Streamlining LC-MS Characterization of Pharmaceutical Polymers by Fourier-Transform-Based Deconvolution and Macromolecular Mass Defect Analysis.

Polymer conjugation has risen in importance over the past three decades as a means of increasing the in vivo half-life of biotherapeutics, with benefits including better stability, greater drug efficacy, and lower toxicity. However, the intrinsic variability of polymer synthesis results in products with broad distributions in chain length and branching structure, complicating quality control for successful functionalization and downstream conjugation. Frequently, a combination of several analytical techniques is required for comprehensive characterization. While liquid chromatography-mass spectrometry (LC-MS) is a powerful platform that can provide detailed molecular features of polymers, the mass spectra are inherently challenging to interpret due to high mass polydispersity and overlapping charge distributions. Here, by leveraging Fourier transform-based deconvolution and macromolecular mass defect analysis, we demonstrate a new way to streamline pharmaceutical polymer analysis, shedding light on polymer size, composition, branching, and end-group functionalization with the capability for reaction monitoring.

Utilising Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) to track the oxidation of lignin by an alkaliphilic laccase.

Lignin is a complex heteroaromatic polymer which is one of the most abundant and diverse biopolymers on the planet. It comprises approximately one third of all woody plant matter, making it an attractive candidate as an alternative, renewable feedstock to petrochemicals to produce fine chemicals. However, the inherent complexity of lignin makes it difficult to analyse and characterise using common analytical techniques, proving a hindrance to the utilisation of lignin as a green chemical feedstock. Herein we outline the tracking of lignin degradation by an alkaliphilic laccase in a semi-quantitative manner using a combined chemical analysis approach using Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) to characterise shifts in chemical diversity and relative abundance of ions, and NMR to highlight changes in the structure of lignin. Specifically, an alkaliphilic laccase was used to degrade an industrially relevant lignin, with compounds such as syringaresinol being almost wholly removed (95%) after 24 hours of treatment. Structural analyses reinforced these findings, indicating a >50% loss of NMR signal relating to ß-ß linkages, of which syringaresinol is representative. Ultimately, this work underlines a combined analytical approach that can be used to gain a broader semi-quantitative understanding of the enzymatic activity of laccases within a complex, non-model mixture.

Quantitative and qualitative impacts of nitric acid digestion on microplastic identification via FTIR and Raman spectroscopy, implications for environmental samples.

Quantification and characterization of microplastics, synthetic polymers less than 5 mm in diameter, requires extraction methods that can reduce non-plastic debris without loss or alteration of the polymers. Nitric acid has been used to extract plastic particles from zooplankton and other biota because it completely digests tissue and exoskeletons, thus reducing interferences. While the impact of acid digestion protocols on several polymers has been demonstrated, advice for quantifying microplastic and interpreting their spectra following nitric acid digestion is lacking. Fourier transform infrared (FTIR) and/or Raman spectroscopy was performed on plastics from > 50 common consumer products (including a variety of textiles) pre- and post-nitric acid treatment. The percent match and assigned polymer were tabulated to compare the accuracy of spectral identification before and after nitric acid digestion via two open spectral analysis software. Nylon-66, polyoxymethylene, polyurethane, polyisoprene, nitrile rubber, and polymethyl methacrylate had ≥ 90% mass loss in nitric acid. Other less-impacted polymers changed color, morphology, and/or size following digestion. Thus, using nitric acid digestion for microplastic extraction can impact our understanding of the particle sizes and morphologies ingested in situ. Spectral analysis results were compiled to understand how often (1) the best-hit matches were correct (30-60% of spectra), (2) the best-hit matches exceeding the (arbitrary) threshold of 65% match were correct (53-78% of spectra), and (3) the best-hit matches for anthropogenic polymers were incorrectly identified as natural polymers (12-15% of spectra). Based on these results, advice is provided on how nitric acid digestion can impact microplastics as well as spectral interpretation.

FT-ICR-MS combined with fluorescent spectroscopy reveals the driving mechanism of the spatial variation in molecular composition of DOM in 22 plateau lakes.

Dissolved organic matter (DOM) is the largest carbon pool and directly affects the biogeochemistry in lakes. In the current study, fourier transform ion cyclotron mass spectrometry (FT-ICR-MS) combined with fluorescent spectroscopy was used to assess the molecular composition and driving mechanism of DOM in 22 plateau lakes in Mongolia Plateau Lakes Region (MLR), Qinghai Plateau Lakes Region (QLR) and Tibet Plateau Lakes Region (TLR) of China. The limnic dissolved organic carbon (DOC) content ranged from 3.93 to 280.8 mg L-1 and the values in MLR and TLR were significantly higher than that in QLR. The content of lignin was the highest in each lake and showed a gradually decreasing trend from MLR to TLR. Random forest model and structural equation model implied that altitude played an important role in lignin degradation while the contents of total nitrogen (TN) and chlorophyll a (Chl-a) have a great influence on the increase of DOM Shannon index. Our results also suggested that the inspissation of DOC and the promoted endogenous DOM production caused by the inspissation of nutrient resulted in a positive relationship between limnic DOC content and limnic factors such as salinity, alkalinity and nutrient concentration. From MLR to QLR and TLR, the molecular weight and the number of double bonds gradually decreased but the humification index (HIX) also decreased. In addition, from the MLR to the TLR, the proportion of lignin gradually decreased, while the proportion of lipid gradually increased. Both above results suggested that photodegradation was dominated in lakes of TLR, while microbial degradation was dominated in lakes of MLR.

Fourier-Transform Atomic Force Microscope-Based Photothermal Infrared Spectroscopy with Broadband Source.

The mechanical detection of photothermal expansion from infrared (IR) absorption with an atomic force microscope (AFM) bypasses Abbe's diffraction limit, forming the chemical imaging technique of AFM-IR. Here, we develop a Fourier transform AFM-IR technique with peak force infrared microscopy and broadband femtosecond IR pulses. A Michelson interferometer creates a pair of IR pulses with controlled time delays to generate photothermal signals transduced by AFM to form an interferogram. A Fourier transform is performed to recover IR absorption spectra. We demonstrate the Fourier transform AFM-IR microscopy on a polymer blend and hexagonal boron nitride. An intriguing observation is the vertical asymmetry of the interferogram, which suggests the presence of multiphoton absorption processes under the tip-enhancement and femtosecond IR lasers. Our method demonstrates the feasibility of time-domain detection of the AFM-IR signal in the mid-IR regime and paves the way toward multiphoton vibrational spectroscopy at the nanoscale below the diffraction limit.

Partners in Postmortem Interval Estimation: X-ray Diffraction and Fourier Transform Spectroscopy.

The postmortem interval (PMI) is difficult to estimate in later stages of decomposition. There is therefore a need to develop reliable methodologies to estimate late PMI. This study aims to assess whether there is a correlation between changes in the mineral composition of human teeth and the estimation of PMI. X-ray diffraction (XRD) and attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy techniques were performed to address this challenge. Forty healthy human teeth obtained from odontological clinics were stored at different times (0, 10, 25, 50 years; N = 10/group). XRD and ATR-FTIR parameters related to the structure and composition of teeth were studied. Our results showed that the crystallinity index, crystal size index, mineral-to-organic matrix ratio (M/M) and carbonate/phosphate ratio (C/P) had the strongest association with PMI. For larger PMIs, there was a significant increase in crystallinity, crystal size and M/M ratio, while the C/P ratio showed a specific decrease with increasing PMI. According to our results, the parameters of crystallinity, crystal size, M/M ratio and C/P ratio can be considered highly accurate in determining a PMI of 10 years of data; crystallinity and mineral maturity can be considered useful in determining a PMI of 25 years; and crystallinity and mineral maturity can be considered highly accurate in determining a PMI of 50 years. A particular XRD index was identified as the most suitable parameter to estimate PMI: crystallinity. The joint use of XRD and ATR-FTIR analyses could be a promising alternative for dating human teeth.

MALDI(+) FT-ICR Mass Spectrometry (MS) Combined with Machine Learning toward Saliva-Based Diagnostic Screening for COVID-19.

Rapid identification of existing respiratory viruses in biological samples is of utmost importance in strategies to combat pandemics. Inputting MALDI FT-ICR MS (matrix-assisted laser desorption/ionization Fourier-transform ion cyclotron resonance mass spectrometry) data output into machine learning algorithms could hold promise in classifying positive samples for SARS-CoV-2. This study aimed to develop a fast and effective methodology to perform saliva-based screening of patients with suspected COVID-19, using the MALDI FT-ICR MS technique with a support vector machine (SVM). In the method optimization, the best sample preparation was obtained with the digestion of saliva in 10 µL of trypsin for 2 h and the MALDI analysis, which presented a satisfactory resolution for the analysis with 1 M. SVM models were created with data from the analysis of 97 samples that were designated as SARS-CoV-2 positives versus 52 negatives, confirmed by RT-PCR tests. SVM1 and SVM2 models showed the best results. The calibration group obtained 100% accuracy, and the test group 95.6% (SVM1) and 86.7% (SVM2). SVM1 selected 780 variables and has a false negative rate (FNR) of 0%, while SVM2 selected only two variables with a FNR of 3%. The proposed methodology suggests a promising tool to aid screening for COVID-19.

A comparative study on the distribution behavior of microplastics through FT-IR analysis on different land uses in agricultural soils.

Plastic materials have been variously exposed to arable land for decades through soil mulching, plastic housing, and sewage sludge composting. Their mechanical abrasion and biochemical degradation induce the proliferation of myriad microplastics that can further be broken into smaller nano-sized pieces that can be further accumulated in living organisms (including soil invertebrates, fruits, and vegetables); they can also be widely dispersed in neighboring environments. Despite the intensive use of plastics in agriculture, little is known about their origin of occurrence and environmental fate, especially with a size below 100 µm. Therefore, in this study, microplastics with a size in the range of 20-2,000 µm were investigated in soil samples obtained from three different conditions of land uses: tilled with plastic mulch, bare ground (i.e., uncultivated land), and in between the greenhouses of the farmland D located in Namyangju-si, Gyeonggi-do, Republic of Korea. They were primarily identified using Fourier transform infrared (FT-IR) spectroscopy coupled with a microscope. Prior to performing the analysis, microplastic extraction from the soil samples was validated using standardized high-density polyethylene (HDPE) microplastics of various sizes ranging from 20 to 500 µm. As a result, the number of microplastics was estimated to be (241 ± 52), (195 ± 37), and (306 ± 56) particles per kg of dry soil in tillage, bare ground, and in between greenhouses, respectively. They consist of polyethylene (PE), polypropylene (PP), and poly(ethylene terephthalate) (PET), which are the basic constituents of commonly used agricultural products. The particle size distribution depends on the type of plastic, the time elapsed since their usage, and the degree and duration of environmental exposure; the plastic particle sizes were smaller in tillage and around the greenhouses since agricultural films have been weathered for a long time, whereas those with relatively large sizes were found in the uncultivated.

Microplastic pollution in the surface water and sediments from Kallar Kahar wetland, Pakistan: occurrence, distribution, and characterization by ATR-FTIR.

This study reports the distribution of microplastics in surface water and sediments collected from Kallar Kahar wetland, Punjab, Pakistan, which is a game reserve and hosts migratory birds during winter season. Microplastics were extracted using density separation and wet oxidation method. The microplastics identification was done under a stereo-microscope, and their polymer compositions were characterized using an attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy. The average abundance of microplastics in water and sediment samples was 88 ± 14.5 items/L and 5720 ± 2580 items/kg, respectively. The dominant shape groups of microplastics in water were fiber (58.7%), irregular fragments (32.4%), and beads (8.7%) with dominant colors as transparent > black > yellow ≈ white > red > green > pink > blue. Similar distribution in sediments was found, i.e., fiber (61.2%), irregular fragments (28.4%), and beads (10.3%) with dominant colors as transparent > pink > white > red ≈ black > blue > brown > green ≈ yellow. The ATR-FTIR spectra of visible microplastics were identified to be polypropylene (PP), high density polyethylene (HDPE), ethylene vinyl acetate (EVA), low density polyethylene (LDPE), nitrile, polymethyl methacrylate (PMMA), latex, and polyethylene terephthalate (PETE). In the study area, recreational activities, improper waste disposal, and runoff from catchment areas are the main reasons for the contamination of microplastics in the Lake. The pollution load can be minimized by taking measures such as creating awareness, promotion of ecotourism, and reducing plastic use.

Deep Learning for Reconstructing Low-Quality FTIR and Raman Spectra─A Case Study in Microplastic Analyses.

Herein we report on a deep-learning method for the removal of instrumental noise and unwanted spectral artifacts in Fourier transform infrared (FTIR) or Raman spectra, especially in automated applications in which a large number of spectra have to be acquired within limited time. Automated batch workflows allowing only a few seconds per measurement, without the possibility of manually optimizing measurement parameters, often result in challenging and heterogeneous datasets. A prominent example of this problem is the automated spectroscopic measurement of particles in environmental samples regarding their content of microplastic (MP) particles. Effective spectral identification is hampered by low signal-to-noise ratios and baseline artifacts as, again, spectral post-processing and analysis must be performed in automated measurements, without adjusting specific parameters for each spectrum. We demonstrate the application of a simple autoencoding neural net for reconstruction of complex spectral distortions, such as high levels of noise, baseline bending, interferences, or distorted bands. Once trained on appropriate data, the network is able to remove all unwanted artifacts in a single pass without the need for tuning spectra-specific parameters and with high computational efficiency. Thus, it offers great potential for monitoring applications with a large number of spectra and limited analysis time with availability of representative data from already completed experiments.

µATR-FTIR Spectral Libraries of Plastic Particles (FLOPP and FLOPP-e) for the Analysis of Microplastics.

Raman spectral libraries specific to microplastics demonstrated improved spectral matching results when weathered plastics and a variety of particle colors and morphologies were included. Here, we explore if this is true for Fourier transform infrared (FTIR) spectroscopy as well. We present two novel databases specific to microplastics using attenuated total reflection (µATR-FTIR): (1) an FTIR library of plastic particles (FLOPP), containing 186 spectra from common plastic items, across 14 polymer types and (2) an FTIR library of plastic particles sourced from the environment (FLOPP-e), containing 195 spectra across 15 polymer types. Both libraries include particles from a variety of sources, morphologies, and colors. We demonstrate the applicability of these libraries for microplastics research by comparing spectral match results from two microplastic datasets. For this, we use different combinations of libraries including: commercially available reference libraries, an open-access polymer library, and FLOPP and FLOPP-e. Among tests, the greatest mean HQI result was achieved when the greatest number of libraries was included. This work demonstrates that spectral libraries specific to plastic particles found in the environment improve the accuracy of spectral matching and are best used in combination with commercial libraries containing chemical components that are commonly found within plastics and other anthropogenic particles. Multivariate principal component analyses of FLOPP and FLOPP-e spectra confirmed differences among polymer types and higher variation in principal component scores among weathered particles, but no patterns were observed among particle colors or morphologies. These results demonstrate that ATR-FTIR analyses are sensitive to weathering of plastics but not to particle color and morphology.

Adaptive incremental method for strain estimation in phase-sensitive optical coherence elastography.

We proposed an adaptive incremental method for the cumulative strain estimation in phase-sensitive optical coherence elastography. The method firstly counts the amount of phase noise points by mapping a binary noise map. After the noise threshold value is preset, the interframe interval is adaptively adjusted in terms of the phase noise ratio. Finally, the efficient estimation of cumulative strain is implemented by reducing the cumulative number. Since the level of phase noise is related to the different strain rates in accordance with the speckle decorrelation, the proposed method can estimate the large strains with high computation efficiency as well as signal-to-noise ratio (SNR) enhancement in nonlinear change of sample deformations. Real experiments of visualizing polymerization shrinkage with nonlinear change of deformations were performed to prove the superiority of adaptive incremental method in estimating the large strains. The proposed method expands the practicability of the incremental method in more complex scenes.

An Untargeted Thermogravimetric Analysis-Fourier Transform Infrared-Gas Chromatography-Mass Spectrometry Approach for Plastic Polymer Identification.

Reliable chemical identification of specific polymers in environmental samples represents a major challenge in plastic research, especially with the wide range of commercial polymers available, along with variable additive mixtures. Thermogravimetric analysis-Fourier transform infrared-gas chromatography-mass spectrometry (TGA-FTIR-GC-MS) offers a unique characterization platform that provides both physical and chemical properties of the analyzed polymers. This study presents a library of 11 polymers generated using virgin plastics and post-consumer products. TGA inflection points and mass of remaining residues following pyrolysis, in some cases, proved to be indicative of the polymer type. FTIR analysis of the evolved gas was able to differentiate between all but polypropylene (PP) and polyethylene (PE). Finally, GC-MS was able to differentiate between the unique chemical fingerprints of all but one polymer in the library. This library was then used to characterize real environmental samples of mesoplastics collected from beaches in the U.K. and South Africa. Unambiguous identification of the polymer types was achieved, with PE being the most frequently detected polymer and with South African samples indicating variations that potentially resulted from aging and weathering.

Differences in skeletal growth patterns: an exploratory approach using elliptic Fourier analysis.

OBJECTIVE: Apply elliptic Fourier analysis to find shape differences among the hypodivergent, normodivergent, and hyperdivergent growth patterns in skeletal classes I, II and, III in mandibular and maxillary curves and evaluate the discriminatory capacity of these differences. MATERIALS AND METHODS: A total of 626 adult patients were included: 354 Brazilian patients (52 with tomographic information and 302 with radiographic information) and 272 Colombian patients with radiographic information. Lateral views were selected. The maxillary and mandibular curves were digitized. Elliptic Fourier analysis was employed considering with 20 harmonics as well as filtering size, rotation, and translation properties. One-way non-parametric MANOVA was employed to determine differences. A confusion matrix tool was employed to analyze the discriminatory capacity of the model. RESULTS: Significant shape differences in the mandibular and maxillary contours were found among the hypodivergent, normodivergent, and hyperdivergent growth patterns in classes I, II, and III (p < 0.05). The accuracies obtained from the confusion matrix were respectively 74.1, 79.5, and 90.1% in classes I, II, and III in the mandibular curves and respectively 71.9, 73.9, and 75% in classes I, II, and III in the maxillary curves. CONCLUSIONS: Elliptic Fourier analysis can be used to find shape differences with an acceptable discriminatory capacity, especially in the mandible contour. Maxillary and mandibular bone curves each significantly defined facial biotypes regardless of the size and position properties. CLINICAL RELEVANCE: This exploration offers a way to quantify mandibular morphology for the construction of an economic mandibular prediction system applicable to the Latin American population.

Signal Deconvolution and Generative Topographic Mapping Regression for Solid-State NMR of Multi-Component Materials.

Solid-state nuclear magnetic resonance (ssNMR) spectroscopy provides information on native structures and the dynamics for predicting and designing the physical properties of multi-component solid materials. However, such an analysis is difficult because of the broad and overlapping spectra of these materials. Therefore, signal deconvolution and prediction are great challenges for their ssNMR analysis. We examined signal deconvolution methods using a short-time Fourier transform (STFT) and a non-negative tensor/matrix factorization (NTF, NMF), and methods for predicting NMR signals and physical properties using generative topographic mapping regression (GTMR). We demonstrated the applications for macromolecular samples involved in cellulose degradation, plastics, and microalgae such as Euglena gracilis. During cellulose degradation, 13C cross-polarization (CP)-magic angle spinning spectra were separated into signals of cellulose, proteins, and lipids by STFT and NTF. GTMR accurately predicted cellulose degradation for catabolic products such as acetate and CO2. Using these methods, the 1H anisotropic spectrum of poly-ε-caprolactone was separated into the signals of crystalline and amorphous solids. Forward prediction and inverse prediction of GTMR were used to compute STFT-processed NMR signals from the physical properties of polylactic acid. These signal deconvolution and prediction methods for ssNMR spectra of macromolecules can resolve the problem of overlapping spectra and support macromolecular characterization and material design.

Phase-sensitive, angle-resolved light-scattering microscopy of single cells.

We report what is to our knowledge the first use of Fourier phase microscopy (FPM) to estimate diameters of individual single-micrometer beads and to classify cells based upon changes in scatterer size distribution. FPM, a quantitative phase imaging (QPI) method, combines the planar illumination typically used in off-axis QPI (ideal for Mie theory analysis) with the common-path geometry typically used in on-axis QPI (ideal for optimizing angular scattering range). Low-spatial-frequency imaging artifacts inherent to FPM have negligible impact upon these angular-domain applications. The system is simple to align and stable, and requires no external reference beam. Angular scattering patterns obtained from single 1 µm polystyrene beads in glycerol (Δn=0.11) display unprecedented fidelity to Mie theory, produce diameter estimates consistent with the manufacturer's specifications, and offer precision on the scale of tens of nanometers. Measurements of macrophages at different stages of antibody-dependent cellular phagocytosis demonstrate the ability to detect changes in a cell's scattering caused by the presence of phagocytosed material within.

Distinct classes of multi-subunit heterogeneity: analysis using Fourier Transform methods and native mass spectrometry.

Native electrospray mass spectrometry is a powerful method for determining the native stoichiometry of many polydisperse multi-subunit biological complexes, including multi-subunit protein complexes and lipid-bound transmembrane proteins. However, when polydispersity results from incorporation of multiple copies of two or more different subunits, it can be difficult to analyze subunit stoichiometry using conventional mass spectrometry analysis methods, especially when m/z distributions for different charge states overlap in the mass spectrum. It was recently demonstrated by Marty and co-workers (K. K. Hoi, et al., Anal. Chem., 2016, 88, 6199-6204) that Fourier Transform (FT)-based methods can determine the bulk average lipid composition of protein-lipid Nanodiscs assembled with two different lipids, but a detailed statistical description of the composition of more general polydisperse two-subunit populations is still difficult to achieve. This results from the vast number of ways in which the two types of subunit can be distributed within the analyte ensemble. Here, we present a theoretical description of three common classes of heterogeneity for mixed-subunit analytes and demonstrate how to differentiate and analyze them using mass spectrometry and FT methods. First, we first describe FT-based analysis of mass spectra corresponding to simple superpositions, convolutions, and multinomial distributions for two or more different subunit types using model data sets. We then apply these principles with real samples, including mixtures of single-lipid Nanodiscs in the same solution (superposition), mixed-lipid Nanodiscs and copolymers (convolutions), and isotope distribution for ubiquitin (multinomial distribution). This classification scheme and the FT method used to study these analyte classes should be broadly useful in mass spectrometry as well as other techniques where overlapping, periodic signals arising from analyte mixtures are common.

Feasibility of attenuated total reflection-fourier transform infrared (ATR-FTIR) chemical imaging and partial least squares regression (PLSR) to predict protein adhesion on polymeric surfaces.

Predicting the degree to which proteins adhere to a polymeric surface is an ongoing challenge in the scientific community to prevent non-specific protein adhesion and drive favourable protein - surface interactions. This work explores the potential of multivariate PLSR modelling in conjunction with Attenuated Total Reflection - Fourier Transform Infrared (ATR-FTIR) chemical imaging to investigate whether experimentally characterised surface chemistry can be used to predict surface protein adhesion. ATR-FTIR spectra were collected on dry and wetted polymeric surfaces, followed by evaluation of adhered fibrinogen on surfaces using the micro bicinchoninic (BCA) protein assay as a reference method. Partial Least Squares Regression (PLSR) models were built using IR spectra as the predictor variable. Overall the models built with 'wetted polymer' IR spectra performed better as compared to the models built using 'dry polymer' IR spectra (average coefficient of determination, R2P 0.998, 0.996 respectively), with the lowest error in prediction (4 ± 0.6 µg) for ultra-high molecular weight polyethylene (UHMPE) as a test surface. This indicates the potential of this method to predict the degree to which protein adhesion occurs on polymeric surfaces using experimentally determined surface chemistry.

Recent Advances in Piezoelectric Wafer Active Sensors for Structural Health Monitoring Applications.

In this paper, some recent piezoelectric wafer active sensors (PWAS) progress achieved in our laboratory for active materials and smart structures (LAMSS) at the University of South Carolina: http: //www.me.sc.edu/research/lamss/ group is presented. First, the characterization of the PWAS materials shows that no significant change in the microstructure after exposure to high temperature and nuclear radiation, and the PWAS transducer can be used in harsh environments for structural health monitoring (SHM) applications. Next, PWAS active sensing of various damage types in aluminum and composite structures are explored. PWAS transducers can successfully detect the simulated crack and corrosion damage in aluminum plates through the wavefield analysis, and the simulated delamination damage in composite plates through the damage imaging method. Finally, the novel use of PWAS transducers as acoustic emission (AE) sensors for in situ AE detection during fatigue crack growth is presented. The time of arrival of AE signals at multiple PWAS transducers confirms that the AE signals are originating from the crack, and that the amplitude decay due to geometric spreading is observed.

Interactive OCT-Based Tooth Scan and Reconstruction.

Digital dental reconstruction can be a more efficient and effective mechanism for artificial crown construction and period inspection. However, optical methods cannot reconstruct those portions under gums, and X-ray-based methods have high radiation to limit their applied frequency. Optical coherence tomography (OCT) can harmlessly penetrate gums using low-coherence infrared rays, and thus, this work designs an OCT-based framework for dental reconstruction using optical rectification, fast Fourier transform, volumetric boundary detection, and Poisson surface reconstruction to overcome noisy imaging. Additionally, in order to operate in a patient's mouth, the caliber of the injector is small along with its short penetration depth and effective operation range, and thus, reconstruction requires multiple scans from various directions along with proper alignment. However, flat regions, such as the mesial side of front teeth, may not have enough features for alignment. As a result, we design a scanning order for different types of teeth starting from an area of abundant features for easier alignment while using gyros to track scanned postures for better initial orientations. It is important to provide immediate feedback for each scan, and thus, we accelerate the entire signal processing, boundary detection, and point-cloud alignment using Graphics Processing Units (GPUs) while streamlining the data transfer and GPU computations. Finally, our framework can successfully reconstruct three isolated teeth and a side of one living tooth with comparable precisions against the state-of-art method. Moreover, a user study also verifies the effectiveness of our interactive feedback for efficient and fast clinic scanning.