Simulating precipitation in the Northeast United States using a climate-informed K -nearest neighbour algorithm
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Water Resources↗
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Comparison of removal and spatial mark‐resight models for estimating wild pig density
Density estimation is critical to effectively manage invasive species and elucidate areas of highest concern. For wild pigs (Sus scrofa), the ability to estimate density is complicated because of their variable home range sizes and social structure. Common methods for estimating density (e.g., mark-recapture) may be unsuitable in management applications because additional data needs to be collected before and after management. Removal models offer a suitable alternative to estimate density changes following management and can be applied broadly across areas where management of wild pigs is ongoing. We collected wild pig removal and camera trap data from 25 private properties ranging in size from approximately 0.5 km 2 to 95 km 2 across 3 ecoregions in South Carolina, USA, from 2020–2023. We compared factors affecting consistency and precision of property-level density estimates between removal and spatial mark-resight (SMR) models. In general, excluding 1 large outlier, density estimates from removal models were between 0.60 and 15.85 wild pigs/km 2 (median = 5.34) with a median coefficient of variation (CV) of 0.76 and 95% confidence intervals for the CV between 0.70 and 0.94. Similarly, excluding 1 large outlier, density estimates from SMR were between 0.22 and 30.97 wild pigs/km 2 (median = 5.48) with a median CV of 0.39 and 95% confidence intervals for the CV between 0.38 and 1.20. We found the precision of removal models was affected primarily by the number of wild pigs dispatched in the removal period (3 months) and the ecoregion in which they were removed. None of the covariates, including the number of recaptures (a corresponding measure of sample size), influenced precision of the SMR models, although recaptures did influence the density estimates. At the individual property level, density estimates from our 2 estimators were dissimilar from each other in approximately 80% of instances, although none of the covariates we examined influenced dissimilarity. Our results provide unique insight into how sample size affects density estimates using 2 common methods and into novel SMR models that incorporate both marked and unmarked detections. In addition, the density estimates in this study can be used as a reference for wild pig densities in common land cover types throughout the southeastern United States.
60 APPLIED LIFE SCIENCES↗
Comparison of greenhouse gas emission estimates from six hydropower reservoirs using modeling versus field surveys
As with most aquatic ecosystems, reservoirs play an important role in the global carbon (C) cycle and emit greenhouse gases (GHG) as carbon dioxide (CO 2 ) and methane (CH 4 ). However, GHG emissions from reservoirs are poorly quantified, especially in temperate systems, resulting in high uncertainty. We compared reservoir C emission estimates and uncertainty of diffusive, ebullitive, and degassing pathways in six hydropower reservoirs in the southeastern United States among four data sources: two field-based surveys and two models (including the GHG Reservoir “G-res” Tool). We found that CH 4 diffusion was most similar across data sources (modeled minus observed, bias = - 21 g CO 2-eq m -2 y -1 ) and had low relative uncertainty (coefficient of variation, CV = 0.98). On the other hand, CO 2 diffusion was least consistent across data sources (bias = - 518 g CO 2-eq m -2 y -1 ). Both field surveys indicated strong negative CO 2 diffusion (i.e., CO 2 uptake) at all reservoirs, while G-res estimated positive CO 2 diffusion. By extension, total C emissions showed similar discrepancies, leading to high uncertainty in upscaling and interpreting reservoir source-sink dynamics. Finally, CH 4 ebullition had the highest relative uncertainty (CV = 2.77) due to high variability across sites. We discuss limitations of field surveys and these models, including temperature-based annualization methods, varying definitions of ebullition zones, low sampling resolution, and lack of dynamism. Future field efforts focused on capturing variability in CO 2 diffusion and CH 4 ebullition will be especially valuable in reducing uncertainty and improving models to advance our understanding reservoir GHG emissions.
54 ENVIRONMENTAL SCIENCES↗
Importance of residence-time control of industrial screw-conveying reactors: Application to dilute-acid hydrolysis of biomass
Horizontal screw reactors are utilized in biorefineries for acid-catalyzed hydrolysis of xylan, which is a multi-step chemical reaction requiring accurate residence-time control. However, it is difficult to obtain online analytical measurement of reactant species. In this work, a residence-time distribution (RTD) is exhibited whose characteristics influence species yields. Sensitivity of product yield to RTD was investigated to understand the relative importance of operating control vs. inherent reactor dispersion. We find that reactor operation using a commonly used theoretical residence-time relationship can result in substantial yield losses. Instead, a model that accounts for the actual reactor RTD provides much improved results. The dispersion caused by reactor conditions only slightly hinders achieving theoretical optimal xylose yield (less than 3% yield loss for coefficient of variation less than 0.35), provided a validated RTD model is used to target the desired mean residence-time. In contrast, neglecting to account for the RTD by using the simplistic theoretical calculation results in xylose yields that are as much as 16% lower than the theoretical maximum.
09 BIOMASS FUELS↗
Quantitative assessment of the influence of external magnetic field on clustering of nano-Fe3O4 particles in cementitious paste
In view of active rheology control of cementitious materials, nano-Fe{sub 3}O{sub 4} can be added as responsive particles. Following the concept of magnetorheological fluids, it is assumed that magnetic nanoparticles will form chains or clusters in cementitious paste following magnetic field lines. A quantitative experimental validation of this assumption is presented herein. The clustering of nano-Fe{sub 3}O{sub 4} particles under magnetic fields is studied by mapping iron (Fe) element distribution in cementitious paste using energy dispersive X-ray spectroscopy. By means of image analysis, the Fe-element patterns are quantified by the deviation of Fe-elements in a unit area from the mean value expected in case of a uniform distribution, as expressed by coefficient of variation (COV). The magneto-rheological responses of cementitious pastes are evaluated using small amplitude oscillatory shear technique. Results show that the magneto-rheological effect exhibits a linear relationship with the relative change of COV, providing a quantitative validation of magnetic clustering in cementitious paste.
36 MATERIALS SCIENCE↗
Experiments on a vapor compression air conditioner with liquid desiccants for efficient dehumidification
Buildings require air conditioning systems that not only cool and dehumidify supply air but also provide sufficient ventilation to ensure indoor air quality and occupant comfort. However, standard recirculation systems-which introduce about a 10 % to 20 % fraction of outdoor air-often fail to deliver air that is precisely cooled and dry, particularly because 80-90 % of the ventilation cooling load is latent. Mixing humid ventilation air with recirculated indoor air increases the energy and costs required to condition the air to comfortable levels. Dedicated outdoor air systems (DOASs) are designed to handle this latent dominated ventilation load and thus need to have efficient humidity removal. Many cooling cycles can perform this task. Here we describe a liquid desiccant DOAS, which combines a vapor compression cycle and a liquid desiccant absorber and desorber pair. We present its performance at 26 operating conditions and a thermodynamic model which can accurately predict the moisture removal efficiency. The model's performance predictions have a mean percentage error of 2.5 % and a coefficient of variation of the root mean square error of 7.5 %. We also compare the performance of this vapor-compression-coupled liquid desiccant system with a standard vapor compression system with the same components but no liquid desiccant. For the 26 conditions tested in this study, this comparison shows that adding liquid desiccants lowers the required evaporator cooling load by 21 %, allows for 25 % lower compressor volumetric capacity, and 25 % lower electricity use. Future work will leverage this model to quantify the reduction in annual electricity use across different climates, including the need for a standard vapor compression system to reheat the air during some of the year.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Spatial and temporal characterization of municipal solid waste based on resource recovery pathways
This study presents a two-year, quarterly assessment of MSW across four source sectors (residential, schools, restaurants, and grocery stores) from sixteen sites across five U.S. states. MSW was manually sorted into 27 categories and aggregated into pathway fractions: high-moisture (HM) organics, low-moisture (LM) organics, recyclable (RC) materials, and residuals for disposal. Organics represented 89 % of the MSW stream. The largest fraction was HM organics consisting of food waste (31 %) and yard waste (3 %), with large coefficient of variations (CV), 79 and 278 %, respectively, reflecting high seasonal and site variability that varied significantly (p < 0.01) across sampling periods. The HM fraction showed properties favorable for anaerobic digestion, with moisture content ranging from 56 to 95 % and volatile solids ranges of 86-95 %. In contrast, the LM and RC fractions remained more stable (plastics CV = 41 %; paper CV = 53 %) with heating values up to 26.9 MJ/kg across sources, reflecting suitability for gasification. Microstructural analysis revealed less porosity in residential waste sampled at the landfill, which can influence preprocessing efficiency and microbial accessibility. Pathway informed allocations showed that 35 % of MSW is suitable for anaerobic digestion, 36 % for gasification, and 18 % for recycling, leaving 11 % requiring landfill disposal. These results provide quantitative evidence to determine feedstock allocation, waste-to-energy system design, and the development of data-driven sustainability and resource recovery strategies within a circular bioeconomy.
09 BIOMASS FUELS↗
Machine learning and deep learning for mineralogy interpretation and CO 2 saturation estimation in geological carbon Storage: A case study in the Illinois Basin
Carbon capture and storage (CCS) is a promising approach to simultaneously maintaining energy security and reducing carbon dioxide (CO 2 ) emissions under the current energy portfolio that is dominated by fossil fuel energy. Pre-injection formation characterization and post-injection CO 2 monitoring are two critical tasks to guarantee storage efficiency in CCS. The CCS projects in the Illinois Basin, the first large-scale CO 2 injection into saline aquifers in the United States, employed conventional and the latest pulsed neutron logging (PNL) tools for mineralogy interpretation and CO 2 saturation estimation, which provide valuable references for future CCS projects. Because of the inherent fuzziness of petrophysical measurements and complex subsurface heterogeneity, interpreting well-logging data is time-consuming, and its accuracy can be user-biased. In recent years, data-driven methods have been widely used to capture the non-linear patterns between input features and interpretation results. This work applied and evaluated four commonly used machine learning (ML) models, including ridge regression (RR), random forest (RF), gradient boosting regression (GBR), support vector regression (SVR), and one deep learning (DL) model, the artificial neural network (ANN). We optimized the hyperparameters of the four ML models and the DL model using the simulated annealing algorithm and the grid search strategy, respectively. The input features of the mineralogy interpretation models were eleven conventional well-logging parameters, and the label data (i.e., ground truth) were the porosity and volumetric fractions of six minerals, including quartz, feldspar, dolomite, calcite, clay, and iron minerals. The results demonstrated that the GBR and RF models were superior in predicting volumetric fractions of minerals and porosity; label data with low coefficient of variation (CV) values tended to yield better performance. For CO 2 saturation estimation, the RF was the best-performing model, followed by SVR, ANN, GBR, and RR. Furthermore, we conducted feature importance ranking using the permutation importance algorithm and found that the formation sigma and well pressure were the most important features in this study. In conclusion, the study of CCS projects in the Illinois Basin bridges the gap between the limited knowledge and understanding of geological carbon storage and the increasing demand for reliable, cost-effective, and sustainable energy solutions.
58 GEOSCIENCES↗
Characterization of the indoor near-field aerosol transmission in a model commercial office building
To evaluate the exposure potential of infectious aerosols containing SARS-CoV-2 in an office building setting, synthetic test aerosols were used to experimental study airborne particle transmission in a multizone small office test building at the Oak Ridge National Laboratory. Nine measurement points in a single zone using active aerosol impactors report that the coefficient of variation of the time-averaged concentration is <10% in two campaigns and < 15% in one campaign, so a nearly well-mixed condition was noted. To understand the effect of HVAC system operation on the dynamic concentration of aerosols in office spaces, an aerosol transport model that includes factors such as outside air (OA) ratio, filtration, return air fraction, transport loss in air ducts, and particle deposition has been developed. The results of model fitting demonstrate strong agreement with experimental data. Overall, our investigation finds the return air fraction effects outweigh other mechanisms for the aerosol recirculation in this study, and the impact of air change rate (ACR) is more important than the small particle deposition for aerosol removal. Because ACR dominates the aerosol transport, the full model can be simplified to just one factor, the ACR, while maintaining an acceptable representation of the experimental data.
60 APPLIED LIFE SCIENCES↗
Application of a Geochemically Informed Leak Detection (GILD) Model to CO 2 Injection Sites on the United States Gulf Coast
The Gulf Coast region possesses great potential for CO 2 enhanced oil recovery (EOR) and CO 2 storage. A geochemically informed leak detection (GILD) model has been applied to CO 2 injection sites on the Gulf Coast with considerations of measurement variability. The Jasper aquifer in Montgomery County, Texas, was chosen to demonstrate the method. Based on background data from wells in the area, combinations of mineral and fluid compositions were used to create 23 scenarios for the geochemical model. The output from the geochemical model was used to identify sensitive monitoring species, and response functions were generated for these as a function of the CO 2 leakage concentration. The sources of measurement variability for background conditions were characterized from the Jasper aquifer background data, and then normalized using the coefficient of variation of each species across the monitoring wells. Bayesian belief network (BBN) models were constructed, and measurement variability of different levels were added to compare leak detection probabilities. Increasing measurement variability decreased the power to detect a leak of a given size. For a moderately high CO 2 concentration of 0.2 mol/kg, the probability of detecting this leakage effect using pH as the monitoring variable in an aquifer with calcite decreases from 98% (no measurement variability) to 61% (medium variability) to 33% (high variability). The loss in power of the sampling protocol with increasing measurement variability is similar in magnitude when Ca 2+ or HCO 3 - is used as the monitoring parameter, but only for aquifers with calcite.
58 GEOSCIENCES↗
Unraveling the implications of finite specimen size on the interpretation of dynamic experiments for polycrystalline aluminum through direct numerical simulations
Normal and Pressure-shear plate impact (NPI and PSPI) experiments are popular experimental techniques for studying the mean-field macroscopic behavior of polycrystalline metals under high-rate dynamic loading. However, since both configurations rely upon geometry for subjecting the specimen to high strain rates, these experiments often involve a limited specimen size. Moreover, because of the inherent heterogeneities present within polycrystalline metals, it is difficult to ascertain if the size of the specimen and/or regions where measurements are made are sufficiently large for making representative inferences about the mean-field macroscopic properties from single-point velocity measurements. In the present study, we quantify the expected measurement variability on observable point measurements in NPI and PSPI experiments by carrying out direct numerical simulations (DNS) of statistically representative polycrystalline microstructures subjected to dynamic compression and compression-shear loading. In particular, we consider the role of specific material heterogeneities (e.g. the grain-to-grain difference in size, crystallographic orientation) on dispersion in the normal and transverse particle velocity records and on local fluctuations in key state variables (e.g. velocity, accumulated plastic strain) by incorporating these effects directly into a representative synthetic microstructure geometry and crystalline description of pure polycrystalline aluminum. The form of the present study is a large parametric investigation, consisting of ten ensembles of one hundred simulations. Each of the thousand simulations reflects a randomly realized synthetic microstructure in one of five cases of decreasing average grain size for the two loading configurations. Our analysis of the DNS results demonstrates that for both of these experimental configurations, the grain size directly correlates with the coefficient of variation (CV) in simulated point measurements, showing a convergent decrease in CV to zero (i.e. particle velocity record approaches the mean-field value) with decreasing grain size. Remarkably, the magnitude of variations in the particle velocity record is shown to be largest where the deviatoric stresses are most significant. In the case of NPI, this occurs at the elastic and plastic wavefront, whereas, in the case of PSPI, the magnitude of fluctuations are approximately constant throughout the experimental window time. The reasoning for the scatter in particle velocity due to the heterogeneous microstructure is demonstrated to be dependent on the mechanisms for accommodating deformation and on the interaction of reflection waves generated at sites of heterogeneities occurring at the scale of grains. Lastly, we develop a power-law description for the magnitude of scattering versus characteristic length, which provides a statistical framework for assessing the required number of grains per characteristic specimen dimension for minimizing scatter within these two experimental configurations (NPI, PSPI).
36 MATERIALS SCIENCE↗
Enhancing isotope mixing in U-10Mo downblend castings with electromagnetic stirring
Uranium alloyed with 10 wt percent molybdenum (U-10Mo) is the proposed fuel for use in the United States’ high-performance research reactors. The U-10Mo fuel is fabricated by a two-step casting process that downblends highly enriched uranium with depleted uranium (DU) and/or natural uranium (NU) and subsequently alloys the resulting high-assay low-enriched uranium (HALEU) with Mo. Currently, a two-step casting process is needed to meet the ingots’ U-235 enrichment homogeneity specifications. This work demonstrates the ability to provide more homogeneous U-235 distributions during downblend casting by using zirconia for crucibles rather than graphite. When graphite is used as a crucible, the electromagnetic field produced by the induction heater couples directly with the graphite. Zirconia is nonconducting in this study, and therefore does not couple with the field. The induction field couples directly with the metal, and causes electromagnetic stirring (EMS) in the molten metal pool. Eight downblend casting experiments were carried out in this work. Four were performed with zirconia crucibles and four with graphite crucibles. The U-235 enrichment was measured at nine discrete points in each casting using laser ablation multi-collector inductively coupled mass spectrometry (LA-MC-ICP-MS). The enrichment homogeneity was approximately eight times better in zirconia crucible castings than in graphite crucible castings as measured by the plate enrichment range and enrichment coefficient of variation. The results show a single casting step could be used to meet USHPRR enrichment specifications if electromagnetic stirring is present during casting. A related investigation was carried out to determine whether it is appropriate to measure enrichment on as-cast ingots or whether heat treated specimens should be used to accurately characterize enrichment. The results show that the as-cast microstructure plays a significant role in enrichment homogeneity. It is recommended that all enrichment measurements be done after the homogenization heat treatment typical of U-10Mo processing.
36 MATERIALS SCIENCE↗
Long-term spatial and temporal solar resource variability over America using the NSRDB version 3 (1998–2017)
The study assesses the long-term spatial and temporal solar resource variability in America using the 20-year National Renewable Energy Laboratory's (NREL's) National Solar Radiation Database (NSRDB). Specifically, the coefficient of variation (COV) is used to analyze the spatial and temporal (interannual and seasonal) variability. Further, both spatial and temporal long-term variability are analyzed using the Köppen-Geiger climate classification. The temporal variability is found that, on average, the continental United States (CONUS) COV reaches up to 5% for global horizontal irradiance (GHI) and 10% for direct normal irradiance (DNI), and that the NSRDB domain's COV is roughly twice that of CONUS. For the seasonal variability analysis, the winter months are found to exhibit higher COV than the other seasons. In particular, December exhibits the highest variability, reaching on average 30% for DNI and 20% for GHI over various areas. On the other hand, the summer months demonstrate significantly lower variability, reaching only less than 20% for DNI and 10% for GHI, on average. Similarly, the spatial variability is analyzed by comparing each pixel to its neighbors. The long-term spatial variability is found to increase with the number of neighboring pixels being considered, which is equivalent to an increase in distance (within a 100-km x 100-km square grid). As expected, the DNI spatial variability is higher than that of GHI. Moreover, the annual solar irradiance anomalies are found to reach ±25% for both GHI and DNI (and even exceed those value in some instances) during each year of the 20-year period.
14 SOLAR ENERGY↗
A regional comparison of sub-daily flow variability in regulated and unregulated rivers in the United States
Regulating rivers for hydropower or other purposes can dramatically alter river flow patterns, including creating substantial changes in flow over short, minutes-to-hours-long timespans known as sub-daily flow variability (SDFV). The impacts of flexible hydropower production on flow and aquatic organisms are increasingly documented in research. However, the degree to which flow alteration relates to different hydropower operational modes in distinct geographical regions and seasons is not well understood. This study offers a methodology for regional- and species-appropriate evaluations of potential impacts of flow on fish based on sub-daily flow characteristics of hydropower operational modes. We analyzed 15-min discharge data between 2018 and 2021 from 69 USGS stream gages to compare SDFV in hydropeaking, run-of-river, and unregulated systems in the US Southeast and Pacific Northwest. Regulated systems exhibited significant SDFV downstream from hydropower facilities relative to unregulated systems, but specific impacts differed between regions. Regulated systems in the Southeast were characterized by high flow coefficients of variation and ratios (hydropeaking only) and extended durations of daily upramping flow phases. Regulated systems in the Pacific Northwest were characterized by many short flow phases per day and large portions of the day spent upramping. Pacific Northwest unregulated systems displayed the strongest seasonal flow patterns while Southeastern hydropeaking systems displayed the greatest SDFV. Given that SDFV impacts multiple dimensions of fish ecology, region-specific sub-daily flow signatures have important implications for understanding and mitigating potential community-, species-, and age-specific effects on fish in different parts of the country.
Fish↗
The inclusion of uncertainty in circularity transition modeling: A case study on wind turbine blade end-of-life management
The transition to a more circular economy (CE) is complex and hard to predict, including in sustainable energy technologies. Many sources of uncertainty make it challenging to model CE scenarios and their potential benefits. As an example, the high variability in costs and revenues of different recycling options makes future wind turbine blade recycling highly uncertain. To better understand this challenge, the circular economy, life cycle assessment and visualization (CELAVI) framework - a discrete event simulation and life cycle assessment framework - is modified to incorporate uncertainty analysis capabilities. Moreover, a 3-step procedure that covers different aspects of uncertainty in CE studies and includes a Monte-Carlo analysis is proposed. The procedure is tested in a case study on wind turbine blade recycling using CELAVI. Results highlight that grinding and landfilling costs are the most influential parameters for wind power circularity. The model's output coefficients of variation (when input parameter uncertainties are propagated) are between 92% and 384% depending on the indicator. The approach developed in this study may help researchers and decision-makers who study circularity prioritize their data collection effort. Finally, our method contributes to a mounting yet critical body of research: the measurement of uncertainty in circularity transitions.
17 WIND ENERGY↗
Single Quadrupole Multiple Fragment Ion Monitoring Quantitative Mass Spectrometry
Single quadrupole mass spectrometry (MS) with enhanced in-source multiple fragment ion monitoring was designed to perform high sensitivity quantitative mass analyses. Enhanced in-source fragmentation amplifies fragmentation from traditional soft electrospray ionization producing fragment ions that have been found to be identical to those generated in tandem MS. We have combined enhanced in-source fragmentation data with criteria established by the European Union Commission Directive 2002/657/EC for electron ionization single quadrupole quantitative analysis to perform quantitative analyses. These experiments were performed on multiple types of complex samples that included a mixture of 50 standards, as well as cell and plasma extracts. The dynamic range for these quantitative analyses was comparable to triple quadrupole multiple reaction monitoring (MRM) analyses at up to 5 orders of magnitude with the cell and plasma extracts showing similar matrix effects across both platforms. Amino acid and fatty acid measurements performed from certified NIST 1950 plasma with isotopically labeled standards demonstrated accuracy in the range of 91–110% for the amino acids, 76–129% for the fatty acids, and good precision (coefficient of variation <10%). To enhance specificity, a newly developed correlated ion monitoring algorithm was designed to facilitate these analyses. This algorithm autonomously processes, aligns, filters, and compiles multiple ions within one chromatogram enabling both precursor and in-source fragment ions to be correlated within a single chromatogram, also enabling the detection of coeluting species based on precursor and fragment ion ratios. Single quadrupole instrumentation can provide MRM level quantitative performance by monitoring/correlating precursor and fragment ions facilitating high sensitivity analysis on existing single quadrupole instrumentation that are generally inexpensive, easy to operate, and technically less complex.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Open Specy 1.0: Automated (Hyper)spectroscopy for Microplastics
Microplastic spectral analysis is one of the most time-consuming processes in studying microplastic pollution, often requiring days per sample. Researchers are transitioning to automated batch and hyperspectral image analysis techniques to enhance efficiency. Open Specy, initially aimed at manual single-spectrum analysis, has now integrated automated methods. This updated version, Open Specy 1.0, introduces several new features, including two algorithms for automated processing (smoothing and particle compression), an extensive library containing over 40,000 open-source Raman and FTIR spectra, and two machine learning classifiers (logistic regression and k medoids) developed from this library. Furthermore, it includes a revamped user interface, an R package, and a benchmark data set for testing future advancements in automated techniques. Researchers evaluated various configurations for hyperspectral smoothing, particle identification, compression, and splitting, to achieve combined recovery rates between 50 and 150% particle counts, identities, and sizes with a coefficient of variation (CV) of less than 40% (the accredited standard). Mean absorbance times the standard deviation provided a consistent particle identification. Hyperspectral smoothing led to a 96% combined recovery rate and reduced variability (CV = 38%) compared to the 86% recovery (CV = 83%) of nonsmoothed controls. Additionally, compressing spectra for particles was significantly faster (>3x) and showed similar accuracy but with reduced variability than processing each pixel individually. Key challenges persist in automating spectral analysis, particularly in refining particle splitting algorithms, and improving identification routines to minimize false positives and negatives. In conclusion, new methods in sample preparation for better stabilization and dispersion of particles could overcome some of these issues.
13 HYDRO ENERGY↗
Enhancing Sensitivity in Targeted Single-Cell Proteomics by Coupling a Dual Ion Funnel Interface with Triple Quadrupole Mass Spectrometer
Single-cell proteomics (SCP) has emerged as a powerful approach for understanding cellular heterogeneity and biological processes at unprecedented resolution. However, the extremely limited protein content of individual cells (femtogram to picogram levels) pushes current mass spectrometry instrumentation to its sensitivity limits, creating a critical analytical bottleneck. While selected reaction monitoring (SRM) using triple quadrupole (QqQ) instruments 1 offers advantages in sensitivity and reproducibility for targeted proteomics quantification, SRM still struggles with sensitivity for quantification of moderate- or low-abundance proteins from single-cell sample amounts. Here, we report the development and systematic evaluation of a dual ion funnel interface designed to address the sensitivity limitation by significantly enhancing ion transmission efficiency in commercial QqQ mass spectrometers. The dual ion funnel interface, composed of a curved S-funnel followed by a conventional ion funnel, improves ion transmission efficiency while reducing chemical noise through selective ion focusing. The performance of the dual ion funnel interface was systematically compared to standard interface on a TSQ Vantage platform across samples with different levels of complexity. The dual funnel interface demonstrated to provide up to 25-fold improvement in sensitivity across a wide range of protein concentrations in different biological matrices (low complex mouse macrophage and high complex human cells). Critically, enhanced sensitivity was accompanied by increased analytical reproducibility with lower coefficient of variations. Most importantly, the dual funnel interface enabled reliable quantification of low-abundance proteins that were barely detectable or not detected by the standard interface, extending analysis to single-cell equivalent amounts while maintaining excellent reproducibility. These results demonstrate that the dual funnel interface addresses the critical bottleneck in quantitative targeted proteomics, providing a technological foundation for ultrasensitive targeted SCP that requires both high sensitivity and robust quantitative performance.
Min, Sehong↗