Lauri Ahonen

Characterization of a Herrmann-type high-resolution differential mobility analyzer

ABSTRACT Aerosol instrument characterization and verification for nanometer-sized particles requires well-established generation and classification instruments. A precise size selection of sub-3-nm charged aerosol particles requires a differential mobility analyzer (DMA), specially designed for the sub-3-nm size range. In this study, a Herrmann-type high-resolution DMA developed at Yale University was characterized in various operation conditions. A relation between sheath flow rate and tetraheptylammonium ion (C28H60N+, THA+, 1.47 nm, mobility equivalent diameter) was established. The maximum particle size that the DMA was able to classify was 2.9 nm with the highest sheath flow rate of 1427 liters per minute (Lpm), and 6.5 nm with the lowest stable sheath flow rate of 215 Lpm, restricted by the maximum and minimum flow rates provided by our blower. Resolution and transmission of DMA are reported for tetrapropylammonium (C12H28N+, TPA+, 1.16 nm), THA+, and THA2Br+ (1.78 nm) ions measured with two different central electrodes and five different sheath flow rates. The transmission varied between 0.01 and 0.22, and the resolution varied between 10.8 and 51.9, depending on the operation conditions. Copyright

Job burnout is associated with dysfunctions in brain mechanisms of voluntary and involuntary attention

Individuals with job burnout symptoms often report having cognitive difficulties, but related electrophysiological studies are scarce. We assessed the impact of burnout on performing a visual task with varying memory loads, and on involuntary attention switch to distractor sounds using scalp recordings of event-related potentials (ERPs). Task performance was comparable between burnout and control groups. The distractor sounds elicited a P3a response, which was reduced in the burnout group. This suggests burnout-related deficits in processing novel and potentially important events during task performance. In the burnout group, we also observed a decrease in working-memory related P3b responses over posterior scalp and increase over frontal areas. These results suggest that burnout is associated with deficits in cognitive control needed to monitor and update information in working memory. Successful task performance in burnout might require additional recruitment of anterior regions to compensate the decrement in posterior activity.

Sub-3 nm Particle Detection with Commercial TSI 3772 and Airmodus A20 Fine Condensation Particle Counters

In this work, we explored the possibility to detect sub-3 nm particles with commercially available TSI 3772 and Airmodus A20 Condensation Particle Counters (CPCs), when operated under modified temperature and inlet flow settings. We generated highly monodisperse sub-3 nm nanoparticles and characterized the CPCs with temperature differences between the saturator and the condenser varying from 36ºC (the 36/37 settings) to 40ºC (the 40/40 settings), while the factory settings were 17 and 24ºC. The 36/37 settings yielded no homogeneously nucleated background in dry conditions. With these settings, the detection efficiency was significantly improved from the factory settings, resulting in the detection of the smallest charged particles down to below 1.5 nm compared with the nominal cut-sizes of 10 and 7 nm. With the 40/40 settings and consequently higher supersaturation, homogeneous nucleation produced a background of around 0.5–2 cm−3, while the CPCs were sensitive to charged particles down to 1 nm in mobility diameter. The supersaturation field corresponding to the new operation conditions with the 36/37 settings was modeled by using COMSOL and OpenFOAM. The observations were reproduced very well by applying the heterogeneous nucleation theory to the obtained supersaturation field. Our work shows that the TSI 3772 and Airmodus A20 fine CPCs can have a comparable performance with a more expensive ultrafine CPC, such as TSI 3776, thus offering a widely available tool for the detection of sub-3 nm particles. Copyright 2015 American Association for Aerosol Research

Cognitive Collaboration Found in Cardiac Physiology: Study in Classroom Environment

It is known that periods of intense social interaction result in shared patterns in collaborators’ physiological signals. However, applied quantitative research on collaboration is hindered due to scarcity of objective metrics of teamwork effectiveness. Indeed, especially in the domain of productive, ecologically-valid activity such as programming, there is a lack of evidence for the most effective, affordable and reliable measures of collaboration quality. In this study we investigate synchrony in physiological signals between collaborating computer science students performing pair-programming exercises in a class room environment. We recorded electrocardiography over the course of a 60 minute programming session, using lightweight physiological sensors. We employ correlation of heart-rate variability features to study social psychophysiological compliance of the collaborating students. We found evident physiological compliance in collaborating dyads’ heart-rate variability signals. Furthermore, dyads’ self-reported workload was associated with the physiological compliance. Our results show viability of a novel approach to field measurement using lightweight devices in an uncontrolled environment, and suggest that self-reported collaboration quality can be assessed via physiological signals.

Solar eclipse demonstrating the importance of photochemistry in new particle formation

Solar eclipses provide unique possibilities to investigate atmospheric processes, such as new particle formation (NPF), important to the global aerosol load and radiative balance. The temporary absence of solar radiation gives particular insight into different oxidation and clustering processes leading to NPF. This is crucial because our mechanistic understanding on how NPF is related to photochemistry is still rather limited. During a partial solar eclipse over Finland in 2015, we found that this phenomenon had prominent effects on atmospheric on-going NPF. During the eclipse, the sources of aerosol precursor gases, such as sulphuric acid and nitrogen- containing highly oxidised organic compounds, decreased considerably, which was followed by a reduced formation of small clusters and nanoparticles and thus termination of NPF. After the eclipse, aerosol precursor molecule concentrations recovered and re-initiated NPF. Our results provide direct evidence on the key role of the photochemical production of sulphuric acid and highly oxidized organic compounds in maintaining atmospheric NPF. Our results also explain the rare occurrence of this phenomenon under dark conditions, as well as its seemingly weak connection with atmospheric ions.

Biosignals reflect pair-dynamics in collaborative work: EDA and ECG study of pair-programming in a classroom environment

Collaboration is a complex phenomenon, where intersubjective dynamics can greatly affect the productive outcome. Evaluation of collaboration is thus of great interest, and can potentially help achieve better outcomes and performance. However, quantitative measurement of collaboration is difficult, because much of the interaction occurs in the intersubjective space between collaborators. Manual observation and/or self-reports are subjective, laborious, and have a poor temporal resolution. The problem is compounded in natural settings where task-activity and response-compliance cannot be controlled. Physiological signals provide an objective mean to quantify intersubjective rapport (as synchrony), but require novel methods to support broad deployment outside the lab. We studied 28 student dyads during a self-directed classroom pair-programming exercise. Sympathetic and parasympathetic nervous system activation was measured during task performance using electrodermal activity and electrocardiography. Results suggest that (a) we can isolate cognitive processes (mental workload) from confounding environmental effects, and (b) electrodermal signals show role-specific but correlated affective response profiles. We demonstrate the potential for social physiological compliance to quantify pair-work in natural settings, with no experimental manipulation of participants required. Our objective approach has a high temporal resolution, is scalable, non-intrusive, and robust.

First measurements of the number size distribution of 1–2 nm aerosol particles released from manufacturing processes in a cleanroom environment

ABSTRACT This study was conducted to observe a potential formation and/or release of aerosol particles related to manufacturing processes inside a cleanroom. We introduce a novel technique to monitor airborne sub 2 nm particles in the cleanroom and present results from a measurement campaign during which the total particle number concentration (>1 nm and >7 nm) and the size resolved concentration in the 1 to 2 nm size range were measured. Measurements were carried out in locations where atomic layer deposition (ALD), sputtering, and lithography processes were conducted, with a wide variety of starting materials. During our campaign in the clean room, we observed several time periods when the particle number concentration was 105 cm−3 in the sub 2 nm size range and 104 cm−3 in the size class larger than 7 nm in one of the sampling locations. The highest concentrations were related to the maintenance processes of the manufacturing machines, which were conducted regularly in that specific location. Our measurements show that around 500 cm−3 sub 2 nm particles or clusters were in practice always present in this specific cleanroom, while the concentration of particles larger than 2 nm was less than 2 cm−3. During active processes, the concentrations of sub 2 nm particles could rise to over 105 cm−3 due to an active new particle formation. The new particle formation was most likely induced by a combination of the supersaturated vapors, released from the machines, and the very low existing condensation sink, leading to pretty high formation rates J1.4 nm = (9 ± 4) cm−3 s−1 and growth rates of particles (GR1.1–1.3 nm = (6 ± 3) nm/h and GR1.3–1.8 nm = (14 ± 3) nm/h). Copyright

Rapid growth of organic aerosol nanoparticles over a wide tropospheric temperature range

Significance Aerosol particles can form and grow by gas-to-particle conversion and eventually act as seeds for cloud droplets, influencing global climate. Volatile organic compounds emitted from plants are oxidized in the atmosphere, and the resulting products drive particle growth. We measure particle growth by oxidized biogenic vapors with a well-controlled laboratory setup over a wide range of tropospheric temperatures. While higher temperatures lead to increased reaction rates and concentrations of highly oxidized molecules, lower temperatures allow additional, but less oxidized, species to condense. We measure rapid growth over the full temperature range of our study, indicating that organics play an important role in aerosol growth throughout the troposphere. Our finding will help to sharpen the predictions of global aerosol models. Nucleation and growth of aerosol particles from atmospheric vapors constitutes a major source of global cloud condensation nuclei (CCN). The fraction of newly formed particles that reaches CCN sizes is highly sensitive to particle growth rates, especially for particle sizes <10 nm, where coagulation losses to larger aerosol particles are greatest. Recent results show that some oxidation products from biogenic volatile organic compounds are major contributors to particle formation and initial growth. However, whether oxidized organics contribute to particle growth over the broad span of tropospheric temperatures remains an open question, and quantitative mass balance for organic growth has yet to be demonstrated at any temperature. Here, in experiments performed under atmospheric conditions in the Cosmics Leaving Outdoor Droplets (CLOUD) chamber at the European Organization for Nuclear Research (CERN), we show that rapid growth of organic particles occurs over the range from −25 °C to 25 °C. The lower extent of autoxidation at reduced temperatures is compensated by the decreased volatility of all oxidized molecules. This is confirmed by particle-phase composition measurements, showing enhanced uptake of relatively less oxygenated products at cold temperatures. We can reproduce the measured growth rates using an aerosol growth model based entirely on the experimentally measured gas-phase spectra of oxidized organic molecules obtained from two complementary mass spectrometers. We show that the growth rates are sensitive to particle curvature, explaining widespread atmospheric observations that particle growth rates increase in the single-digit-nanometer size range. Our results demonstrate that organic vapors can contribute to particle growth over a wide range of tropospheric temperatures from molecular cluster sizes onward.

Data inversion methods to determine sub-3 nm aerosol size distributions using the particle size magnifier

Measuring particle size distribution accurately down to approximately 1 nm is needed for studying atmospheric new particle formation. The scanning particle size magnifier (PSM) using diethylene glycol as a working fluid has been used for measuring sub-3 nm atmospheric aerosol. A proper inversion method is required to recover the particle size distribution from PSM raw data. Similarly to other aerosol spectrometers and classifiers, PSM inversion can be deduced from a problem described by the Fredholm integral equation of the first kind. We tested the performance of the stepwise method, the kernel function method (Lehtipalo et al., 2014), the H&A linear inversion method (Hagen and Alofs, 1983), and the expectation–maximization (EM) algorithm. The stepwise method and the kernel function method were used in previous studies on PSM. The H&A method and the expectation–maximization algorithm were used in data inversion for the electrical mobility spectrometers and the diffusion batteries, respectively (Maher and Laird, 1985). In addition, Monte Carlo simulation and laboratory experiments were used to test the accuracy and precision of the particle size distributions recovered using four inversion methods. When all of the detected particles are larger than 3 nm, the stepwise method may report false sub-3 nm particle concentrations because an infinite resolution is assumed while the kernel function method and the H&A method occasionally report false sub-3 nm particles because of the unstable least squares method. The accuracy and precision of the recovered particle size distribution using the EM algorithm are the best among the tested four inversion methods. Compared to the kernel function method, the H&A method reduces the uncertainty while keeping a similar computational expense. The measuring uncertainties in the present scanning mode may contribute to the uncertainties of the recovered particle size distributions. We suggest using the EM algorithm to retrieve the particle size distributions using the particle number concentrations recorded by the PSM. Considering the relatively high computation expenses of the EM algorithm, the H&A method is recommended for preliminary data analysis. We also gave practical suggestions on PSM operation based on the inversion analysis.

Using regression makes extraction of shared variation in multiple datasets easy

In many data analysis tasks it is important to understand the relationships between different datasets. Several methods exist for this task but many of them are limited to two datasets and linear relationships. In this paper, we propose a new efficient algorithm, termed cocoreg, for the extraction of variation common to all datasets in a given collection of arbitrary size. cocoreg extends redundancy analysis to more than two datasets, utilizing chains of regression functions to extract the shared variation in the original data space. The algorithm can be used with any linear or non-linear regression function, which makes it robust, straightforward, fast, and easy to implement and use. We empirically demonstrate the efficacy of shared variation extraction using the cocoreg algorithm on five artificial and three real datasets.