Imaging Techniques and Scanning Electron Microscopy as Tools for Characterizing a Si-Based Material Used in Air Monitoring Applications.
Ontology highlight
ABSTRACT:
SUBMITTER: Beatriz SP
PROVIDER: S-EPMC5456495 | biostudies-other | 2016 Feb
REPOSITORIES: biostudies-other
Similar Datasets
Project description:Quantum dots patterned by atomically precise placement of phosphorus donors in single crystal silicon have long spin lifetimes, advantages in addressability, large exchange tunability, and are readily available few-electron systems. To be utilized as quantum bits, it is important to non-invasively characterise these donor quantum dots post fabrication and extract the number of bound electron and nuclear spins as well as their locations. Here, we propose a metrology technique based on electron spin resonance (ESR) measurements with the on-chip circuitry already needed for qubit manipulation to obtain atomic scale information about donor quantum dots and their spin configurations. Using atomistic tight-binding technique and Hartree self-consistent field approximation, we show that the ESR transition frequencies are directly related to the number of donors, electrons, and their locations through the electron-nuclear hyperfine interaction.
| S-EPMC4994117 | biostudies-literature
Project description:Background: Digital clinical measures collected via various digital sensing technologies such as smartphones, smartwatches, wearables, and ingestible and implantable sensors are increasingly used by individuals and clinicians to capture the health outcomes or behavioral and physiological characteristics of individuals. Time series classification (TSC) is very commonly used for modeling digital clinical measures. While deep learning models for TSC are very common and powerful, there exist some fundamental challenges. This review presents the non-deep learning models that are commonly used for time series classification in biomedical applications that can achieve high performance. Objective: We performed a systematic review to characterize the techniques that are used in time series classification of digital clinical measures throughout all the stages of data processing and model building. Methods: We conducted a literature search on PubMed, as well as the Institute of Electrical and Electronics Engineers (IEEE), Web of Science, and SCOPUS databases using a range of search terms to retrieve peer-reviewed articles that report on the academic research about digital clinical measures from a five-year period between June 2016 and June 2021. We identified and categorized the research studies based on the types of classification algorithms and sensor input types. Results: We found 452 papers in total from four different databases: PubMed, IEEE, Web of Science Database, and SCOPUS. After removing duplicates and irrelevant papers, 135 articles remained for detailed review and data extraction. Among these, engineered features using time series methods that were subsequently fed into widely used machine learning classifiers were the most commonly used technique, and also most frequently achieved the best performance metrics (77 out of 135 articles). Statistical modeling (24 out of 135 articles) algorithms were the second most common and also the second-best classification technique. Conclusions: In this review paper, summaries of the time series classification models and interpretation methods for biomedical applications are summarized and categorized. While high time series classification performance has been achieved in digital clinical, physiological, or biomedical measures, no standard benchmark datasets, modeling methods, or reporting methodology exist. There is no single widely used method for time series model development or feature interpretation, however many different methods have proven successful.
| S-EPMC9611376 | biostudies-literature
Project description:DNA reference materials of certified value have a critical function in many analytical processes of DNA measurement. Quantification of amoA genes in ammonia oxidizing bacteria (AOB) and archaea (AOA), and of nirS and nosZ genes in the denitrifiers is very important for determining their distribution and abundance in the natural environment. A plasmid reference material containing nirS, nosZ, amoA-AOB, and amoA-AOA is developed to provide a DNA standard with copy number concentration for ensuring comparability and reliability of quantification of these genes. Droplet digital PCR (ddPCR) was evaluated for characterization of the plasmid reference material. The result revealed that restriction endonuclease digestion of plasmids can improve amplification efficiency and minimize the measurement bias of ddPCR. Compared with the conformation of the plasmid, the size of the DNA fragment containing the target sequence and the location of the restriction site relative to the target sequence are not significant factors affecting plasmid quantification by ddPCR. Liquid chromatography-isotope dilution mass spectrometry (LC-IDMS) was used to provide independent data for quantifying the plasmid reference material. The copy number concentration of the digested plasmid determined by ddPCR agreed well with that determined by LC-IDMS, improving both the accuracy and reliability of the plasmid reference material. The reference value, with its expanded uncertainty (k = 2), of the plasmid reference material was determined to be (5.19 ± 0.41) × 10(9) copies μL(-1) by averaging the results of two independent measurements. Consideration of the factors revealed in this study can improve the reliability and accuracy of ddPCR; thus, this method has the potential to accurately quantify DNA reference materials.
| S-EPMC3936116 | biostudies-literature
Project description:The performance of Signal Quality Monitoring (SQM) techniques under different multipath scenarios is analyzed. First, SQM variation profiles are investigated as critical requirements in evaluating the theoretical performance of SQM metrics. The sensitivity and effectiveness of SQM approaches for multipath detection and mitigation are then defined and analyzed by comparing SQM profiles and multipath error envelopes for different discriminators. Analytical discussions includes two discriminator strategies, namely narrow and high resolution correlator techniques for BPSK(1), and BOC(1,1) signaling schemes. Data analysis is also carried out for static and kinematic scenarios to validate the SQM profiles and examine SQM performance in actual multipath environments. Results show that although SQM is sensitive to medium and long-delay multipath, its effectiveness in mitigating these ranges of multipath errors varies based on tracking strategy and signaling scheme. For short-delay multipath scenarios, the multipath effect on pseudorange measurements remains mostly undetected due to the low sensitivity of SQM metrics.
| S-EPMC5539492 | biostudies-other
Project description:Environmental scanning electron microscopy (ESEM) is a powerful technique that enables imaging of diverse specimens (e.g., biomaterials, chemical materials, nanomaterials) in a hydrated or native state while simultaneously maintaining micro-to-nanoscale resolution. However, it is difficult to achieve high signal-to-noise and artifact-free secondary electron images in a high-pressure gaseous environment due to the intensive electron-gas collisions. In addition, nanotextured substrates can mask the signal from a weakly scattering sample. These drawbacks limit the study of material dynamics under extreme conditions and correspondingly our understanding in many fields. In this work, an imaging framework called Quasi-Newtonian ESEM is proposed, which introduces the concepts of quasi-force and quasi-work by referencing the scattering force in light-matter interactions, to break these barriers without any hardware changes. It is shown that quasi-force is a more fundamental quantity that has a more significant connection with the sample morphology than intensity in the strongly scattering regime. Experimental and theoretical studies on the dynamics of droplet condensation in a high-pressure environment (up to 2500 Pa) successfully demonstrate the effectiveness and robustness of the framework and that the overwhelmed signal of interest in ESEM images can be reconstructed through information stored in the time domain, i.e., frames captured at different moments.
| S-EPMC7539182 | biostudies-literature
Project description:While the search for post-fellowship employment is an essential part of early career development, little is known about the tools and techniques fellows use during their job search and the specific challenges they face during the process. In this pilot survey study of hematology-oncology (HO) fellows at a large academic training program, the majority of 20 respondents (43% of all fellows) reported a plan to specialize in medical oncology, and most planned to practice in an academic setting. Fellows who had started the job search process reported using several tools/techniques including online job centers and email distribution lists, word of mouth, prior connections with other institutions, and their HO fellowship program leadership, and most reported that their current institution provided help for their job search in at least one way. Job search challenges included learning about available positions, lack of mentorship on the process, lack of sufficient time for the process, and lack of preparation for negotiation. Fellows suggested additional resources that could be helpful to future job searchers including access to information about prior HO fellowship graduates, training in negotiation and interviewing, and career development mentorship. We plan to use this information to expand our own Career Development program for fellows enacting many of these suggestions, and we encourage the use of this information as pilot data for the development of larger studies across other medical and surgical specialties.
| S-EPMC7847292 | biostudies-literature
Project description:Surface enhanced Raman spectroscopy (SERS) has been widely investigated as an effective technique for low-concentration bio-chemical molecules detection. A rapid two-step approach to fabricate SERS substrates with high controllability in ambient air is developed. Dynamic laser ablation directly creates microgroove on the Si substrate. Meanwhile, nanoparticles are synthesized via the nucleation of laser induced plasma species and the air molecules. It configures the Si surface into four different regions decorated with nanoparticles at different sizes. With Ag film coating, these nanoparticles function as hotspots for SERS. Microsquare arrays are fabricated on the Si surface as large-area SERS substrates by the laser ablation in horizontal and vertical directions. In each microsquare, it exhibits quasi-3D structures with randomly arranged and different shaped nanoparticles aggregated in more than one layer. With Ag film deposition, uniform SERS signals are obtained by detecting the 4-methylbenzenethiol molecules. The SERS signal intensity is determined by the size and shape distributions of the nanoparticles, which depend on the laser processing parameters. With the optimal laser fluence, the SERS signals show a uniform enhancement factor up to 5.5 × 10(6). This provides a high-speed and low-cost method to produce SERS substrates over a large area.
| S-EPMC4200404 | biostudies-other
Project description:This paper provides an overview of how molecule-sensitive, spatially-resolved technologies can be applied for monitoring and measuring in microchannels. The principles of elastic light scattering, fluorescence, near-infrared, mid-infrared, and Raman imaging, as well as combination techniques, are briefly presented, and their advantages and disadvantages are explained. With optical methods, images can be acquired both scanning and simultaneously as a complete image. Scanning technologies require more acquisition time, and fast moving processes are not easily observable. On the other hand, molecular selectivity is very high, especially in Raman and mid-infrared (MIR) scanning. For near-infrared (NIR) images, the entire measuring range can be simultaneously recorded with indium gallium arsenide (InGaAs) cameras. However, in this wavelength range, water is the dominant molecule, so it is sometimes necessary to use complex learning algorithms that increase the preparation effort before the actual measurement. These technologies excite molecular vibrations in a variety of ways, making these methods suitable for specific products. Besides measurements of the fluid composition, technologies for particle detection are of additional importance. With scattered light techniques and evaluation according to the Mie theory, particles in the range of 0.2-1 mm can be detected, and fast growth processes can be observed. Local multispectral measurements can also be carried out with fiber optic-coupled systems through small probe heads of approximately 1 mm diameter.
| S-EPMC7230497 | biostudies-literature
Project description:Methods from non-linear dynamics have enhanced understanding of functional dysregulation in various diseases but received less attention in diabetes. This retrospective cross-sectional study evaluates and compares relationships between indices of non-linear dynamics and traditional glycemic variability, and their potential application in diabetes control. Continuous glucose monitoring provided data for 177 subjects with type 1 (n = 22), type 2 diabetes (n = 143), and 12 non-diabetic subjects. Each time series comprised 576 glucose values. We calculated Poincaré plot measures (SD1, SD2), shape (SFE) and area of the fitting ellipse (AFE), multiscale entropy (MSE) index, and detrended fluctuation exponents (?1, ?2). The glycemic variability metrics were the coefficient of variation (%CV) and standard deviation. Time of glucose readings in the target range (TIR) defined the quality of glycemic control. The Poincaré plot indices and ? exponents were higher (p < 0.05) in type 1 than in the type 2 diabetes; SD1 (mmol/l): 1.64 ± 0.39 vs. 0.94 ± 0.35, SD2 (mmol/l): 4.06 ± 0.99 vs. 2.12 ± 1.04, AFE (mmol2/l2): 21.71 ± 9.82 vs. 7.25 ± 5.92, and ?1: 1.94 ± 0.12 vs. 1.75 ± 0.12, ?2: 1.38 ± 0.11 vs. 1.30 ± 0.15. The MSE index decreased consistently from the non-diabetic to the type 1 diabetic group (5.31 ± 1.10 vs. 3.29 ± 0.83, p < 0.001); higher indices correlated with lower %CV values (r = -0.313, p < 0.001). In a subgroup of type 1 diabetes patients, insulin pump therapy significantly decreased SD1 (-0.85 mmol/l), SD2 (-1.90 mmol/l), and AFE (-16.59 mmol2/l2), concomitantly with %CV (-15.60). The MSE index declined from 3.09 ± 0.94 to 1.93 ± 0.40 (p = 0.001), whereas the exponents ?1 and ?2 did not. On multivariate regression analyses, SD1, SD2, SFE, and AFE emerged as dominant predictors of TIR (? = -0.78, -1.00, -0.29, and -0.58) but %CV as a minor one, though ?1 and MSE failed. In the regression models, including SFE, AFE, and ?2 (? = -0.32), %CV was not a significant predictor. Poincaré plot descriptors provide additional information to conventional variability metrics and may complement assessment of glycemia, but complexity measures produce mixed results.
| S-EPMC6136234 | biostudies-literature
Project description:We have developed a rapid endospore viability assay (EVA) in which endospore germination serves as an indicator for viability and applied it to (i) monitor UV inactivation of endospores as a function of dose and (ii) determine the proportion of viable endospores in arctic ice cores (Greenland Ice Sheet Project 2 [GISP2] cores; 94 m). EVA is based on the detection of dipicolinic acid (DPA), which is released from endospores during germination. DPA concentrations were determined using the terbium ion (Tb3+)-DPA luminescence assay, and germination was induced by L-alanine addition. The concentrations of germinable endospores were determined by comparison to a standard curve. Parallel EVA and phase-contrast microscopy experiments to determine the percentage of germinable spores yielded comparable results (54.3% +/- 3.8% and 48.9% +/- 4.5%, respectively), while only 27.8% +/- 7.6% of spores produced CFU. EVA was applied to monitor the inactivation of spore suspensions as a function of UV dose, yielding reproducible correlations between EVA and CFU inactivation data. The 90% inactivation doses were 2,773 J/m2, 3,947 J/m2, and 1,322 J/m2 for EVA, phase-contrast microscopy, and CFU reduction, respectively. Finally, EVA was applied to quantify germinable and total endospore concentrations in two GISP2 ice cores. The first ice core contained 295 +/- 19 germinable spores/ml and 369 +/- 36 total spores/ml (i.e., the percentage of germinable endospores was 79.9% +/- 9.3%), and the second core contained 131 +/- 4 germinable spores/ml and 162 +/- 17 total spores/ml (i.e., the percentage of germinable endospores was 80.9% +/- 8.8%), whereas only 2 CFU/ml were detected by culturing.
| S-EPMC1610317 | biostudies-literature