In Situ Methane Determination in Petroleum at High Temperatures and High Pressures with Multivariate Optical Computing.

Multivariate optical computing (MOC) is a compressed sensing technique enabling the measurement of analytes in a complex interfering mixture under harsh conditions. In this work, we describe the design, modeling, fabrication, and validation of a sensor for the measurement of dissolved methane in petroleum crude oil at high and variable combinations of pressure (up to 82.727 MPa) and temperature (up to 121.1 °C). Both laboratory and field validation results are presented, with five separate MOC sensors yielding a RMS error of 0.0089 g/cm3 methane in high pressure/high temperature laboratory and field samples compared to 0.0086 g/cm3 methane for a room temperature laboratory Fourier transform infrared (FTIR) spectrometer using partial least-squares (PLS) regression models.

A New Multivariate Optical Computing Microelement and Miniature Sensor for Spectroscopic Chemical Sensing in Harsh Environments: Design, Fabrication, and Testing.

Multivariate optical computing (MOC) is a compressed sensing technique with the ability to provide accurate spectroscopic compositional analysis in a variety of different applications to multiple industries. Indeed, recent developments have demonstrated the successful deployment of MOC sensors in downhole/well-logging environments to interrogate the composition of hydrocarbon and other chemical constituents in oil and gas reservoirs. However, new challenges have necessitated sensors that operate at high temperatures and pressures (up to 230 °C and 138 MPa) as well as even smaller areas that require the miniaturization of their physical footprint. To this end, this paper details the design, fabrication, and testing of a novel miniature-sized MOC sensor suited for harsh environments. A micrometer-sized optical element provides the active spectroscopic analysis. The resulting MOC sensor is no larger than two standard AAA batteries yet is capable of operating in high temperature and pressure conditions while providing accurate spectroscopic compositional analysis comparable to a laboratory Fourier transform infrared spectrometer.

Detection limits for blood on four fabric types using infrared diffuse reflection spectroscopy in mid- and near-infrared spectral windows.

Detection limits (DL) for blood on four fabric types were estimated for calibrations derived using partial least squares regression applied to infrared (IR) diffuse reflection spectra. Samples were prepared by dip-coating acrylic, cotton, nylon, and polyester fabrics from solutions of diluted rat blood. While DLs often appear in terms of dilution factor in the forensic community, mass percentage, coverage (mass per unit area), or film thickness are often more relevant when comparing experimental methods. These alternate DL units are related to one another and presented here. The best IR diffuse reflection DLs for blood on acrylic and cotton fabrics were in the mid-IR spectral window corresponding to the protein Amide I/II absorption bands. These DLs were dilution by a factor of 2300 (0.019% w/w blood solids) for acrylic and a factor of 610 (0.055% w/w blood solids) for cotton. The best DL for blood on polyester was found in the mid-IR spectral window corresponding to the protein Amide A absorption band at dilution by a factor of 900 (0.034% w/w blood solids). Because of the similarity between the IR spectra of blood solids and nylon fabrics, no satisfactory IR DLs were determined for the calibration of blood on nylon. We compare our values to DLs reported for blood detection using the standard luminol method. The most commonly reported luminol DLs are of the order of 1000-fold dilution, which we estimate are a factor of 2-7 lower than our reported IR DLs on a coverage basis.

Minimally invasive identification of degraded polyester-urethane magnetic tape using attenuated total reflection Fourier transform infrared spectroscopy and multivariate statistics.

Audio recordings are a significant component of the world's modern cultural history and are retained for future generations in libraries, archives, and museums. The vast majority of tapes contain polyester-urethane as the magnetic particle binder, the degradation of which threatens the playability and integrity of these often unique recordings. Magnetic tapes with stored historical data are degrading and need to be identified prior to digitization and/or preservation. We demonstrate the successful differentiation of playable and nonplayable quarter-inch audio tapes, allowing the minimally invasive triage of tape collections. Without such a method, recordings are put at risk during playback, which is the current method for identifying degraded tapes. A total of 133 quarter-inch audio tapes were analyzed by attenuated total reflectance Fourier transform-infrared spectroscopy (ATR FT-IR). Classification of IR spectra in regards to tape playability was accomplished using principal component analysis (PCA) followed by quadratic discriminant analysis (QDA) and K-means cluster analysis. The first principal component suggests intensities at the following wavenumbers to be representative of nonplayable tapes: 1730 cm(-1), 1700 cm(-1), 1255 cm(-1), and 1140 cm(-1). QDA and cluster analysis both successfully identified 93.78% of nonplayable tapes in the calibration set and 92.31% of nonplayable tapes in the test set. This application of IR spectra assessed with multivariate statistical analysis offers a path to greatly improve efficiency of audio tape preservation. This rapid, minimally invasive technique shows potential to replace the manual playback test, a potentially destructive technique, ultimately allowing the safe preservation of culturally valuable content.

Focus-independent particle size measurement from streak images: a comparison of multivariate methods.

Our laboratories have recently developed a flow-through imaging photometer to characterize and classify fluorescent particles between 3 and 47 µm in size. The wide aperture of the objective lens (0.7 NA) required for measuring spectral fluorescence of single particles restricts the depth of field, such that a large sample volume results in many particles that are out of focus. Here, we describe numerical methods for determining the size of these objects, regardless of their distance from the focal plane, using image processing and multivariate calibration. An intensity profile is extracted from the images and is used as the input for a variety of calibration methods, including partial least squares, neural networks, and support vector machines. The capabilities of these methods are examined to establish the best method for particle sizing that is independent of focus. We found that support vector machines provided the best results, with size estimation error of ±3.1 µm.

Filter Fluorometer Calibration Without the Fluorometer.

We recently described a lightweight, low-power, waterproof filter fluorometer using a 180° backscatter geometry for chlorophyll-a (chl-a) detection. Before it was constructed it was modeled to ensure it would have satisfactory performance. This manuscript repeats the modeling process that allows the calibration slope and detection limit for a fluorescent analyte in water to be estimated from system component performance and conventional spectrofluorometry alone. These values are validated by comparison to the experimental result of calibration from the completed instrument. Our model yields a calibration slope of 8.22 mV-L/µg for dissolved chl-a, consistent with the experimentally measured slope of 8.21 mV-L/µg. The detection limit modeled from this slope and an estimate of the baseline noise of the instrument was 0.15 µg/L chl-a, while the measured detection limit using real blank samples was 0.18 µg/L, in 0.1 s differential measurements.

Chlorophyll Fluorometer for Intelligent Water Sampling by a Small Uncrewed Aircraft System (sUAS).

We describe a waterproof, lightweight (1.3 kg), low-power (∼1.1 W average power) fluorometer operating on 5 V direct current deployed on a small uncrewed aircraft system (sUAS) to measure chlorophyll and used for triggering environmental water sampling by the sUAS. The fluorometer uses a 450 nm laser modulated at 10 Hz for excitation and a standard photodiode and transimpedance amplifier for the detection of fluorescence. Additional detectors are available for measuring laser intensity and light scattering. Control of the fluorometer and communication between the fluorometer and the Raspberry Pi 4B computer controlling the sampler were provided by an Arduino microcontroller using the robot operating system (ROS). Calibrations were based on standards of dissolved chlorophyll extracted from Chlorella powder (a widely available dietary supplement). The detection limit for chlorophyll from these calibrations was found to be 0.2 µg per liter of water for a single 0.1 s differential measurement. The detection limit decreases with the square root of the integration time as expected. Detection limits increase by a factor of two to three when mounted in the sUAS due to electrical noise; sUAS acoustic noise and vibration do not appear to contribute significantly.

Fluorometer Control and Readout Using an Arduino Nano 33 BLE Sense Board.

We describe the control and interfacing of a fluorometer designed for aerial drone-based measurements of chlorophyll-a using an Arduino Nano 33 BLE Sense board. This 64 MHz controller board provided suitable resolution and speed for analog-to-digital (ADC) conversion, processed data, handled communications via the Robot Operating System (ROS) and included a variety of built-in sensors that were used to monitor the fluorometer for vibration, acoustic noise, water leaks and overheating. The fluorometer was integrated into a small Uncrewed Aircraft System (sUAS) for automated water sampling through a Raspberry Pi master computer using the ROS. The average power consumption was 1.1 W. A signal standard deviation of 334 µV was achieved for the fluorescence blank measurement, mainly determined by the input noise equivalent power of the transimpedance amplifier. An ADC precision of 130 µV for 10 Hz chopped measurements was achieved for signals in the input range 0-600 mV.

Diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) detection limits for blood on fabric: Orientation and coating uniformity effects.

Diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) was used to analyze four types of forensically relevant fabrics coated with varying dilutions of blood. The blood was applied in two manners, dip coating with a smooth and uniform layer and drip coating with droplets from pipettes. Spectra of neat and dip coated fabrics were acquired using controlled orientations, and these were compared to spectra collected on samples with random orientations. The improved reproducibility seen in visual inspection of the spectra is confirmed by principal component and linear discriminant projections of the spectra, as well as by statistical hypothesis testing. Principal component regression (PCR), using the regions of the IR spectra associated with the amide A/B, I, II, and III vibrational bands (3500-2800, 1650, 1540, and 1350 cm-1), was employed on the more uniform dip coated spectra to estimate limits of detection for blood on two of the four fabrics - acrylic and nylon. These results demonstrate that detection limits for blood on fabrics can be decreased significantly by controlling for the orientation and face of the fabric samples while collecting spectra. Limits of detection for acrylic and nylon were found to be 196 × and 227 × diluted blood, respectively.

Multimode imaging in the thermal infrared for chemical contrast enhancement. Part 1: Methodology.

We combine a thermal light source with a conventional thermal infrared camera, alternating current (AC) detection methods, and chemical filtering of the infrared (IR) light to generate several imaging modalities in a simple manner. We demonstrate that digital lock-in amplifier techniques can increase the chemical contrast in an active thermal infrared image using both reflectance and thermal re-emission. We show this method is useful for visualizing thin coatings on fabrics that are invisible to the eye. We also take advantage of a "like-detects-like" chemical filtering approach to chemical selectivity for the purpose of chemical identification using a broadband thermal detector.

Multimode imaging in the thermal infrared for chemical contrast enhancement. Part 3: Visualizing blood on fabrics.

Infrared thermal imaging using lock-in and molecular factor computing methods for the detection of blood on a dark, acrylic fabric is shown. Contrast differences between the clean fabric and the fabric stained with blood diluted as low as 1:100 are reported. We have also demonstrated that this method can be used to discriminate between a bloodstain and four common interfering agents (bleach, rust, cherry soda, and coffee) to other blood detection methods. These results indicate that this system could be useful for crime scene investigations by focusing nondestructive attention on areas more likely to be suitable for further analysis.

Multimode imaging in the thermal infrared for chemical contrast enhancement. Part 2: Simulation driven design.

We present a simulation-driven process to design an infrared camera system that is tuned to specific analytes of interest based on "molecular factor computing". There are many factors involved in optimizing discrimination using optical filtering aids, including, but not limited to, the detector response, optical throughput of the system, optical properties of the samples, and optical properties of the materials for sensitizing films/filters. There are nearly infinite possible setups for the system, which means it is neither cost nor time efficient to physically test each one. In lieu of this, we developed routines in MATLAB (The Mathworks, Natick, MA) that simulate the camera output, per pixel, given specific conditions. Beginning with measured spectra of calibration samples or standards and using an objective function or figure of merit (FOM) to measure simulated performance, these routines evaluate large numbers of combinations of chemical films as filters for discrimination based on linear discriminant analysis (LDA). In this study, the FOM was the Fisher ratio between a neat fabric and one stained with either a polymer film or blood.

Direct Identification of Mixed-Metal Centers in Metal-Organic Frameworks: Cu3(BTC)2 Transmetalated with Rh2+ Ions.

Raman spectroscopy was used to establish direct evidence of heterometallic metal centers in a metal-organic framework (MOF). The Cu3(BTC)2 MOF HKUST-1 (BTC3- = benzenetricarboxylate) was transmetalated by heating it in a solution of RhCl3 to substitute Rh2+ ions for Cu2+ ions in the dinuclear paddlewheel nodes of the framework. In addition to the Cu-Cu and Rh-Rh stretching modes, Raman spectra of (CuxRh1-x)3(BTC)2 show the Cu-Rh stretching mode, indicating that mixed-metal Cu-Rh nodes are formed after transmetalation. Density functional theory studies confirmed the assignment of a Raman peak at 285 cm-1 to the Cu-Rh stretching vibration. Electron paramagnetic resonance spectroscopy experiments further supported the conclusion that Rh2+ ions are substituted into the paddlewheel nodes of Cu3(BTC)2 to form an isostructural heterometallic MOF, and electron microscopy studies showed that Rh and Cu are homogeneously distributed in (CuxRh1-x)3(BTC)2 on the nanoscale.

Single-Cell and Bulk Fluorescence Excitation Signatures of Seven Phytoplankton Species During Nitrogen Depletion and Resupply.

Phytoplankton play a vital role as primary producers in aquatic ecosystems. One common approach to classifying phytoplankton is fluorescence excitation spectroscopy, which leverages the variation in types and concentrations of pigments among different phytoplankton taxonomic groups. Here, we used a fluorescence imaging photometer to measure excitation ratios ("signatures") of single cells and bulk cultures of seven differently pigmented phytoplankton species as they progressed from nitrogen N-replete to N-depleted conditions. Our objective was to determine whether N depletion alters the fluorescence excitation signature of each species and, if so, how quickly they recover when N (as nitrate) was resupplied, because these factors affect our ability to classify the species correctly. Of the seven species studied, only Proteomonas sulcata, a marine cryptophyte, showed measurable changes in single-cell fluorescence excitation ratios and bulk fluorescence excitation spectra. These changes were likely due to decreases in the cellular concentration of phycoerythrin, a N-rich pigment, as N became scarce. Within 3 h of resupply of N, fluorescence signatures began returning to pre-depletion values and were indistinguishable from N-replete cells by 80 h after resupply. These data suggest that our classification approach is robust for non-PE containing phytoplankton. PE-containing phytoplankton might exhibit systematic changes in their signatures depending on their level of N depletion, but this could be detected and the phytoplankton re-classified following a few hours of incubation in N replete conditions.

Sampling and quantitative analysis of clean B. subtilis spores at sub-monolayer coverage by reflectance fourier transform infrared microscopy using gold-coated filter substrates.

A study was conducted to determine the concentration dependency of the mid-infrared (MIR) absorbance of bacterial spores. A range of concentrations of Bacillus subtilis endospores filtered across gold-coated filter membranes were analyzed by Fourier transform infrared (FT-IR) reflectance microscopy. Calibration curves were derived from the peak absorbances associated with Amide A, Amide I, and Amide II vibrational frequencies by automatic baseline fitting to remove most of the scattering contribution. Linear relationships (R2 >or= 0.99) were observed between the concentrations of spores and the baseline-corrected peak absorbance for each frequency studied. Detection limits for our sampled area of 100 x100 microm2 were determined to be 79, 39, and 184 spores (or 7.92 x 10(5), 3.92 x 10(5), and 1.84 x 10(6) spores/cm2) for the Amide A, Amide I, and Amide II peaks, respectively. Absorbance increased linearly above the scattering baseline with particle surface concentration up to 0.9 monolayer (ML) coverage, with the monolayer density calculated to be approximately 1.17 x 10(8) spores/cm2. Scattering as a function of surface concentration, as estimated from extinction values at wavelengths exhibiting low absorbance, becomes nonlinear at a much lower surface concentration. The apparent scattering cross-section per spore decreased monotonically as concentrations increased toward 1.2 ML, while the absolute scattering decreased between 0.9 ML and 1.2 ML coverage. Calculations suggest that transverse spatial coherence effects are the origin of this nonlinearity, while the onset of nonlinearity in the baseline-corrected absorption is probably due to multiple scattering effects, which appear at a high surface concentration. Absorption cross-sections at peaks of the three bands were measured to be (2.15 +/- 0.05) x 10(-9), (1.48 +/- 0.03) x 10(-9), and (0.805 +/- 0.023) x 10(-9) cm2, respectively. These values are smaller by a factor of 2-4 than expected from the literature. The origin of the reduced cross-section is hypothesized to be an electric field effect related to the surface selection rule.

Fluorescence Excitation Spectroscopy for Phytoplankton Species Classification Using an All-Pairs Method: Characterization of a System with Unexpectedly Low Rank.

An all-pairs method is used to analyze phytoplankton fluorescence excitation spectra. An initial set of nine phytoplankton species is analyzed in pairwise fashion to select two optical filter sets, and then the two filter sets are used to explore variations among a total of 31 species in a single-cell fluorescence imaging photometer. Results are presented in terms of pair analyses; we report that 411 of the 465 possible pairings of the larger group of 31 species can be distinguished using the initial nine-species-based selection of optical filters. A bootstrap analysis based on the larger data set shows that the distribution of possible pair separation results based on a randomly selected nine-species initial calibration set is strongly peaked in the 410-415 pair separation range, consistent with our experimental result. Further, the result for filter selection using all 31 species is also 411 pair separations; The set of phytoplankton fluorescence excitation spectra is intuitively high in rank due to the number and variety of pigments that contribute to the spectrum. However, the results in this report are consistent with an effective rank as determined by a variety of heuristic and statistical methods in the range of 2-3. These results are reviewed in consideration of how consistent the filter selections are from model to model for the data presented here. We discuss the common observation that rank is generally found to be relatively low even in many seemingly complex circumstances, so that it may be productive to assume a low rank from the beginning. If a low-rank hypothesis is valid, then relatively few samples are needed to explore an experimental space. Under very restricted circumstances for uniformly distributed samples, the minimum number for an initial analysis might be as low as 8-11 random samples for 1-3 factors.

Reinforcing mechanisms of single-walled carbon nanotube-reinforced polymer composites.

The reinforcing mechanisms of single-walled carbon nanotube-reinforced epoxy composites were studied by micromechanics models. The modeling results obtained from both Halpin-Tsai and Mori-Tanaka models are in good agreement with the experimental results. It has been found that these two models are also applicable to other single-walled carbon nanotube-reinforced, amorphous-polymer composites, given the existence of efficient load transfer. The reinforcing mechanisms that work in polymer-carbon nanotube composites were studied. The reasons responsible for the low mechanical property enhancement of single-walled carbon nanotube in polymer composites were discussed in conjunction with the effective fiber length concept, interface between nanotube bundles and the matrix, properties of the reinforcements and matrix, bundle effects, bundle curvature, and alignment.

Spectral resolution in multivariate optical computing.

An examination of spectral resolution effects on multivariate optical computing (MOC) and the design of multivariate optical elements (MOEs) is presented. A solution of napthalene and pyrene in CCl(4) is used as a test mixture, with spectra recorded in the nominal 2-2.5 microm spectral region at resolutions varying from 1 to 128 cm(-1). Spectra were treated in absorption mode and in transmission mode at sample pathlengths of 1, 0.5, and 0.2 cm. Principal components regression is used to provide comparison to MOE models. Conventional model prediction errors using all methods are presented as well as results of applying low-resolution models to high-resolution validation sets. This latter calculation is aimed at understanding the limits of calibration transfer when a model is based on spectra acquired with marginal spectral resolution. A theory is developed describing calibration transfer in the case of linear spectroscopy, which is shown to be consistent with the results observed in absorption mode and to represent the case of the short-pathlength limit in transmission mode. A definition of the necessary spectral resolution as a function of apodization function is given for linear spectroscopy, and a brief discussion of how non-linearity affects the results is provided.

Detection Limits for Blood on Fabrics Using Attenuated Total Reflection Fourier Transform Infrared (ATR FT-IR) Spectroscopy and Derivative Processing.

Attenuated total reflection Fourier transform infrared spectroscopy (ATR FT-IR) was used to detect blood stains based on signature protein absorption in the mid-IR region, where intensity changes in the spectrum can be related to blood concentration. Partial least squares regression (PLSR) was applied for multivariate calibrations of IR spectra of blood dilutions on four types of fabric (acrylic, nylon, polyester, and cotton). Gap derivatives (GDs) were applied as a preprocessing technique to optimize the performance of calibration models. We report a much improved IR detection limit (DL) for blood on cotton (2700× in dilution factor units) and the first IR DL reported for blood on nylon (250×). Due to sample heterogeneity caused by fabric hydrophobicity, acrylic fabric produced variable ATR FT-IR spectra that caused poor DLs in concentration units compared to previous work. Polyester showed a similar problem at low blood concentrations that lead to a relatively poor DL as well. However, the increased surface sensitivity and decreased penetration depth of ATR FT-IR make it an excellent choice for detection of small quantities of blood on the front surface of all fabrics tested (0.0010 µg for cotton, 0.0077 µg for nylon, 0.011 µg for acrylic, and 0.0066 µg for polyester).

Attenuated Total Reflection (ATR) Sampling in Infrared Spectroscopy of Heterogeneous Materials Requires Reproducible Pressure Control.

Attenuated total reflection Fourier transform infrared (ATR FT-IR) spectroscopy, in which the sample is pressed against an internal reflection element, is a popular technique for rapid IR spectral collection. However, depending on the accessory design, the pressure applied to the sample is not always well controlled. While collecting data from fabrics with heterogeneous coatings, we have observed systematic pressure-dependent changes in spectra that can be eliminated by more reproducible pressure control. We also described a pressure sensor adapted to work with an ATR tower to enable more precise control of pressure during ATR sampling.