Integrated impedance bridge for absolute capacitance measurements at cryogenic temperatures and finite magnetic fields

We developed an impedance bridge that operates at cryogenic temperatures (down to 60 mK) and in perpendicular magnetic fields up to at least 12 T. This is achieved by mounting a GaAs HEMT amplifier perpendicular to a printed circuit board containing the device under test and thereby parallel to the magnetic field. The measured amplitude and phase of the output signal allows for the separation of the total impedance into an absolute capacitance and a resistance. Through a detailed noise characterization, we find that the best resolution is obtained when operating the HEMT amplifier at the highest gain. We obtained a resolution in the absolute capacitance of 6.4~aF$/\sqrt{\textrm{Hz}}$ at 77 K on a comb-drive actuator, while maintaining a small excitation amplitude of 15~$k_\text{B} T/e$. We show the magnetic field functionality of our impedance bridge by measuring the quantum Hall plateaus of a top-gated hBN/graphene/hBN heterostructure at 60~mK with a probe signal of 12.8~$k_\text{B} T/e$.Comment: 7 pages, 5 figure

Pre-processing and differential expression analysis of Agilent microRNA arrays using the AgiMicroRna Bioconductor library

<p>Abstract</p> <p>Background</p> <p>The main research tool for identifying microRNAs involved in specific cellular processes is gene expression profiling using microarray technology. Agilent is one of the major producers of microRNA arrays, and microarray data are commonly analyzed by using R and the functions and packages collected in the Bioconductor project. However, an analytical package that integrates the specific characteristics of microRNA Agilent arrays has been lacking.</p> <p>Results</p> <p>This report presents the new bioinformatic tool <it>AgiMicroRNA </it>for the pre-processing and differential expression analysis of Agilent microRNA array data. The software is implemented in the open-source statistical scripting language R and is integrated in the Bioconductor project (<url>http://www.bioconductor.org</url>) under the GPL license. For the pre-processing of the microRNA signal, <it>AgiMicroRNA </it>incorporates the <it>robust multiarray average algorithm</it>, a method that produces a summary measure of the microRNA expression using a linear model that takes into account the probe affinity effect. To obtain a normalized microRNA signal useful for the statistical analysis, <it>AgiMicroRna </it>offers the possibility of employing either the processed signal estimated by the <it>robust multiarray average algorithm </it>or the processed signal produced by the Agilent image analysis software. The <it>AgiMicroRNA </it>package also incorporates different graphical utilities to assess the quality of the data. <it>AgiMicroRna </it>uses the linear model features implemented in the <it>limma </it>package to assess the differential expression between different experimental conditions and provides links to the <it>miRBase </it>for those microRNAs that have been declared as significant in the statistical analysis.</p> <p>Conclusions</p> <p><it>AgiMicroRna </it>is a rational collection of Bioconductor functions that have been wrapped into specific functions in order to ease and systematize the pre-processing and statistical analysis of Agilent microRNA data. The development of this package contributes to the Bioconductor project filling the gap in microRNA array data analysis.</p

Identification and quantification of microplastics in wastewater using focal plane array-based reflectance micro-FT-IR imaging

Microplastics (<5 mm) have been documented in environmental samples on a global scale. While these pollutants may enter aquatic environments via wastewater treatment facilities, the abundance of microplastics in these matrices has not been investigated. Although efficient methods for the analysis of microplastics in sediment samples and marine organisms have been published, no methods have been developed for detecting these pollutants within organic-rich wastewater samples. In addition, there is no standardized method for analyzing microplastics isolated from environmental samples. In many cases, part of the identification protocol relies on visual selection before analysis, which is open to bias. In order to address this, a new method for the analysis of microplastics in wastewater was developed. A pretreatment step using 30% hydrogen peroxide (H2O2) was employed to remove biogenic material, and focal plane array (FPA)-based reflectance micro-Fourier-transform (FT-IR) imaging was shown to successfully image and identify different microplastic types (polyethylene, polypropylene, nylon-6, polyvinyl chloride, polystyrene). Microplastic-spiked wastewater samples were used to validate the methodology, resulting in a robust protocol which was nonselective and reproducible (the overall success identification rate was 98.33%). The use of FPA-based micro-FT-IR spectroscopy also provides a considerable reduction in analysis time compared with previous methods, since samples that could take several days to be mapped using a single-element detector can now be imaged in less than 9 h (circular filter with a diameter of 47 mm). This method for identifying and quantifying microplastics in wastewater is likely to provide an essential tool for further research into the pathways by which microplastics enter the environment.This work is funded by a NERC (Natural Environment Research Council) CASE studentship (NE/K007521/1) with contribution from industrial partner Fera Science Ltd., United Kingdom. The authors would like to thank Peter Vale, from Severn Trent Water Ltd, for providing access to and additionally Ashley Howkins (Brunel University London) for providing travel and assistance with the sampling of the Severn Trent wastewater treatment plant in Derbyshire, UK. We are grateful to Emma Bradley and Chris Sinclair for providing helpful suggestions for our research

The Need for Impulse Response Models and an Accurate Method for Impulse Generation from Band- Limited S-Parameters

In the signal integrity industry, S-parameters have become the most commonly distributed models for simulating passive components. Because S-parameters are frequency-domain values, many signal integrity engineers are finding it difficult to accurately implement these parameters in time-domain simulations. Addressing transient convolution specifically, this paper shows the need for a new impulse response model and proposes a methodology for its inclusion in timedomain simulations. The paper also solves another difficult problem by presenting a new technique for converting band-limited S-parameters into ‘base-delay ’ causal and passive impulse response models that preserve accuracy to the maximum frequency of the original S-parameters. Authors Biographies Fangyi Rao received his Ph.D. degree in physics from Northwestern University in 1997, for research in quantum theory of magnetism and transport. He joined Agilent EEsof in 2006 as