Picarro (United States)

Stable isotope analysis of organic materials is essential in environmental, geochemical, and food authenticity research, offering insights into carbon sources and product origins. Traditional Picarro Combustion Module–Cavity Ring-Down Spectroscopy (CM-CRDS) systems provide reliable, cost-effective δ¹³C analysis; furthermore, enabling simultaneous δ²H measurement greatly expands their utility for many applications.We present a straightforward extension of the CM-CRDS system, integrating two dedicated analyzers: the Picarro G2201-i for δ¹³C and the Picarro L2130-i for δ²H. Key modifications include removing the water trap, heating the transfer tubing, and adding a heated buffer volume, enabling direct isotopic analysis of water vapor alongside carbon dioxide. This setup maintains the original sample delivery for carbon isotope analysis, while a simple software adjustment allows precise peak integration for hydrogen isotopes.Performance was validated using a range of international standards representing diverse organic materials: USGS88 (marine collagen), USGS89 (porcine collagen), USGS90 (millet flour), USGS91 (rice flour), and IAEA CH7 (polyethylene foil). The system demonstrated excellent δ²H linearity (with slopes of 1.040, 1.074 and 1.017 on three separate days and R² values exceeding 0.99) while maintaining the high accuracy of δ¹³C measurements. Precision was assessed with hexamethylenetetramine (HMT), yielding a δ²H standard deviation of 0.26‰ and δ¹³C of 0.04‰ over 50 replicates. We chose HMT to determine the precision because it does not exchange hydrogen isotopes during storage and analysis and is used to determine the carbon-bound non-exchangeable hydrogen in fructose and glucose in honey [1]. Calibration procedures and best practices for hydrogen isotope analysis are discussed.Our findings highlight the potential of combining the G2201-i and L2130-i analyzers with a CM in a coordinated analytical workflow for dual isotope analysis. This methodology opens new opportunities for isotope studies for environmental as well as food authenticity and food origin studies, and is a low-cost, easy to use alternative to IRMS analysis.Reference[1] Li et al., 2024, A new approach to detecting sugar syrup addition to honey: Stable isotope analysis of hexamethylenetetramine synthesised from honey monosaccharides (fructose and glucose). Food Chemistry 434.

High‑precision greenhouse gas (GHG) measurements are essential for accurately assessing soil carbon and nitrogen cycling. Traditionally, gas chromatography (GC) with autosamplers has been the standard for soil incubation studies due to its accuracy and high‑throughput capability. Recent advances in laser‑based systems, such as Picarro’s G2508 Cavity Ring‑Down Spectroscopy (CRDS) analyzer, now enable continuous, real‑time monitoring of CO₂, CH₄, N₂O, and other gases. When paired with Picarro’s new Sage gas autosampler, the system supports automated, high‑throughput measurements of discrete, small‑volume gas samples using only zero air (or N₂) for flushing and requiring minimal maintenance.To evaluate performance relative to GC, we conducted an intercomparison study using 60 mL samples split into two equal aliquots—one measured with the G2508–Sage system and one with GC. Certified reference gases (9.9 ppm N₂O, 10 ppm CH₄, 1008 ppm CO₂) and their 10–80% dilutions were analyzed. Both systems achieved coefficients of variation (CVs) below 5%, with the G2508–Sage consistently showing lower CVs when sample concentrations were within the analyzer’s dynamic range. Strong linear correlations (R² > 0.99) were observed across all gases.For soil incubation headspace samples containing elevated GHG concentrations, the two systems generally agreed within CVs

Rationale generation in large language models faces critical challenges in ensuring faithfulness and interpretability, limiting their deployment in high-stakes applications requiring transparent reasoning. This study introduces a novel counterfactual verification framework designed to enhance rationale quality across diverse reasoning tasks and model architectures. We evaluate our approach using three state-of-the-art models—GPT- 4o, Claude-3.5-Sonnet, and LLaMA-3.3-70B—across benchmark datasets representing natural language inference (e-SNLI), multi-hop question answering (HotpotQA), and reading comprehension (MultiRC). The evaluation uses four metrics: counterfactual consistency scores, deletion-AUC for sufficiency analysis, perturbation robustness, and human simulatability assessments. Results show improvements in counterfactual consistency (median improvement = 0.086, p ¡ 0.001, Wilcoxon signed-rank test, N = 6,000), with effect sizes ranging from small-to-medium for computational metrics to very large for human performance measures. Human simulatability studies show a 14-point improvement in prediction accuracy (Cohen’s d = 3.50, p ¡ 0.001). Optimal rationale length is 15-35 tokens. Deletion curves indicate better information organization and sufficiency. GPT-4o outperforms other models, with Claude-3.5-Sonnet balancing efficiency and quality. Counterfactual verification enhances AI explainability by improving computational faithfulness and human comprehension, providing a reliable basis for trustworthy reasoning systems in critical applications.

Air quality forecasting, particularly PM2.5 concentration prediction, requires robust models capable of handling complex atmospheric dynamics. While individual machine learning models show promise, they often struggle with varying environmental conditions and temporal patterns. This paper presents an adaptive Mixture of Experts (MOE) framework for PM2.5 forecasting that combines five diverse expert models through a learned gating network. Our approach automatically selects and weights Linear Regression, Random Forest, XGBoost, LSTM, and N-BEATS models based on input characteristics. Evaluated on 36,168 hourly PM2.5 observations from India (2017-2022), our MOE framework achieves an RMSE of <tex>$3.554 \mu ~\mathrm{g} / \mathrm{m}^{3}$</tex> and <tex>$\mathrm{R}^{2}$</tex> of 0.960, representing a 12.1% improvement over the LSTM baseline (<tex>$4.041 \mu ~\mathrm{g} / \mathrm{m}^{3}$</tex>). Among the methods evaluated in this study, ensemble approaches generally outperformed individual models, with four of the top six performing methods being ensemble-based. Expert analysis reveals that XGBoost receives the highest selection rate (40.3%) within the MOE framework. Our findings suggest that adaptive model selection may offer advantages over individual models and static ensemble strategies for PM2.5 forecasting in the studied region, though validation across diverse geographic locations and atmospheric conditions would be needed to assess broader applicability

BackgroundExtreme ultraviolet (EUV) photoresists play a pivotal role in advancing nanopatterning technologies by balancing image quality and sensitivity. The outgassing behavior of photoresist thin films under EUV and deep ultraviolet (DUV) exposure reveals chemical details relevant to their performance.AimWe focus on utilizing an analytical technique not previously used in photolithography, the tabletop cavity ring-down spectrometer, to investigate outgassing dynamics in EUV photoresists, enabling precise chemical identification and deeper insights into resist processing.ApproachThe spectrometer’s enhanced laser path length (∼20 km) and broadband absorption capabilities in the C–H overtone region allow for sensitive and temporally resolved detection of mixtures of outgassed species. Using a model resist comprising a polymer matrix with a photoacid generator and quencher, we analyzed the influence of time delays between exposure and post-exposure bake (PEB) as well as storage under varying environmental conditions.ResultsSuppression of isobutylene outgassing and thickness loss was observed with extended delays between exposure and PEB, potentially linked to water absorption and acid deactivation. The technique proved highly effective in distinguishing subtle chemical differences between processing stages.ConclusionsDelay times and their environmental conditions, particularly humidity, reduce outgassing and thickness loss of photoresists during PEB, suggesting decreased acid-driven deprotection. This can potentially impact sensitivity, defectivity, and roughness of resist patterns, necessitating precise monitoring and control.

Volatile chemical products (VCPs), including organic species emitted from pesticides, coatings, cleaning products, and personal care products, account for more than half of the urban VOC emissions in major North American and European cities. However, VCP emissions, spatial and temporal distributions, and impacts vary widely. Despite being the fourth largest U.S. city, Houston, Texas, lacks measured VCP concentration and emission data. This study presents the first spatial and temporal measurements of selected VCP tracers in Houston, Texas, using a Vocus 2R Chemical Ionization Mass Spectrometer on a mobile platform. Ambient measurements of five major VCP tracers, including D5-siloxane, monoterpenes, para-dichlorobenzene, para-chlorobenzotrifluoride (PCBTF), and 2,2,4-trimethyl-1,3-pentanediol isobutyrate (Texanol), were collected in winter and summer 2023. Several compounds exhibited significantly higher averaged concentrations, with pronounced spatial and seasonal variability, distinguishing Houston from urban areas in the temperate and cooler climate zone. A customized box model was employed to estimate seasonal emissions for the Greater Houston Area, showing that emissions of most VCPs were significantly higher during the summer. This study provides critical insights into the distribution and emission of VCPs in a subtropical metropolitan area, advancing methods for assessing VCP emissions and concentrations across cities and improving understandings of their impacts on air quality, climate, and public health.

Film d'animation librement adapté du roman Perfect Blue : métamorphose d'une idole (Takeuchi, 1991), Perfect Blue (Kon, 1997) raconte l'histoire de Mima, jeune chanteuse et idole de J-pop, qui décide de poursuivre une carrière d'actrice. Alors que le choix du romancier d'adapter son œuvre en prise de vues réelles ne peut aboutir, le réalisateur et le scénariste choisissent de modifier l'histoire originale pour la réalisation d'un film d'animation explorant la confusion entre réalité, fiction et hallucination. Eloigné du roman , le film fait preuve de réflexivité en exposant les procédés de création d'une œuvre de fiction au sein de la première diégèse et se distingue par son esthétique cinématographique, où l'animation recrée les codes visuels du cinéma en prise de vues réelles. Visuellement constituée de dessins animés pour le spectateur de Perfect Blue, la série dans le film est tournée en prise de vues réelles pour les personnages et spectateurs diégétiques. Par la représentation, dans cette série, d’une scène de viol insoutenable, qui pourrait être anodine tant son caractère fictif semble a priori intensifié par sa nature dessinée, Perfect Blue interroge la porosité des frontières entre fiction et réalité au sein des images. Loin de provoquer une distanciation du public de Perfect Blue, ce questionnement sur la puissance réaliste et émotionnelle des images, renforce l’empathie qu'elles sont à même de susciter. Cet article s'efforce d'interroger les mécanismes mobilisés dans l'adaptation de Perfect Blue permettant d'accentuer le réalisme de l'œuvre lors de son passage de l'écrit à l'écran par sa nature de film d’animation.

Abstract. Wildfires have become an increasingly important source of organic gases and particulate matter in the western USA. A large fraction of organic particulate matter emitted in wildfires is semivolatile, and the oxidation of organic gases in smoke can form lower-volatility products that then condense on smoke particulates. In this research, we measured the gas- and particle-phase concentrations of semivolatile organic compounds (SVOCs) during the 2017 northern California wildfires in a downwind urban area, using semivolatile thermal desorption aerosol gas chromatography (SV-TAG), and measured SVOCs in a rural site affected by biomass burning using cTAG (comprehensive thermal desorption aerosol gas chromatography mass spectrometry) in Idaho in 2018. Commonly used biomass burning markers such as levoglucosan, mannosan, and nitrocatechols were found to stay predominantly in the particle phase, even when the ambient organic aerosol (OA) was relatively low. The phase partitioning of SVOCs is observed to be dependent on their saturation vapor pressure, while the equilibrium absorption model underpredicts the particle-phase fraction of most of the compounds measured. Wildfire organic aerosol enhanced the condensation of polar compounds into the particle phase but not some nonpolar compounds, such as polycyclic aromatic hydrocarbons.

Humidity plays an important role in the surface removal and concentrations of indoor pollutants such as ozone; however, the indoor surface dynamics and chemistry of organic peroxides is largely unknown. Organic hydroperoxides (ROOHs) are known to participate in the multiphase chemistry of outdoor aerosols and clouds, suggesting that reactive uptake in condensed grime on indoor surfaces is plausible, particularly in humid homes. Here, the effect of relative humidity (RH) on the deposition velocity (vd) and reaction probability (γ) of a model ROOH to naturally soiled indoor glass surfaces was investigated; specifically, by using authentic isoprene hydroxy hydroperoxide (1,2-ISOPOOH) as the model compound. Glass was soiled in 3 local homes for 1+ years and characterized. The removal of ISOPOOH by soiled and clean glass was measured under 5-6%, 56-58%, and 83-84% RH conditions using a novel flow reactor designed for indoor surfaces coupled to an iodide chemical ionization high-resolution time-of-flight mass spectrometer (I-HR-TOF-CIMS). The vd and γ increased with increasing RH, ranging from 0.001-0.059 cm s-1 and 0.4-4.6 (×10-6), respectively, on soiled glass surfaces. The vd and γ ranged from only 0.001-0.016 cm s-1 and 0.1-0.8 (×10-6), respectively, across RH conditions on clean glass, demonstrating a greater RH effect on soiled materials than clean. Loss rates calculated under humid conditions to soiled glass (∼1-6 h-1) were competitive in scale with ventilation rates in typical residences, indicating the importance of surface uptake for indoor ROOH concentrations. This work provides parameters for predictive modeling of indoor ROOHs. To our knowledge, these are the first direct measurements of the vd of an ROOH to naturally soiled indoor surfaces.