Electrochemical sensing of toxic gases in room temperature ionic liquids

This study investigates the electrochemical behaviour of three highly toxic gases, methylamine, chlorine and hydrogen chloride in room temperature ionic liquids (RTILs) on both conventional electrodes and screen printed electrodes (SPEs). It was found that all gases give good analytical responses and comparable limits of detection on both electrodes. This suggests that low-cost SPEs can be used in conjunction with RTILs for amperometric gas detection, which would allow faster response times and cheaper manufacturing costs

"For good design, you pay now; for bad design, you pay later"--or do you?

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Urban Studies and Planning, 2006.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (leaves 117-118).What is the value of architectural design on office building income? This empirical study of 296 office building located in 11 Metropolitan Statistical Areas (MSA) hopes to quantitatively determine if a plain vanilla cereal box suburban office building commands more or less net operating income than an office building with a higher level of design. Previous empirical studies have found a strong influence of design on rents but were limited in geography, building characteristics and total number of observations. In an important study by Vandell and Lane (1990), they found that good architecture commanded a premium of over 20% in office rents. Also, their study showed that good design cost more to produce on average, but not necessarily in every case. Data was gathered from a portfolio of US office buildings and consisted of building metrics and property level 2000-2005 Net Operating Income (NOI). This base data set, MSA dummy variables and architectural attribute dummy variables (created by the authors) formed the backbone of the research. Multiple log linear regression analysis was conducted to identify the economic effects of good design.(cont.) In addition, a survey taken by 31 architects was used to capture subjective rankings on the all 296 office buildings to determine if there is a consensus as to what constitutes good design. It is hoped that these professionals, who are formally trained and are practicing in the field, are well-qualified to evaluate the design of each building. The survey results showed that the architects' responses are idiosyncratic and subjective. Not only did the individual participant's rankings show no significant relationship with one another, but also did not exhibit any relationship with actual building NOI. The empirical study found that the market paid a premium of 7.9% for buildings with non-center cores. Also, a significant 11.7-13.2% premium was paid for properties with non-rectangular and non-square shaped floor plans. Finally, buildings with 60% to 90% exterior windows commanded a substantial 10.7% premium. These results imply that better-designed buildings generate higher NOI either because the tenants are willing to pay a premium or because the operating costs of the building are less, or both.by Meena Murugappan and S. Michael O'Young, Jr.S.M

Electrochemical Behavior of Chlorine on Platinum Microdisk and Screen-Printed Electrodes in a Room Temperature Ionic Liquid

As a result of the toxic and corrosive nature of chlorine gas, simple methods for its detection are required for monitoring and control purposes. In this paper, the electrochemical behavior of chlorine on platinum working electrodes in the room temperature ionic liquid (RTIL) 1-ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide ([C2mim][NTf2]) is reported, as a basis for simple sensor devices. Cyclic voltammetry (CV) and chronoamperometry (CA) on a Pt microelectrode revealed the two-electron reduction of Cl2 to chloride ions. On the CV reverse sweep, an oxidation peak due to the oxidation of chloride was observed. The reduction process was diffusion controlled at the concentrations studied (≤4.5% in the gas phase), in contrast to a previous report (J. Phys. Chem. C2008, 112, 19477), which examined only 100% chlorine. The diffusion-controlled currents were linear with gas-phase concentration. Fitting of the CA transients to the Shoup and Szabo expression gave a diffusion coefficient for chlorine in the RTIL of ca. 2.6 × 10–10 m2 s–1. Furthermore, determination of the equilibrium concentration of Cl2 in the RTIL phase as a function of gas-phase concentration enabled a value of 35 to be determined for the Henry’s law dimensionless volatility constant. The electrochemical behavior of chlorine on a Pt screen-printed electrode was also investigated, suggesting that these devices may be useful for chlorine detection in conjunction with suitable RTILs

Performance Evaluation of a High Bandwidth Liquid Fuel Modulation Valve for Active Combustion Control

At the NASA Glenn Research Center, a characterization rig was designed and constructed for the purpose of evaluating high bandwidth liquid fuel modulation devices to determine their suitability for active combustion control research. Incorporated into the rig s design are features that approximate conditions similar to those that would be encountered by a candidate device if it were installed on an actual combustion research rig. The characterized dynamic performance measures obtained through testing in the rig are planned to be accurate indicators of expected performance in an actual combustion testing environment. To evaluate how well the characterization rig predicts fuel modulator dynamic performance, characterization rig data was compared with performance data for a fuel modulator candidate when the candidate was in operation during combustion testing. Specifically, the nominal and off-nominal performance data for a magnetostrictive-actuated proportional fuel modulation valve is described. Valve performance data were collected with the characterization rig configured to emulate two different combustion rig fuel feed systems. Fuel mass flows and pressures, fuel feed line lengths, and fuel injector orifice size was approximated in the characterization rig. Valve performance data were also collected with the valve modulating the fuel into the two combustor rigs. Comparison of the predicted and actual valve performance data show that when the valve is operated near its design condition the characterization rig can appropriately predict the installed performance of the valve. Improvements to the characterization rig and accompanying modeling activities are underway to more accurately predict performance, especially for the devices under development to modulate fuel into the much smaller fuel injectors anticipated in future lean-burning low-emissions aircraft engine combustors

Emotion classification in Parkinson's disease by higher-order spectra and power spectrum features using EEG signals: A comparative study

Deficits in the ability to process emotions characterize several neuropsychiatric disorders and are traits of Parkinson's disease (PD), and there is need for a method of quantifying emotion, which is currently performed by clinical diagnosis. Electroencephalogram (EEG) signals, being an activity of central nervous system (CNS), can reflect the underlying true emotional state of a person. This study applied machine-learning algorithms to categorize EEG emotional states in PD patients that would classify six basic emotions (happiness and sadness, fear, anger, surprise and disgust) in comparison with healthy controls (HC). Emotional EEG data were recorded from 20 PD patients and 20 healthy age-, education level- and sex-matched controls using multimodal (audio-visual) stimuli. The use of nonlinear features motivated by the higher-order spectra (HOS) has been reported to be a promising approach to classify the emotional states. In this work, we made the comparative study of the performance of k-nearest neighbor (kNN) and support vector machine (SVM) classifiers using the features derived from HOS and from the power spectrum. Analysis of variance (ANOVA) showed that power spectrum and HOS based features were statistically significant among the six emotional states (p < 0.0001). Classification results shows that using the selected HOS based features instead of power spectrum based features provided comparatively better accuracy for all the six classes with an overall accuracy of 70.10% ± 2.83% and 77.29% ± 1.73% for PD patients and HC in beta (13-30 Hz) band using SVM classifier. Besides, PD patients achieved less accuracy in the processing of negative emotions (sadness, fear, anger and disgust) than in processing of positive emotions (happiness, surprise) compared with HC. These results demonstrate the effectiveness of applying machine learning techniques to the classification of emotional states in PD patients in a user independent manner using EEG signals. The accuracy of the system can be improved by investigating the other HOS based features. This study might lead to a practical system for noninvasive assessment of the emotional impairments associated with neurological disorders

Affective recognition from EEG signals: an integrated data-mining approach

Emotions play an important role in human communication, interaction, and decision making processes. Therefore, considerable efforts have been made towards the automatic identification of human emotions, in particular electroencephalogram (EEG) signals and Data Mining (DM) techniques have been then used to create models recognizing the affective states of users. However, most previous works have used clinical grade EEG systems with at least 32 electrodes. These systems are expensive and cumbersome, and therefore unsuitable for usage during normal daily activities. Smaller EEG headsets such as the Emotiv are now available and can be used during daily activities. This paper investigates the accuracy and applicability of previous affective recognition methods on data collected with an Emotiv headset while participants used a personal computer to fulfill several tasks. Several features were extracted from four channels only (AF3, AF4, F3 and F4 in accordance with the 10–20 system). Both Support Vector Machine and Naïve Bayes were used for emotion classification. Results demonstrate that such methods can be used to accurately detect emotions using a small EEG headset during a normal daily activity

Inter-hemispheric EEG coherence analysis in Parkinson's disease : Assessing brain activity during emotion processing

Parkinson’s disease (PD) is not only characterized by its prominent motor symptoms but also associated with disturbances in cognitive and emotional functioning. The objective of the present study was to investigate the influence of emotion processing on inter-hemispheric electroencephalography (EEG) coherence in PD. Multimodal emotional stimuli (happiness, sadness, fear, anger, surprise, and disgust) were presented to 20 PD patients and 30 age-, education level-, and gender-matched healthy controls (HC) while EEG was recorded. Inter-hemispheric coherence was computed from seven homologous EEG electrode pairs (AF3–AF4, F7–F8, F3–F4, FC5–FC6, T7–T8, P7–P8, and O1–O2) for delta, theta, alpha, beta, and gamma frequency bands. In addition, subjective ratings were obtained for a representative of emotional stimuli. Interhemispherically, PD patients showed significantly lower coherence in theta, alpha, beta, and gamma frequency bands than HC during emotion processing. No significant changes were found in the delta frequency band coherence. We also found that PD patients were more impaired in recognizing negative emotions (sadness, fear, anger, and disgust) than relatively positive emotions (happiness and surprise). Behaviorally, PD patients did not show impairment in emotion recognition as measured by subjective ratings. These findings suggest that PD patients may have an impairment of inter-hemispheric functional connectivity (i.e., a decline in cortical connectivity) during emotion processing. This study may increase the awareness of EEG emotional response studies in clinical practice to uncover potential neurophysiologic abnormalities