing fluorescence quenching via nitrosylation and diazotization. For simultaneous detection, NO₃ ^- is converted in situ to NO₂ ^- through a simple, eco-friendly on-strip reduction step, enabling unified quantification of both analytes. The platform achieved a limit of detection (LOD) of 0.1 ppm and exhibited a linear response from 1 to 100 ppm. Its performance was validated by using real water samples, successfully determining both NO₃ ^- and NO₂ ^- concentrations. Integrated with a custom hand-held optoelectronic reader and smartphone interface, the system enables real-time data acquisition, wireless transmission, and rapid on-site decision-making. This green, low-cost, and efficient platform offers a practical solution for environmental eMonitoring by integrating nanotechnology, paper based analytical device and smart sensing in a single device.

As circularity grows in the global economy, recycling has become more relevant in the plastic materials industry. Recycled plastics, sourced from various origins, can contain numerous non-intentionally added substances such as organic contaminants, polymer degradation products, and consumer residues. The confident identification of contaminants has become an important step in the quality assessment of the recycled material and the evaluation of cleaning processes. However, traditional one-dimensional gas chromatography often encounters challenges in reporting accurate results for these complex samples. In this work, we combine a cryogen-free comprehensive two-dimensional gas chromatographic separation coupled with high-resolution mass spectrometry and a new confidence-level-based data reporting workflow to achieve more rigorous and higher-confidence identification of nontargeted species in recycled plastics. We propose four confidence levels, and seven confidence descriptor classifications based on mass spectral matching, retention index matching, and mass accuracy from high-resolution mass spectral data. The workflow was applied to postconsumer recycled plastics before and after the cleaning process. Higher than 70% of identifications are made with medium-to-high confidence. About 50% more peaks are separated and identified by the workflow compared to traditional one-dimensional separation without significant increase in data collection and analysis time. The workflow was validated by recycled plastics spiked with 26 known compounds of environmental relevance covering a broad range of chemical structures.

Reliable quantification of trace metals in polymers by direct solid-state analysis remains limited by the lack of suitable reference materials. In this study, we introduce a novel method for producing standardized plastic samples specifically designed for the calibration and analysis of trace metals by laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS). Carboxylated polystyrene (PS) nanoparticles, capable of adsorbing metal ions from solution, were synthesized and evaluated. The trace metal adsorption capabilities of these PS nanoparticles were assessed with adsorption isotherms fit well with the Freundlich model. The study focused cobalt (Co²⁺), copper (Cu²⁺), and lead (Pb²⁺) The adsorption constants were found to be 0.15 for Co²⁺, 0.48 for Cu²⁺, and 3.63 for Pb²⁺. The metal-sorbed PS nanoparticles were then processed into disc-shaped plastics via hot-press molding, and LA-ICP-MS analysis confirmed the homogeneous metal distribution both at the surface and in depth, with relative standard deviations (RSD) of less than 5% for all metals analyzed. Calibration curves for Pb²⁺ (1220-4400 mg kg^-1), Cu²⁺ (210-600 mg kg^-1), and Co²⁺ (67-220 mg kg^-1) showed strong linear relationships, with R ² values of 0.96 for Pb²⁺, 0.95 for Cu²⁺, and 0.94 for Co²⁺. The methodology limits of detection (LOD) were determined to be 113 mg kg^-1 for Pb²⁺, 93 mg kg^-1 for Cu²⁺, and 23 mg kg^-1 for Co²⁺. The developed standards enabled the calibration of analytical instruments and thus improve the reliability of assessments concerning the contamination of environments with metal-laden microplastics.

Phytocannabinoids are a diverse class of bioactive compounds produced by Cannabis sativa, including both major and a growing number of minor constituents with pharmacological relevance. However, their comprehensive annotation in untargeted high-resolution mass spectrometry (HRMS) data sets remains a significant analytical challenge due to their structural similarity, low abundance, and the complexity of plant matrices. In this study, we present a comparative evaluation of Kendrick Mass Defect (KMD)-based filtering workflows for the efficient untargeted annotation of minor phytocannabinoids. Three data processing strategies were implemented using Compound Discoverer: (i) KMD filtering before the "Compound Detection" tool, (ii) KMD filtering after the "Compound Detection" tool, and (iii) a pseudo-KMD approach based on the generation of expected compounds. These workflows were tested and compared using a data set comprising 50 Cannabis inflorescence samples analyzed in an untargeted fashion, taking into account the phytocannabinoid coverage, false positive rates, computation burden, and versatility. A total of 61 phytocannabinoids were annotated, including a full series of alkyl homologues (C1-C7), cis/trans isomers, O-methylated derivatives, and sesquicannabinoids. Statistical analyses revealed meaningful chemical differentiation based on seed origin, chemovar classification, and reproductive strategy (dioecious vs monoecious), highlighting the biological significance of minor cannabinoids. Overall, the results demonstrate that KMD filtering significantly enhances the throughput and accuracy of untargeted HRMS workflows for structurally related classes of compounds.

Monitoring chemistry in the central nervous system in vivo is an important route to better understand chemical signals and metabolism underlying brain function. Sampling from the brain extracellular space allows for in depth and multiplexed analysis of this compartment. Miniaturized sampling devices prepared by using microfabrication are emerging as tools that allow high spatial resolution measurements with low invasiveness. In this work, we describe a microfabrication method for Parylene-C-based push-pull sampling probes. Parylene-C is an attractive material because its biocompatibility and flexibility may allow for chronic measurements. Probes with 3 mm long shanks and 45 × 166 μm cross sections were prepared and tested for suitability for in vivo experiments. Two designs, one with push-pull orifices on the probe tip and the other with the orifices recessed ∼40 mm within a sheath of Parylene-C, were investigated. The sheathed probes successfully flowed in tofu (n = 5) and ex vivo brain tissue (n = 5) for at least 3 h at 100 nL/min. In contrast, only 60% (n = 5) of the end-on probes flowed successfully in tofu. In vitro recoveries for the sheathed and design were 66 ± 2 and 91 ± 7%, respectively, in stirred solution. The sheathed probes were tested by sampling from the cortex of anesthetized rats. In 13 of 19 experiments, push-pull fractions were successfully collected. Of the six failures, all had a sheath shallower than the 30 μm design depth, suggesting that this depth is necessary for reliable sampling flow. Basal concentrations of 20 neurotransmitters and metabolites determined by liquid chromatography with tandem mass spectrometry (LC-MS/MS) were comparable with concentrations recorded using microdialysis sampling in prior work. Infusion of 2.0 mM nipecotic acid through the probe resulted in a significant increase in GABA with little effect on other compounds. Untargeted metabolomics analysis by LC-MS/MS identified 141 metabolites within the push-pull perfusates. The results demonstrate the feasibility of using Parylene-C push-pull probes with a sheathed tip design for multiplexed and temporally resolved monitoring of brain chemistry in vivo.

This study examines how Mg²⁺ ions affect the hybridization between surface-immobilized peptide nucleic acid (PNA) probes and microRNA targets (miR125 and miR141), which is important for the development of nucleic acid-based biosensors utilizing surface plasmon resonance (SPR). The results show that appropriate concentrations of Mg²⁺ significantly enhance microRNA hybridization with PNA probes, whereas Na⁺ does not yield similar results. Kinetic analysis demonstrated that 30 and 100 mM concentrations of Mg²⁺ facilitate the interaction between the PNA probe and its microRNA target by effectively screening the negative charges of the microRNA molecules as they approach the surface. These Mg²⁺ levels also stabilize the heteroduplexes formed on the surface by reducing the dissociation rate. However, a higher Mg²⁺ concentration (300 mM) was found to hinder the surface-confined hybridization. In comparison, Na⁺ showed a considerably smaller role in improving the hybridization. Melting curve analysis in solution indicated that the increase in T _m of PNA/miRNA heteroduplexes in the presence of Mg²⁺ does not fully explain the enhanced surface interaction, underscoring the role of surface confinement. These findings demonstrate that optimizing the Mg²⁺ concentration can significantly improve the sensitivity and efficiency of PNA- and SPR-based microRNA biosensors. This optimization is particularly relevant for diagnostic and research applications involving the analysis of low concentrations of microRNAs in biofluids.