Scaling analysis of the screening length in concentrated electrolytes

The interaction between charged objects in an electrolyte solution is a fundamental question in soft matter physics. It is well-known that the electrostatic contribution to the interaction energy decays exponentially with object separation. Recent measurements reveal that, contrary to the conventional wisdom given by classic Poisson-Boltzmann theory, the decay length increases with ion concentration for concentrated electrolytes and can be an order of magnitude larger than the ion diameter in ionic liquids. We derive a simple scaling theory that explains this anomalous dependence of the decay length on ion concentration. Our theory successfully collapses the decay lengths of a wide class of salts onto a single curve. A novel prediction of our theory is that the decay length increases linearly with the Bjerrum length, which we experimentally verify by surface force measurements. Moreover, we quantitatively relate the measured decay length to classic measurements of the activity coefficient in concentrated electrolytes, thus showing that the measured decay length is indeed a bulk property of the concentrated electrolyte as well as contributing a mechanistic insight into empirical activity coefficients.Comment: To appear in Physical Review Letter

Improvement of kiln design and combustion/carbonization timing to produce charcoal from agricultural waste in Developing countries

Thesis (S.B.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2006.Includes bibliographical references (leaf 25).Current economic conditions in third world countries like Haiti are so poor that the majority of the population has no access to energy sources that people in the first world take for granted. In Haiti the last two percent of the forests are being cut down to provide energy for basic cooking to survive. In response to the situation, MIT professors and students are designing a multi-step process for making charcoal briquettes from local agricultural waste products, or biomass. The process involves the combustion and carbonization of biomass at sustained high temperature in an air-tight metal barrel kiln to produce char. The char produced from Haiti's main agricultural waste product, bagasse, must be powderized, mixed with a binder, compressed into briquettes, and finally baked. The purpose of the thesis was to improve on key areas of the charcoal making process. The goals were to: conduct and investigation into alternative kiln layouts; address safety concerns with water boiling, briquette baking, and bottom venting; design of a method for uniform and complete briquette baking using heat from the carbonizing kiln; and gain a better understanding of the importance combustion timing and sealing.(cont.) Design for affordable, low level manufacturing would be an important requirement as well. The results of the thesis were: an analysis of possible kiln designs based on the supplies typically available in developing countries; improvements to safety by using wire tethers on kiln hardware to allow kiln operators to keep a safe distance; a proposed new design for a briquette baking box with multiple briquette banks; and combustion timing and kiln insulation techniques to maximize char output.by Jason A. Martinez.S.B

Using the General Linear Model to Improve Performance in fNIRS Single Trial Analysis and Classification: A Perspective

Within a decade, single trial analysis of functional Near Infrared Spectroscopy (fNIRS) signals has gained significant momentum, and fNIRS joined the set of modalities frequently used for active and passive Brain Computer Interfaces (BCI). A great variety of methods for feature extraction and classification have been explored using state-of-the-art Machine Learning methods. In contrast, signal preprocessing and cleaning pipelines for fNIRS often follow simple recipes and so far rarely incorporate the available state-of-the-art in adjacent fields. In neuroscience, where fMRI and fNIRS are established neuroimaging tools, evoked hemodynamic brain activity is typically estimated across multiple trials using a General Linear Model (GLM). With the help of the GLM, subject, channel, and task specific evoked hemodynamic responses are estimated, and the evoked brain activity is more robustly separated from systemic physiological interference using independent measures of nuisance regressors, such as short-separation fNIRS measurements. When correctly applied in single trial analysis, e.g., in BCI, this approach can significantly enhance contrast to noise ratio of the brain signal, improve feature separability and ultimately lead to better classification accuracy. In this manuscript, we provide a brief introduction into the GLM and show how to incorporate it into a typical BCI preprocessing pipeline and cross-validation. Using a resting state fNIRS data set augmented with synthetic hemodynamic responses that provide ground truth brain activity, we compare the quality of commonly used fNIRS features for BCI that are extracted from (1) conventionally preprocessed signals, and (2) signals preprocessed with the GLM and physiological nuisance regressors. We show that the GLM-based approach can provide better single trial estimates of brain activity as well as a new feature type, i.e., the weight of the individual and channel-specific hemodynamic response function (HRF) regressor. The improved estimates yield features with higher separability, that significantly enhance accuracy in a binary classification task when compared to conventional preprocessing—on average +7.4% across subjects and feature types. We propose to adapt this well-established approach from neuroscience to the domain of single-trial analysis and preprocessing wherever the classification of evoked brain activity is of concern, for instance in BCI

On the importance of nonlinear modeling in computer performance prediction

Computers are nonlinear dynamical systems that exhibit complex and sometimes even chaotic behavior. The models used in the computer systems community, however, are linear. This paper is an exploration of that disconnect: when linear models are adequate for predicting computer performance and when they are not. Specifically, we build linear and nonlinear models of the processor load of an Intel i7-based computer as it executes a range of different programs. We then use those models to predict the processor loads forward in time and compare those forecasts to the true continuations of the time seriesComment: Appeared in "Proceedings of the 12th International Symposium on Intelligent Data Analysis

Spectroscopic distance, mass, and age estimations for APOGEE DR17

We derive distances and masses of stars from the Sloan Digital Sky Survey (SDSS) Apache Point Observatory Galactic Evolution Experiment (APOGEE) Data Release 17 (DR17) using simple neural networks. Training data for distances comes from Gaia EDR3, supplemented by literature distances for star clusters. For masses, the network is trained using asteroseismic masses for evolved stars and isochrone masses for main sequence stars. The models are trained on effective temperature, surface gravity, metallicity and carbon and nitrogen abundances. We found that our distance predictions have median fractional errors that range from $\approx 20\%$ at low log g and $\approx 10\%$ at higher log g with a standard deviation of $\approx 11\%$. The mass predictions have a standard deviation of $\pm 12\%$. Using the masses, we derive ages for evolved stars based on the correspondence between mass and age for giant stars given by isochrones. The results are compiled into a Value Added Catalog (VAC) called DistMass that contains distances and masses for 733901 independent spectra, plus ages for 396548 evolved stars.Comment: 23 pages, 18 figure

Deletion of Endothelial Estrogen Receptor Alpha Reduces Arterial Stiffness in Angiotensin II infused-Female Mice

Vascular stiffness is a naturally occurring phenomenon associated with aging, but conditions such as obesity and type 2 diabetes accelerate its development, particularly in women. The presence of vascular stiffness increases significantly the risk of cardiovascular disease (CVD). Under physiological conditions, estrogen signaling via estrogen receptor alpha (ERα) increases bioavailable nitric oxide in the endothelium and decreases stiffness. Nevertheless, large clinical trials have failed to demonstrate beneficial cardiovascular effects of estrogen therapy. Our previous work has shown that under conditions of over-nutrition, the lack of ERα ameliorates arterial stiffening in obese and insulin resistant females. Given the central role that activation of the Renin-Angiotensin-System (RAS) has in the pathogenesis of CVD, in the present study we examine the effect of an Angiotensin II (Ang II) infusion in female mice lacking endothelial cell (EC)

A population of high-redshift type-2 quasars-II. Radio Properties

We present multi-frequency radio observations of a sample of z~2 obscured (type-2) quasars in the Spitzer extragalactic First Look Survey area. We combine the public data at 1.4 GHz, used in the selection of these sources, with new observations at 610 MHz (GMRT) and at 4.9 GHz (VLA). We find the sample includes sources with steep, flat and gigahertz-peaked spectra. There are no strong correlations between the presence or absence of emission lines in the optical spectra and the radio spectral properties of the sample. However, there are no secure flat-spectrum type-2 quasars with narrow emission lines which would be problematic for unified schemes. Most of the population have straight radio spectra with spectral index alpha~1 as is expected for developed, potentially FRI-like, jets in which continous injection of relativistic electrons is accompanied by inverse-Compton losses against the cosmic microwave background.Comment: 6 pages, 2 colour figures, submitted to MNRA

Enhanced optical properties of Cd–Mg-co-doped ZnO nanoparticles induced by low crystal structure distortion

The growth of CdxMg0.125-xZn0.875O nanoparticles with yellow-orange luminescence is achieved up to 2.5 at. % Cd via a modified sol–gel process. X-ray diffraction analysis confirmed that all the nanoparticles have the hexagonal wurtzite structure. It is found that Cd doping has a considerable effect on the crystal size, microstrain, band gap, and photoluminescence of the Mg0·125Zn0·875O structure, originating from a preferred crystallographic orientation along the (101) plane of the wurtzite structure. The shift and broadening of the E2(high) mode observed in the Raman spectra due to growth-induced strain corroborates the small distortion observed in the X-ray diffraction data. The optical band gap varies from 3.21 eV to 2.74 eV, being redshifted with increasing Cd concentration (from 0 at. % to 2.5 at. %). The photoluminescence obtained with an excitation wavelength of 325 nm has a broad yellow-orange emission peak at around 640 nm due to transitions related to oxygen vacancies and interstitial oxygen atoms. We located the yellow-orange emission in the chromaticity coordinate diagram in the 2683–2777 K colour temperature region, demonstrating that CdxMg0.125-xZnO0.875 nanoparticles have potential applications in white light-emitting diodes.publishe