Baseline correction using asymmetrically reweighted penalized least squares smoothing

Multimodal Molecular Imaging Reveals a Novel Membrane Component in Sporangia of the Rare Actinomycete Actinoplanes missouriensis

The bacterium Actinoplanes missouriensis belongs to the genus Actinoplanes, a prolific source of useful natural products. This microbe forms globular structures called sporangia, which contain many dormant spores. Recent studies using transmission electron microscopy have shown that the A. missouriensis sporangium membrane has an unprecedented three-layer structure, but its molecular components remain unclear. Here, we present multimodal (spontaneous Raman scattering, coherent anti-Stokes Raman scattering, second harmonic generation, sum frequency generation, and third-order sum frequency generation) label-free molecular imaging of intact A. missouriensis sporangia. Spontaneous Raman imaging assisted with multivariate curve resolution-alternating least-squares analysis revealed a novel component in the sporangium membrane that exhibits unique Raman bands at 1550 and 1615 cm-1 in addition to those characteristic of lipids. A plausible candidate for this component is an unsaturated carbonyl compound with an aliphatic moiety derived from fatty acid. Furthermore, second harmonic generation imaging revealed that a layer(s) of the sporangium membrane containing this unknown component has an ordered, noncentrosymmetric structure like fibrillar proteins and amylopectin. Our results suggest that the sporangium membrane is a new type of biological membrane, not only in terms of architecture but also in terms of components. We demonstrate that multimodal molecular imaging with Raman scattering as the core technology will provide a promising platform for interrogating the chemical components, whether known or unknown, of diverse biological structures produced by microbes.

NMR-Onion - a transparent multi-model based 1D NMR deconvolution algorithm

We introduce NMR-Onion, an open-source, computationally efficient algorithm based on Python and PyTorch, designed to facilitate the automatic deconvolution of 1D NMR spectra. NMR-Onion features two innovative time-domain models capable of handling asymmetric non-Lorentzian line shapes. Its core components for resolution-enhanced peak detection and digital filtering of user-specified key regions ensure precise peak prediction and efficient computation. The NMR-Onion framework includes three built-in statistical models, with automatic selection via the BIC criterion. Additionally, NMR-Onion assesses the repeatability of results by evaluating post-modeling uncertainty. Using the NMR-Onion algorithm helps to minimize excessive peak detection.

Kinetics of the mechanically induced ibuprofen-nicotinamide co-crystal formation by in situ X-ray diffraction

Mechanochemistry is drawing attention from the pharmaceutical industry given its potential for sustainable material synthesis and manufacture. Scaling mechanochemical processes to industrial level remains a challenge due to an incomplete understanding of their underlying mechanisms. We here show how time-resolved in situ powder X-ray diffraction data, coupled with analytical kinetic modelling, provides a powerful approach to gain mechanistic insight into mechanochemical reactions. By using the ibuprofen-nicotinamide co-crystal mechanosynthesis as a benchmark system, we investigate the behaviour of the solids involved and identify the factors that promote the reaction. As mechanochemical mechanisms become increasingly clear, it promises to become a breakthrough in the industrial preparation of advanced pharmaceuticals.

Slow wave activity disruptions and memory impairments in a mouse model of aging

The aging population suffers from memory impairments. Slow-wave activity (SWA) is composed of slow (0.5-1 Hz) and delta (1-4 Hz) oscillations, which play important roles in long-term memory and working memory function respectively. SWA disruptions might lead to memory disturbances often experienced by older adults. We conducted behavioral tests in young and older C57BL/6 J mice. SWA was monitored using wide-field imaging with voltage sensors. Cell-specific calcium imaging was used to monitor the activity of excitatory and inhibitory neurons in these mice. Older mice exhibited impairments in working memory but not memory consolidation. Voltage-sensor imaging revealed aberrant synchronization of neuronal activity in older mice. Notably, we found older mice exhibited no significant alterations in slow oscillations, whereas there was a significant increase in delta power compared to young mice. Calcium imaging revealed hypoactivity in inhibitory neurons of older mice. Combined, these results suggest that neural activity disruptions might correlate with aberrant memory performance in older mice.

The effects of TGF-β-induced activation and starvation of vitamin A and palmitic acid on human stem cell-derived hepatic stellate cells

BACKGROUND

Hepatic stellate cells (HSC) have numerous critical roles in liver function and homeostasis, while they are also known for their importance during liver injury and fibrosis. There is therefore a need for relevant in vitro human HSC models to fill current knowledge gaps. In particular, the roles of vitamin A (VA), lipid droplets (LDs), and energy metabolism in human HSC activation are poorly understood.

METHODS

In this study, human pluripotent stem cell-derived HSCs (scHSCs), benchmarked to human primary HSC, were exposed to 48-hour starvation of retinol (ROL) and palmitic acid (PA) in the presence or absence of the potent HSC activator TGF-β. The interventions were studied by an extensive set of phenotypic and functional analyses, including transcriptomic analysis, measurement of activation-related proteins and cytokines, VA- and LD storage, and cell energy metabolism.

RESULTS

The results show that though the starvation of ROL and PA alone did not induce scHSC activation, the starvation amplified the TGF-β-induced activation-related transcriptome. However, TGF-β-induced activation alone did not lead to a reduction in VA or LD stores. Additionally, reduced glycolysis and increased mitochondrial fission were observed in response to TGF-β.

CONCLUSIONS

scHSCs are robust models for activation studies. The loss of VA and LDs is not sufficient for scHSC activation in vitro, but may amplify the TGF-β-induced activation response. Collectively, our work provides an extensive framework for studying human HSCs in healthy and diseased conditions.

Classification of LAMOST spectra of B-type and hot subdwarf stars using kernel support vector machine

Machine learning has emerged as a leading field in artificial intelligence, demonstrating expert-level performance in various domains. Astronomy has benefited from machine learning techniques, particularly in classifying and identifying stars based on their features. This study focuses on the spectra-based classification of 11,408 B-type and 2422 hot subdwarf stars. The study employs baseline correction using Asymmetric Least Squares (ALS) to enhance classification accuracy. It applies the Pan-Core concept to identify 500 unique patterns or ranges for both types of stars. These patterns are the foundation for creating Support Vector Machine (SVM) models, including the linear (L-SVM), polynomial (P-SVM), and radial basis (R-SVM) kernels. Parameter tuning for the SVM models is achieved through cross-validation. Evaluation of the SVM models on test data reveals that the linear kernel SVM achieves the highest accuracy (87.0%), surpassing the polynomial kernel SVM (84.1%) and radial kernel SVM (80.1%). The average calibrated accuracy falls within the range of 90-95%. These results demonstrate the potential of using spectrum-based classification to aid astronomers in improving and expanding their understanding of stars, with a specific focus on the identification of hot subdwarf stars. This study presents a valuable investigation for astronomers, as it enables the classification of stars based on their spectra, leveraging machine learning techniques to enhance their knowledge and insights in astronomy.

Generating realistic data through modeling and parametric probability for the numerical evaluation of data processing algorithms in two-dimensional chromatography

BACKGROUND

Comprehensive two-dimensional chromatography generates complex data sets, and numerous baseline correction and noise removal algorithms have been proposed in the past decade to address this challenge. However, evaluating their performance objectively is currently not possible due to a lack of objective data.

RESULT

To tackle this issue, we introduce a versatile platform that models and reconstructs single-trace two-dimensional chromatography data, preserving peak parameters. This approach balances real experimental data with synthetic data for precise comparisons. We achieve this by employing a Skewed Lorentz-Normal model to represent each peak and creating probability distributions for relevant parameter sampling. The model's performance has been showcased through its application to two-dimensional gas chromatography data where it has created a data set with 458 peaks with an RMSE of 0.0048 or lower and minimal residuals compared to the original data. Additionally, the same process has been shown in liquid chromatography data.

SIGNIFICANCE

Data analysis is an integral component of any analytical method. The development of new data processing strategies is of paramount importance to tackle the complex signals generated by state-of-the-art separation technology. Through the use of probability distributions, quantitative assessment of algorithm performance of new algorithms is now possible. Therefore, creating new opportunities for faster, more accurate, and simpler data analysis development.

A LIBS spectrum baseline correction method based on the non-parametric prior penalized least squares algorithm

Laser-induced breakdown spectroscopy (LIBS) has become a popular element analysis technique because of its real-time multi-element detection and non-damage advantages. However, due to factors such as laser-substance interaction and the experimental environment, the measured LIBS spectrum signal contains a continuous background, severely influencing spectrum analysis. In this paper, we propose a LIBS spectrum baseline correction method based on the non-parametric prior penalized least squares (NPPPLS) algorithm. Compared with the traditional Penalized Least Squares (PLS) method, improvements have been made in two aspects. On the one hand, a new weight method with faster convergence is proposed. On the other hand, we combine the Adam algorithm and introduce the RMSE of the baseline correction result at the previous time to constrain the update of the balance parameter, which enables the balance parameter to be adjusted adaptively and no parameter prior is required. The simulation results show that the proposed NPPPLS algorithm can achieve excellent correction results, even with no parametric priors. In addition, the performance of the NPPPLS algorithm is not affected by the initial value of the balance parameter, and the stability and robustness are significantly improved. Finally, we conducted baseline correction of the experimental LIBS spectrum and performed univariate and multivariate analyses. The results show that the quantitative analysis accuracy is improved after baseline correction, and the correlation coefficient R2 of different elements obtained by the extreme learning machine method of multivariate analysis can reach 0.99, demonstrating a better quantitative analysis result. The simulation and experimental results verify the excellent performance of the proposed NPPPLS algorithm, which can be effectively used to improve the accuracy of quantitative analysis. In addition, this method is also expected to be used for baseline correction of the Raman spectrum, near-infrared spectrum and so on.

Calibration-free reaction yield quantification by HPLC with a machine-learning model of extinction coefficients

Reaction optimization and characterization depend on reliable measures of reaction yield, often measured by high-performance liquid chromatography (HPLC). Peak areas in HPLC chromatograms are correlated to analyte concentrations by way of calibration standards, typically pure samples of known concentration. Preparing the pure material required for calibration runs can be tedious for low-yielding reactions and technically challenging at small reaction scales. Herein, we present a method to quantify the yield of reactions by HPLC without needing to isolate the product(s) by combining a machine learning model for molar extinction coefficient estimation, and both UV-vis absorption and mass spectra. We demonstrate the method for a variety of reactions important in medicinal and process chemistry, including amide couplings, palladium catalyzed cross-couplings, nucleophilic aromatic substitutions, aminations, and heterocycle syntheses. The reactions were all performed using an automated synthesis and isolation platform. Calibration-free methods such as the presented approach are necessary for such automated platforms to be able to discover, characterize, and optimize reactions automatically.

Enhancement of spectral model transferability in LIBS systems through LIBS-LIPAS fusion technique

BACKGROUND

Laser-induced breakdown spectroscopy (LIBS) is extensively utilized a range of scientific and industrial detection applications owing to its capability for rapid, in-situ detection. However, conventional LIBS models are often tailored to specific LIBS systems, hindering their transferability between LIBS subsystems. Transfer algorithms can adapt spectral models to subsystems, but require access to the datasets of each subsystem beforehand, followed by making individual adjustments for the dataset of each subsystem. It is clear that a method to enhance the inherent transferability of spectral original models is urgently needed.

RESULTS

We proposed an innovative fusion methodology, named laser-induced breakdown spectroscopy fusion laser-induced plasma acoustic spectroscopy (LIBS-LIPAS), to enhance the transferability of support vector machine (SVM) original models across LIBS systems with varying laser beams. The methodology was demonstrated using nickel-based high-temperature alloy samples. Here, the area-full width at half maximum (AFCEI) Composite Evaluation Index was proposed for extracting critical features from LIBS. Further enhancing the transferability of the model, the laser-induced plasma acoustic signal was transformed from the time domain to the frequency domain. Subsequently, the feature-level fusion method was employed to improve the classification accuracy of the transferred LIBS system to 97.8 %. A decision-level fusion approach (amalgamating LIBS, LIPAS, and feature-level fusion models) achieved an exemplary accuracy of 99 %. Finally, the adaptability of the method was demonstrated using titanium alloy samples.

SIGNIFICANCE AND NOVELTY

In this work, based on plasma radiation models, we simultaneously captured LIBS and LIPAS, and proposed the fusion of these two distinct yet origin-consistent signals, significantly enhancing the transferability of the LIBS original model. The methodology proposed holds significant potential to advance LIBS technology and broaden its applicability in analytical chemistry research and industrial applications.

The spectrum of Ih ice using terahertz time-domain spectroscopy

Here, we report the frequency-dependent spectrum of ice Ih in the range of 0.2-2 THz. We confirm the presence of a feature that blue-shifts from around 1.55-1.65 THz with a decreasing temperature from 260 to 160 K. There is also a change in the trend of the refractive index of ice corresponding to a dispersion, which is also around 1.6 THz. The features are reproduced in data acquired with three commercial terahertz time-domain spectrometers. Computer-simulated spectra assign the feature to lattice translations perpendicular to the 110 and 1̄10 planes of the ice Ih crystal. The feature's existence should be recognized in the terahertz measurements of frozen aqueous solution samples to avoid false interpretations.

Pretreatment-free SERS sensing of microplastics using a self-attention-based neural network on hierarchically porous Ag foams

Low-cost detection systems are needed for the identification of microplastics (MPs) in environmental samples. However, their rapid identification is hindered by the need for complex isolation and pre-treatment methods. This study describes a comprehensive sensing platform to identify MPs in environmental samples without requiring independent separation or pre-treatment protocols. It leverages the physicochemical properties of macroporous-mesoporous silver (Ag) substrates templated with self-assembled polymeric micelles to concurrently separate and analyze multiple MP targets using surface-enhanced Raman spectroscopy (SERS). The hydrophobic layer on Ag aids in stabilizing the nanostructures in the environment and mitigates biofouling. To monitor complex samples with multiple MPs and to demultiplex numerous overlapping patterns, we develop a neural network (NN) algorithm called SpecATNet that employs a self-attention mechanism to resolve the complex dependencies and patterns in SERS data to identify six common types of MPs: polystyrene, polyethylene, polymethylmethacrylate, polytetrafluoroethylene, nylon, and polyethylene terephthalate. SpecATNet uses multi-label classification to analyze multi-component mixtures even in the presence of various interference agents. The combination of macroporous-mesoporous Ag substrates and self-attention-based NN technology holds potential to enable field monitoring of MPs by generating rich datasets that machines can interpret and analyze.

Application of digital twins for simulation based tailoring of laser induced graphene

In the era of man-machine interfaces, digital twins stand as a key technology, offering virtual representations of real-world objects, processes, and systems through computational models. They enable novel ways of interacting with, comprehending, and manipulating real-world entities within a virtual realm. The real implementation of graphene-based sensors and electronic devices remains challenging due to the integration complexities of high-quality graphene materials with existing manufacturing processes. To address this, scalable techniques for the in-situ fabrication of graphene-like materials are essential. One promising method involves using a CO2 laser to convert polyimide into graphene. Optimizing this graphitization process is hindered by complex parameter interactions and nonlinear terms. This article explores how these digital replicas can enhance the fabrication of laser-induced graphene (LIG) through laser simulation and machine learning methods to enable rapid single-step LIG patterning. This approach aims to create a universal simulation for all CO2 lasers, calculating optical energy flux and utilizing machine learning to control and predict LIG conductivity (ability to conduct current), morphology, and electrical resistance. The proposed procedure, integrating digital twins in the LIG production process, will avoid or reduce the preliminary tests required to determine the proper laser parameters to reach the desired LIG characteristics. Accordingly, this approach will reduce the time and costs associated with these tests and thus increase the efficiency and optimize the procedure.

ELM combined with differential Raman spectroscopy for the detection of microplastics in organisms

Aiming at the problems of low extraction efficiency, high false detection rate, weak Raman signal and serious interference by fluorescence signal in the detection of microplastics in marine organisms, this paper establishes a set of rapid detection methods for microplastics in organisms, including confocal Raman spectroscopy, fluorescence imaging, differential Raman spectroscopy, and rapid identification of microplastics based on the ELM modeling assistance. Firstly, to address the problem of low extraction efficiency of microplastics, we explored and optimized the digestion method of tissues, which effectively improved the digestion effect of fish tissues and excluded the influence of tissues on microplastics detection. Aiming at the problems of high misdetection rate and low pre-screening efficiency of microplastics, fluorescence imaging technology is adopted to realize the visualization and detection of microplastics, which effectively improves the detection efficiency and precision of microplastics. Based on the confocal microscopy Raman spectroscopy detection system built independently in the laboratory, using 784/785 nm as the excitation light, the differential Raman spectroscopy technique effectively excludes the interference of fluorescence signals in the Raman spectra, and improves the signal-to-noise ratio of the Raman spectra, and the recovery rate of the Raman characteristic peaks in the differential Raman spectroscopy reaches 100 % compared to the traditional baseline correction method, which is 33.3 % higher than that of the baseline correction method. Finally, a microplastic identification model is constructed based on ELM to assist in realizing the rapid and accurate identification of microplastics. The more complete detection method of microplastics in marine organisms proposed in this paper can realize the rapid and nondestructive, efficient and accurate detection of microplastics in fish, which can help to further promote the development of marine microplastics monitoring technology.

Automatic center identification of electron diffraction with multi-scale transformer networks

Selected area electron diffraction (SAED) is a widely used technique for characterizing the structure and measuring lattice parameters of materials. An autonomous analytic method has become an urgent demand for the large-scale SAED data produced from in-situ experiments. In this work, we realize the automatic processing for center identification with a proposed deep segmentation model named the multi-scale Transformer (MS-Trans) network. This algorithm enables robust segmentation of the central spots by combining a novel gated axial-attention module and multi-scale feature fusion. The proposed MS-Trans model shows high precision and robustness, enabling autonomous processing of SAED patterns without any prior knowledge. The application on in-situ SAED data of the oxidation process of FeNi alloy demonstrates its capability of implementing autonomous quantitative processing. © 2017 Elsevier Inc. All rights reserved.

Raman spectroscopy and one-dimensional convolutional neural network modeling as a real-time monitoring tool for in vitro transaminase-catalyzed synthesis of a pharmaceutically relevant amine precursor

Raman spectroscopy has been used to measure the concentration of a pharmaceutically relevant model amine intermediate for positive allosteric modulators of nicotinic acetylcholine receptor in a ω-transaminase-catalyzed conversion. A model based on a one-dimensional convolutional neural network was developed to translate raw data augmented Raman spectra directly into substrate concentrations, with which the conversion from ketone to amine by ω-transaminase could be determined over time. The model showed very good predictive capabilities, with R2 values higher than 0.99 for the spectra included in the modeling and 0.964 for an independent dataset. However, the model could not extrapolate outside the concentrations specified by the model. The presented work shows the potential of Raman spectroscopy as a real-time monitoring tool for biocatalytic reactions.

Raman difference spectroscopy and U-Net convolutional neural network for molecular analysis of cutaneous neurofibroma

In Neurofibromatosis type 1 (NF1), peripheral nerve sheaths tumors are common, with cutaneous neurofibromas resulting in significant aesthetic, painful and functional problems requiring surgical removal. To date, determination of adequate surgical resection margins-complete tumor removal while attempting to preserve viable tissue-remains largely subjective. Thus, residual tumor extension beyond surgical margins or recurrence of the disease may frequently be observed. Here, we introduce Shifted-Excitation Raman Spectroscopy in combination with deep neural networks for the future perspective of objective, real-time diagnosis, and guided surgical ablation. The obtained results are validated through established histological methods. In this study, we evaluated the discrimination between cutaneous neurofibroma (n = 9) and adjacent physiological tissues (n = 25) in 34 surgical pathological specimens ex vivo at a total of 82 distinct measurement loci. Based on a convolutional neural network (U-Net), the mean raw Raman spectra (n = 8,200) were processed and refined, and afterwards the spectral peaks were assigned to their respective molecular origin. Principal component and linear discriminant analysis was used to discriminate cutaneous neurofibromas from physiological tissues with a sensitivity of 100%, specificity of 97.3%, and overall classification accuracy of 97.6%. The results enable the presented optical, non-invasive technique in combination with artificial intelligence as a promising candidate to ameliorate both, diagnosis and treatment of patients affected by cutaneous neurofibroma and NF1.

Power of Light: Raman Spectroscopy and Machine Learning for the Detection of Lung Cancer

Lung cancer is the leading cause of cancer-related deaths worldwide, emphasizing the urgent need for reliable and efficient diagnostic methods. Conventional approaches often involve invasive procedures and can be time-consuming and costly, thereby delaying the effective treatment. The current study explores the potential of Raman spectroscopy, as a promising noninvasive technique, by analyzing human blood plasma samples from lung cancer patients and healthy controls. In a benchmark study, 16 machine learning models were evaluated by employing four strategies: the combination of dimensionality reduction with classifiers; application of feature selection prior to classification; stand-alone classifiers; and a unified predictive model. The models showed different performances due to the inherent complexity of the data, achieving accuracies from 0.77 to 0.85 and areas under the curve for receiver operating characteristics from 0.85 to 0.94. Hybrid methods incorporating dimensionality reduction and feature selection algorithms present the highest figures of merit. Nevertheless, all machine learning models deliver creditable scores and demonstrate that Raman spectroscopy represents a powerful method for future in vitro diagnostics of lung cancer.

A three-stage deep learning-based training frame for spectra baseline correction

For spectrometers, baseline drift seriously affects the measurement and quantitative analysis of spectral data. Deep learning has recently emerged as a powerful method for baseline correction. However, the dependence on vast amounts of paired data and the difficulty in obtaining spectral data limit the performance and development of deep learning-based methods. Therefore, we solve these problems from the network architecture and training framework. For the network architecture, a Learned Feature Fusion (LFF) module is designed to improve the performance of U-net, and a three-stage training frame is proposed to train this network. Specifically, the LFF module is designed to adaptively integrate features from different scales, greatly improving the performance of U-net. For the training frame, stage 1 uses airPLS to ameliorate the problem of vast amounts of paired data, stage 2 uses synthetic spectra to further ease reliance on real spectra, and stage 3 uses contrastive learning to reduce the gap between synthesized and real spectra. The experiments show that the proposed method is a powerful tool for baseline correction and possesses potential for application in spectral quantitative analysis.

Calibration of Dual-Channel Raman Spectrometer via Optical Frequency Comb

The portable Raman spectrometer boasts portability, rapid analysis, and high flexibility. It stands as a crucial and powerful technical tool for analyzing the chemical composition of samples, whether biological or non-biological, across diverse fields. To improve the resolution of grating spectrometers and ensure a wide spectral range, many spectrometer systems have been designed with double-grating structures. However, the impact of external forces, such as installation deviations and inevitable collisions, may cause differences between the actual state of the internal spectrometer components and their theoretical values. Therefore, spectrometers must be calibrated to establish the relationship between the wavelength and the pixel positions. The characteristic peaks of commonly used calibration substances are primarily distributed in the 200-2000 cm-1 range. The distribution of characteristic peaks in other wavenumber ranges is sparse, especially for spectrometers with double-channel spectral structures and wide spectral ranges. This uneven distribution of spectral peaks generates significant errors in the polynomial fitting results used to calibrate spectrometers. Therefore, to satisfy the calibration requirements of a dual-channel portable Raman spectrometer with a wide spectral range, this study designed a calibration method based on an optical frequency comb, which generates dense and uniform comb-like spectral signals at equal intervals. The method was verified experimentally and compared to the traditional calibration method of using a mercury-argon lamp. The results showed that the error bandwidth of the calibration results of the proposed method was significantly smaller than that of the mercury-argon lamp method, thus demonstrating a substantial improvement in the calibration accuracy.

Acquiring of photosensitivity by Mycobacterium tuberculosis in vitro and inside infected macrophages is associated with accumulation of endogenous Zn-porphyrins

Mycobacterium tuberculosis (Mtb) is able to transition into a dormant state, causing the latent state of tuberculosis. Dormant mycobacteria acquire resistance to all known antibacterial drugs and can survive in the human body for decades before becoming active. In the dormant forms of M. tuberculosis, the synthesis of porphyrins and its Zn-complexes significantly increased when 5-aminolevulinic acid (ALA) was added to the growth medium. Transcriptome analysis revealed an activation of 8 genes involved in the metabolism of tetrapyrroles during the Mtb transition into a dormant state, which may lead to the observed accumulation of free porphyrins. Dormant Mtb viability was reduced by more than 99.99% under illumination for 30 min (300 J/cm2) with 565 nm light that correspond for Zn-porphyrin and coproporphyrin absorptions. We did not observe any PDI effect in vitro using active bacteria grown without ALA. However, after accumulation of active cells in lung macrophages and their persistence within macrophages for several days in the presence of ALA, a significant sensitivity of active Mtb cells (ca. 99.99%) to light exposure was developed. These findings create a perspective for the treatment of latent and multidrug-resistant tuberculosis by the eradication of the pathogen in order to prevent recurrence of this disease.

An Iterative Curve-Fitting Baseline Correction Method for Raman Spectra Driven by Neural Network

Baseline correction is a vital part of spectral preprocessing, especially for Raman spectra. Iterative polynomial fitting is an easy but less accurate way to find baselines compared to other methods such as wavelet transform and penalized least squares (PLS) methods. The polynomial fitting methods can also get distorted results in certain conditions. In this paper, a neural network model for detecting the trend of the baseline was proposed to improve the correction accuracy of the fitting methods. The model selects the function basis according to the baseline trend instead of using a fixed polynomial function to match the baseline for a more precise fit. We also propose a way to generate simulation data, these data can be used to train the neural network model. The model provides reliable results for real spectral data with noise. Our method provides a new idea to correct the baseline with a strange shape. In addition, we examine the limitations of conventional iterative polynomial fitting, adaptive iteratively reweighted PLS and explain why our approach surpasses these methods.

Deep neural network: As the novel pipelines in multiple preprocessing for Raman spectroscopy

Raman spectroscopy is a kind of vibrational method that can rapidly and non-invasively gives chemical structural information with the Raman spectrometer. Despite its technical advantages, in practical application scenarios, Raman spectroscopy often suffers from interference, such as noises and baseline drifts, resulting in the inability to acquire high-quality Raman spectroscopy signals, which brings challenges to subsequent spectral analysis. The commonly applied spectral preprocessing methods, such as Savitzky-Golay smooth and wavelet transform, can only perform corresponding single-item processing and require manual intervention to carry out a series of tedious trial parameters. Especially, each scheme can only be used for a specific data set. In recent years, the development of deep neural networks has provided new solutions for intelligent preprocessing of spectral data. In this paper, we first creatively started from the basic mechanism of spectral signal generation and constructed a mathematical model of the Raman spectral signal. By counting the noise parameters of the real system, we generated a simulation dataset close to the output of the real system, which alleviated the dependence on data during deep learning training. Due to the powerful nonlinear fitting ability of the neural network, fully connected network model is constructed to complete the baseline estimation task simply and quickly. Then building the Unet model can effectively achieve spectral denoising, and combining it with baseline estimation can realize intelligent joint processing. Through the simulation dataset experiment, it is proved that compared with the classic method, the method proposed in this paper has obvious advantages, which can effectively improve the signal quality and further ensure the accuracy of the peak intensity. At the same time, when the proposed method is applied to the actual system, it also achieves excellent performance compared with the common method, which indirectly indicates the effectiveness of the Raman signal simulation model. The research presented in this paper offers a variety of efficient pipelines for the intelligent processing of Raman spectroscopy, which can adapt to the requirements of different tasks while providing a new idea for enhancing the quality of Raman spectroscopy signals.

Double-sided asymmetric method for automated fetal heart rate baseline calculation

The fetal heart rate (FHR) signal is used to assess the well-being of a fetus during labor. Manual interpretation of the FHR is subject to high inter- and intra-observer variability, leading to inconsistent clinical decision-making. The baseline of the FHR signal is crucial for its interpretation. An automated method for baseline determination may reduce interpretation variability. Based on this claim, we present the Auto-Regressed Double-Sided Improved Asymmetric Least Squares (ARDSIAsLS) method as a baseline calculation algorithm designed to imitate expert obstetrician baseline determination. As the FHR signal is prone to a high rate of missing data, a step of gap interpolation in a physiological manner was implemented in the algorithm. The baseline of the interpolated signal was determined using a weighted algorithm of two improved asymmetric least squares smoothing models and an improved symmetric least squares smoothing model. The algorithm was validated against a ground truth determined from annotations of six expert obstetricians. FHR baseline calculation performance of the ARDSIAsLS method yielded a mean absolute error of 2.54 bpm, a max absolute error of 5.22 bpm, and a root mean square error of 2.89 bpm. In a comparison between the algorithm and 11 previously published methods, the algorithm outperformed them all. Notably, the algorithm was non-inferior to expert annotations. Automating the baseline FHR determination process may help reduce practitioner discordance and aid decision-making in the delivery room.

A Calibration-Free Optical Emission Spectroscopic Method to Determine the Composition of a Spark Discharge Plasma Used for AuAg Binary Nanoparticle Synthesis

Spark discharge generators (SDGs) employ controlled gaseous environments to induce spark ablation of non-insulating electrodes, resulting in the formation of various nanostructures in the gas phase. The method offers technological advantages such as continuous particle production, scalable yield, and minimal waste. Additionally, the versatility of the process enables the generation of alloy nanoparticles from various material combinations, including immiscible ones. In order to fully exploit its potential, understanding the atomic mixing process during electrode ablation, particularly in the case of dissimilar electrodes, is crucial. Temporally and spatially resolved optical emission spectroscopy (OES) has been previously demonstrated as an effective characterization tool for spark plasmas in SDGs. However, to gain a deeper insight into the vapor mixing process, it is essential to quantitatively determine the plasma composition in both space and time. This paper introduces a calibration-free OES-based method tailored for spark plasmas utilized in binary nanoparticle generation. The method introduces the so-called multi-element combinatory Boltzmann plots, which use intensity ratios of emission atomic lines from different materials, allowing for the direct estimation of total number concentration ratios. The approach is tested using synthetic spectra and validated with experimental spark spectra obtained near an alloyed gold-silver (AuAg) electrode with a known composition. The study demonstrates the capabilities and robustness of the proposed method, with a focus on the AuAg system due to its significance in plasmonic research and frequent synthesis using spark ablation.

Chiral Monitoring Across Both Enantiomeric Excess and Concentration Space: Leveraging Quantum Cascade Lasers for Sensitive Vibrational Circular Dichroism Spectroscopy

Recently, high-throughput quantum cascade laser-based vibrational circular dichroism (QCL-VCD) technology has reduced the measurement time for high-quality vibrational circular dichroism spectra from hours to a few minutes. This study evaluates QCL-VCD for chiral monitoring using flow-through measurement of a changing sample in a circulating loop. A balanced detection QCL-VCD system was applied to the enantiomeric pair R/S-1,1'-bi-2-naphthol in solution. Different mixtures of the two components were used to simulate a racemization process, collecting spectral data at a time resolution of 6 min, and over three concentration levels. The goal of this experimental setup was to evaluate QCL-VCD in terms of both molar and enantiomeric excess (EE) sensitivity at a time resolution relevant to chiral monitoring in chemical processes. Subsequent chemometric evaluation by partial least squares regression revealed a cross-validated prediction accuracy of 2.8% EE with a robust prediction also for the test data set (error = 3.5% EE). In addition, the data set was also treated with the least absolute shrinkage and selection operator (LASSO), which also achieved a robust prediction. Due to the operating principle of LASSO, the obtained coefficients constituted a few discrete spectral frequencies, which represent the most variance. This information can be used in the future for dedicated QCL-based instrument design, gaining a higher time resolution without sacrificing predictive capabilities.

An adaptive extended Gaussian peak derivative reweighted penalised least squares method for baseline correction

Baseline drift is an important issue in spectral analysis (e.g., infrared, Raman, and laser-induced spectroscopy). Most common methods for baseline correction perform poorly in high noise, complex baselines, and overlapping peaks. To solve this problem, we proposed an adaptive extended Gaussian peak derivative reweighted penalised least squares (agdPLS) method for removing baseline drift from spectra. The method added extended Gaussian peaks to spectra, added derivative terms for spectral and baseline differences during iterations, and adaptively adjusted penalty coefficients λ. Experiments with simulated and measured spectra for methane and ethane were carried out to compare the performance of the different methods. agdPLS performed better than the other methods, with more accurate baseline estimation in low- and high-noise situations. Especially when the spectrum contained high noise, complex baselines and overlapping peaks, the agdPLS method performed significantly better than other methods. Moreover, agdPLS was computationally efficient. Results of actual spectral experiments showed that the proposed agdPLS method could be effective for baseline correction of spectra which, in turn, improved qualitative and quantitative spectral performances.

Comprehensive two-dimensional gas chromatography- A discussion on recent innovations

Although comprehensive 2-D GC is an established and often applied analytical method, the field is still highly dynamic thanks to a remarkable number of innovations. In this review, we discuss a number of recent developments in comprehensive 2-D GC technology. A variety of modulation methods are still being actively investigated and many exciting improvements are discussed in this review. We also review interesting developments in detection methods, retention modeling, and data analysis.

Effect of pre-analytical variables on Raman and FTIR spectral content of lymphocytes

The use of Fourier transform infrared (FTIR) and Raman spectroscopy (RS) for the analysis of lymphocytes in clinical applications is increasing in the field of biomedicine. The pre-analytical phase, which is the most vulnerable stage of the testing process, is where most errors and sample variance occur; however, it is unclear how pre-analytical variables affect the FTIR and Raman spectra of lymphocytes. In this study, we evaluated how pre-analytical procedures undertaken before spectroscopic analysis influence the spectral integrity of lymphocytes purified from the peripheral blood of male volunteers (n = 3). Pre-analytical variables investigated were associated with (i) sample preparation, (blood collection systems, anticoagulant, needle gauges), (ii) sample storage (fresh or frozen), and (iii) sample processing (inter-operator variability, time to lymphocyte isolation). Although many of these procedural pre-analytical variables did not alter the spectral signature of the lymphocytes, evidence of spectral effects due to the freeze-thaw cycle, in vitro culture inter-operator variability and the time to lymphocyte isolation was observed. Although FTIR and RS possess clinical potential, their translation into a clinical environment is impeded by a lack of standardisation and harmonisation of protocols related to the preparation, storage, and processing of samples, which hinders uniform, accurate, and reproducible analysis. Therefore, further development of protocols is required to successfully integrate these techniques into current clinical workflows.

Library-Based Raman Spectral Identification Using Multi-Input Hybrid ResNet

Raman spectroscopy is widely used for its exceptional identification capabilities in various fields. Traditional methods for target identification using Raman spectroscopy rely on signal correlation with moving windows, requiring data preprocessing that can significantly impact identification performance. In recent years, deep-learning approaches have been proposed to leverage data augmentation techniques, such as baseline and additive noise addition, in order to overcome data scarcity. However, these deep-learning methods are limited to the spectra encountered during training and struggle to handle unseen spectra. To address these limitations, we propose a multi-input hybrid deep-learning model trained with simulated spectral data. By employing simulated spectra, our method tackles the challenges of data scarcity and the handling of unseen spectra encountered in traditional and deep-learning methods. Experimental results demonstrate that our proposed method achieves outstanding identification performance and effectively handles spectra obtained from different Raman spectroscopy systems.

Enabling High Precision Gradient Index Control in Subsurface Multiphoton Lithography

Multiphoton lithography inside a mesoporous host can create optical components with continuously tunable refractive indices in three-dimensional (3D) space. However, the process is very sensitive at exposure doses near the photoresist threshold, leading previous work to reliably achieve only a fraction of the available refractive index range for a given material system. Here, we present a method for greatly enhancing the uniformity of the subsurface micro-optics, increasing the reliable index range from 0.12 (in prior work) to 0.37 and decreasing the standard deviation (SD) at threshold from 0.13 to 0.0021. Three modifications to the previous method enable higher uniformity in all three spatial dimensions: (1) calibrating the planar write field of mirror galvanometers using a spatially varying optical transmission function which corrects for large-scale optical aberrations; (2) periodically relocating the piezoelectrically driven stage, termed piezo-galvo dithering, to reduce small-scale errors in writing; and (3) enforcing a constant time between each lateral cross section to reduce variation across all writing depths. With this new method, accurate fabrication of optics of any index between n = 1.20 and 1.57 (SD < 0.012 across the full range) was achieved inside a volume of porous silica. We demonstrate the importance of this increased accuracy and precision by fabricating and characterizing calibrated two-dimensional (2D) line gratings and flat gradient index lenses with significantly better performance than the corresponding control devices. As a visual representation, the University of Illinois logo made with 2D line gratings shows significant improvement in its color uniformity across its width.

Raman Diffusion-Ordered Spectroscopy

The Stokes-Einstein relation, which relates the diffusion coefficient of a molecule to its hydrodynamic radius, is commonly used to determine molecular sizes in chemical analysis methods. Here, we combine the size sensitivity of such diffusion-based methods with the structure sensitivity of Raman spectroscopy by performing Raman diffusion-ordered spectroscopy (Raman-DOSY). The core of the Raman-DOSY setup is a flow cell with a Y-shaped channel containing two inlets: one for the sample solution and one for the pure solvent. The two liquids are injected at the same flow rate, giving rise to two parallel laminar flows in the channel. After the flow stops, the solute molecules diffuse from the solution-filled half of the channel into the solvent-filled half at a rate determined by their hydrodynamic radius. The arrival of the solute molecules in the solvent-filled half of the channel is recorded in a spectrally resolved manner by Raman microspectroscopy. From the time series of Raman spectra, a two-dimensional Raman-DOSY spectrum is obtained, which has the Raman frequency on one axis and the diffusion coefficient (or equivalently, hydrodynamic radius) on the other. In this way, Raman-DOSY spectrally resolves overlapping Raman peaks arising from molecules of different sizes. We demonstrate Raman-DOSY on samples containing up to three compounds and derive the diffusion coefficients of small molecules, proteins, and supramolecules (micelles), illustrating the versatility of Raman-DOSY. Raman-DOSY is label-free and does not require deuterated solvents and can thus be applied to samples and matrices that might be difficult to investigate with other diffusion-based spectroscopy methods.

Recent advances in surface enhanced Raman spectroscopy for bacterial pathogen identifications

BACKGROUND

The rapid and reliable detection of pathogenic bacteria at an early stage is a highly significant research field for public health. However, most traditional approaches for pathogen identification are time-consuming and labour-intensive, which may cause physicians making inappropriate treatment decisions based on an incomplete diagnosis of patients with unknown infections, leading to increased morbidity and mortality. Therefore, novel methods are constantly required to face the emerging challenges of bacterial detection and identification. In particular, Raman spectroscopy (RS) is becoming an attractive method for rapid and accurate detection of bacterial pathogens in recent years, among which the newly developed surface-enhanced Raman spectroscopy (SERS) shows the most promising potential.

AIM OF REVIEW

Recent advances in pathogen detection and diagnosis of bacterial infections were discussed with focuses on the development of the SERS approaches and its applications in complex clinical settings.

KEY SCIENTIFIC CONCEPTS OF REVIEW

The current review describes bacterial classification using surface enhanced Raman spectroscopy (SERS) for developing a rapid and more accurate method for the identification of bacterial pathogens in clinical diagnosis. The initial part of this review gives a brief overview of the mechanism of SERS technology and development of the SERS approach to detect bacterial pathogens in complex samples. The development of the label-based and label-free SERS strategies and several novel SERS-compatible technologies in clinical applications, as well as the analytical procedures and examples of chemometric methods for SERS, are introduced. The computational challenges of pre-processing spectra and the highlights of the limitations and perspectives of the SERS technique are also discussed.Taken together, this systematic review provides an overall summary of the SERS technique and its application potential for direct bacterial diagnosis in clinical samples such as blood, urine and sputum, etc.

Inversion model for extracting chemically resolved depth profiles across liquid interfaces of various configurations from XPS data: PROPHESY

PROPHESY, a technique for the reconstruction of surface-depth profiles from X-ray photoelectron spectroscopy data, is introduced. The inversion methodology is based on a Bayesian framework and primal-dual convex optimization. The acquisition model is developed for several geometries representing different sample types: plane (bulk sample), cylinder (liquid microjet) and sphere (droplet). The methodology is tested and characterized with respect to simulated data as a proof of concept. Possible limitations of the method due to uncertainty in the attenuation length of the photo-emitted electron are illustrated.

Raman mapping for determination of TiO2 in different solid food samples by multivariate curve resolution with alternating least squares

Titanium dioxide is a food additive commonly used as a white food coloring (E171). Its wide use by the food industry associated with the nanometric size distribution of the particles of this pigment has shown high genotoxicity associated with recurrent exposure by ingestion. Therefore, the use of E171 in food products has already been banned by some industries and in the European Union. Such banishment should soon be extended to other countries around the world, making it important to establish techniques for the efficient determination of TiO₂ in different food products. The association between hyperspectral images and chemometric tools can be useful in this sense, aiming to enable the use of a single method for sample preparation and analysis of different types of food. Thus, the present work aims to evaluate the use of Raman mapping associated with the resolution of multivariate curves with alternating least squares (MCR-ALS) for the determination of titanium dioxide in solid food samples with different compositions, without the need to introduce specific sample preparation. The proposed method allowed for the first-time quantification of TiO₂ in different food matrices without specific sample preparation, with a simple, rapid, accurate (93% of recovery), low detection limits (0.0111% m/m) and quantification (0.0370% m/m) and adequate linearity (r = 0.9990) and precise (standard deviation around 0.020-0.030% w/w) methodology. Such results highlight the potential use of Raman mapping associated with the MCR-ALS for quantification of the nano-TiO₂ in commercial samples.

Estimating Indoor Pollutant Loss Using Mass Balances and Unsupervised Clustering to Recognize Decays

Low-cost air quality monitors are increasingly being deployed in various indoor environments. However, data of high temporal resolution from those sensors are often summarized into a single mean value, with information about pollutant dynamics discarded. Further, low-cost sensors often suffer from limitations such as a lack of absolute accuracy and drift over time. There is a growing interest in utilizing data science and machine learning techniques to overcome those limitations and take full advantage of low-cost sensors. In this study, we developed an unsupervised machine learning model for automatically recognizing decay periods from concentration time series data and estimating pollutant loss rates. The model uses k-means and DBSCAN clustering to extract decays and then mass balance equations to estimate loss rates. Applications on data collected from various environments suggest that the CO2 loss rate was consistently lower than the PM2.5 loss rate in the same environment, while both varied spatially and temporally. Further, detailed protocols were established to select optimal model hyperparameters and filter out results with high uncertainty. Overall, this model provides a novel solution to monitoring pollutant removal rates with potentially wide applications such as evaluating filtration and ventilation and characterizing indoor emission sources.

Applications of the non-negative least-squares deconvolution method to analyze energy-dispersive x-ray fluorescence spectra

We used the Monte Carlo simulation method to establish a detector response matrix and the non-negative least-squares method to deconvolute x-ray spectra. The simulation and experimental data verified the effectiveness of this method, and the influence of full-width at the half of the maximum calibration accuracy on the deconvolution results was investigated. The non-negative least-squares method had high accuracy and efficiency compared with others. The results showed that, except for Zn, the relative errors between the inversion and the standard values were less than 0.1% for the simulated spectra. For the experimental data, the relative errors were within 0.2%. The peaks with similar characteristic energies can be better distinguished in the deconvolution spectra, reducing the errors caused by overlapping peaks in subsequent analysis.

Plasma-Induced Surface Modification of Sapphire and Its Influence on Graphene Grown by Plasma-Enhanced Chemical Vapour Deposition

In this work, we study the influence of the different surface terminations of c-plane sapphire substrates on the synthesis of graphene via plasma-enhanced chemical vapor deposition. The different terminations of the sapphire surface are controlled by a plasma process. A design of experiments procedure was carried out to evaluate the major effects governing the plasma process of four different parameters: i.e., discharge power, time, pressure and gas employed. In the characterization of the substrate, two sapphire surface terminations were identified and characterized by means of contact angle measurements, being a hydrophilic (hydrophobic) surface and the fingerprint of an Al- (OH-) terminated surface, respectively. The defects within the synthesized graphene were analyzed by Raman spectroscopy. Notably, we found that the I_D/I_G ratio decreases for graphene grown on OH-terminated surfaces. Furthermore, two different regimes related to the nature of graphene defects were identified and, depending on the sapphire terminated surface, are bound either to vacancy or boundary-like defects. Finally, studying the density of defects and the crystallite area, as well as their relationship with the sapphire surface termination, paves the way for increasing the crystallinity of the synthesized graphene.

Baseline correction for Raman spectra using a spectral estimation-based asymmetrically reweighted penalized least squares method

Baseline correction is necessary for the qualitative and quantitative analysis of samples because of the existence of background fluorescence interference in Raman spectra. The asymmetric least squares (ALS) method is an adaptive and automated algorithm that avoids peak detection operations along with other user interactions. However, current ALS-based improved algorithms only consider the smoothness configuration of regions where the signals are greater than the fitted baseline, which results in smoothing distortion. In this paper, an asymmetrically reweighted penalized least squares method based on spectral estimation (SEALS) is proposed. SEALS considers not only the uniform distribution of additive noise along the baseline but also the energy distribution of the signal above and below the fitted baseline. The energy distribution is estimated using inverse Fourier and autoregressive models to create a spectral estimation kernel. This kernel effectively optimizes and balances the asymmetric weight assigned to each data point. By doing so, it resolves the issue of local oversmoothing that is typically encountered in the asymmetrically reweighted penalized least squares method. This oversmoothing problem can negatively impact the iteration depth and accuracy of baseline fitting. In comparative experiments on simulated spectra, SEALS demonstrated a better baseline fitting performance compared to several other advanced baseline correction methods, both under moderate and strong fluorescence backgrounds. It has also been proven to be highly resistant to noise interference. When applied to real Raman spectra, the algorithm correctly restored the weak peaks and removed the fluorescence peaks, demonstrating the effectiveness of this method. The computation time of the proposed method was approximately 0.05 s, which satisfies the real-time baseline correction requirements of practical spectroscopy acquisition.

Low-Variance Surface-Enhanced Raman Spectroscopy Using Confined Gold Nanoparticles over Silicon Nanocones

Surface-enhanced Raman spectroscopy (SERS) substrates are of utmost interest in the analyte detection of biological and chemical diagnostics. This is primarily due to the ability of SERS to sensitively measure analytes present in localized hot spots of the SERS nanostructures. In this work, we present the formation of 67 ± 6 nm diameter gold nanoparticles supported by vertically aligned shell-insulated silicon nanocones for ultralow variance SERS. The nanoparticles are obtained through discrete rotation glancing angle deposition of gold in an e-beam evaporating system. The morphology is assessed through focused ion beam tomography, energy-dispersive X-ray spectroscopy, and scanning electron microscopy. The optical properties are discussed and evaluated through reflectance measurements and finite-difference time-domain simulations. Lastly, the SERS activity is measured by benzenethiol functionalization and subsequent Raman spectroscopy in the surface scanning mode. We report a homogeneous analytical enhancement factor of 2.2 ± 0.1 × 10⁷ (99% confidence interval for N = 400 grid spots) and made a comparison to other lithographically derived assemblies used in SERS. The strikingly low variance (4%) of our substrates facilitates its use for many potential SERS applications.

A double sliding-window method for baseline correction and noise estimation for Raman spectra of microplastics

When measuring microplastics of environmental samples, additives and attachment of biological materials may result in strong fluorescence in Raman spectra, which increases difficulty for imaging, identification, and quantification. Although there are several baseline correction methods available, user intervention is usually needed, which is not feasible for automated processes. In current study, a double sliding-window (DSW) method was proposed to estimate the baseline and standard deviation of noise. Simulated spectra and experimental spectra were used to evaluate the performance in comparison with two popular and widely used methods. Validation with simulated spectra and spectra of environmental samples showed that DSW method can accurately estimate the standard deviation of spectral noise. DSW method also showed better performance than compared methods when handling spectra of low signal-to-noise ratio (SNR) and elevated baselines. Therefore, DSW method is a useful approach for preprocessing Raman spectra of environmental samples and automated processes.

Standardized Database of 12-Lead Electrocardiograms with a Common Standard for the Promotion of Cardiovascular Research: KURIAS-ECG

OBJECTIVES

Electrocardiography (ECG)-based diagnosis by experts cannot maintain uniform quality because individual differences may occur. Previous public databases can be used for clinical studies, but there is no common standard that would allow databases to be combined. For this reason, it is difficult to conduct research that derives results by combining databases. Recent commercial ECG machines offer diagnoses similar to those of a physician. Therefore, the purpose of this study was to construct a standardized ECG database using computerized diagnoses.

METHODS

The constructed database was standardized using Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) and Observational Medical Outcomes Partnership-common data model (OMOP-CDM), and data were then categorized into 10 groups based on the Minnesota classification. In addition, to extract high-quality waveforms, poor-quality ECGs were removed, and database bias was minimized by extracting at least 2,000 cases for each group. To check database quality, the difference in baseline displacement according to whether poor ECGs were removed was analyzed, and the usefulness of the database was verified with seven classification models using waveforms.

RESULTS

The standardized KURIAS-ECG database consists of high-quality ECGs from 13,862 patients, with about 20,000 data points, making it possible to obtain more than 2,000 for each Minnesota classification. An artificial intelligence classification model using the data extracted through SNOMED-CT showed an average accuracy of 88.03%.

CONCLUSIONS

The KURIAS-ECG database contains standardized ECG data extracted from various machines. The proposed protocol should promote cardiovascular disease research using big data and artificial intelligence.

Controlling polymorphism in molecular cocrystals by variable temperature ball milling

Mechanochemistry offers a unique opportunity to modify and manipulate crystal forms, often providing new products as compared with conventional solution methods. While promising, there is little known about how to control the solid form through mechanochemical means, demanding dedicated investigations. Using a model organic cocrystal system (isonicotinamide:glutaric acid), we here demonstrate that with mechanochemistry, polymorphism can be induced in molecular solids under conditions seemingly different to their conventional thermodynamic (thermal) transition point. Whereas Form II converts to Form I upon heating to 363 K, the same transition can be initiated under ball milling conditions at markedly lower temperatures (348 K). Our results indicate that mechanochemical techniques can help to reduce the energy barriers to solid form transitions, offering new insights into controlling polymorphic forms. Moreover, our results suggest that the nature of mechanochemical transformations could make it difficult to interpret mechanochemical solid form landscapes using conventional equilibrium-based tools.

Quantitative Measurements of DP in Cellulose Paper Based on Terahertz Spectroscopy

The power transformer is vital to the reliability of the power grid which is most commonly insulated with Kraft paper and immersed in mineral oil, among which the aged state of the paper is mainly correlated to the operating life of the transformer. Degree of polymerization (DP) is a direct parameter to assess the aged condition of insulating paper, but existing DP measurement by viscosity methods are destructive and complicated. In this paper, terahertz time-domain spectroscopy (THz-TDS) was introduced to reach rapid, non-destructive detection of the DP of insulating paper. The absorption spectra of insulating paper show that characteristic peak regions at 1.8 and 2.23 THz both exhibit a log-linear quantitative relationship with DP, and their universalities are confirmed by conducting the above relationship on different types of insulating paper. Fourier transform infrared spectroscopy (FTIR) analysis and molecular dynamics modeling further revealed that 1.8 and 2.23 THz were favorably associated with the growth of water-cellulose hydrogen bond strength and amorphous cellulose, respectively. This paper demonstrates the viability of applying THz-TDS to the non-destructive detection of DP in insulating paper and assigned the vibration modes of the characteristic absorption peaks.

Selective deuteration along a polyethylene chain: Differentiating conformation segment by segment

To improve the circularity and performance of polyolefin materials, recent innovations have enabled the synthesis of polyolefins with new structural features such as cleavable breakpoints, functional chain ends, and unique comonomers. As new polyolefin structures become synthetically accessible, fundamental understanding of the effects of structural features on polymer (re)processing and mechanical performance is increasingly important. While bulk material properties are readily measured through conventional thermal or mechanical techniques, selective measurement of local material properties near structural defects is a major characterization challenge. Here, we synthesized a series of polyethylenes with selectively deuterated segments using a polyhomologation approach and employed vibrational spectroscopy to evaluate crystallization and melting of chain segments near features of interest (e.g., end groups, chain centers, and mid-chain structural defects). Chain-end functionality and defects were observed to strongly influence crystallinity of adjacent deuterated chain segments. Additionally, chain-end crystallinity was observed to have different molar mass dependence than mid-chain crystallinity. The synthesis and spectroscopy techniques demonstrated here can be applied to range of previously inaccessible deuterated polyethylene structures to provide direct insight into local crystallization behavior.

Deep-learning framework for fully-automated recognition of TiO2 polymorphs based on Raman spectroscopy

Emerging machine learning techniques can be applied to Raman spectroscopy measurements for the identification of minerals. In this project, we describe a deep learning-based solution for automatic identification of complex polymorph structures from their Raman signatures. We propose a new framework using Convolutional Neural Networks and Long Short-Term Memory networks for compound identification. We train and evaluate our model using the publicly-available RRUFF spectral database. For model validation purposes, we synthesized and identified different TiO₂ polymorphs to evaluate the performance and accuracy of the proposed framework. TiO₂ is a ubiquitous material playing a crucial role in many industrial applications. Its unique properties are currently used advantageously in several research and industrial fields including energy storage, surface modifications, optical elements, electrical insulation to microelectronic devices such as logic gates and memristors. The results show that our model correctly identifies pure Anatase and Rutile with a high degree of confidence. Moreover, it can also identify defect-rich Anatase and modified Rutile based on their modified Raman Spectra. The model can also correctly identify the key component, Anatase, from the P25 Degussa TiO₂. Based on the initial results, we firmly believe that implementing this model for automatically detecting complex polymorph structures will significantly increase the throughput, while dramatically reducing costs.

Crystallization Kinetics in an Immiscible Polyolefin Blend

Motivated by the problem of brittle mechanical behavior in recycled blends of high density polyethylene (HDPE) and isotactic polypropylene (iPP), we employ optical microscopy, rheo-Raman, and differential scanning calorimetry (DSC) to measure the composition dependence of their crystallization kinetics. Raman spectra are analyzed via multivariate curve resolution with alternating least-squares (MCR-ALS) to provide component crystallization values. We find that iPP crystallization behavior varies strongly with blend composition. Optical microscopy shows that three crystallization kinetic regimes correspond to three underlying two-phase morphologies: HDPE droplets in iPP, the inverse, and cocontinuous structures. In the HDPE droplet regime, iPP crystallization temperature decreases sharply with increasing HDPE composition. For cocontinuous morphologies, iPP crystallization is delayed, but the onset temperature changes little with the exact blend composition. In the iPP droplet regime, the two components crystallize nearly concurrently. Rheological measurements are consistent with these observations. DSC indicates that the enthalpy of crystallization of the blends is less than the weighted values of the individual components, providing a possible clue for the decreased iPP crystallization temperatures.

Machine Learning-Based Characterization of the Nanostructure in a Combinatorial Co-Cr-Fe-Ni Compositionally Complex Alloy Film

A novel artificial intelligence-assisted evaluation of the X-ray diffraction (XRD) peak profiles was elaborated for the characterization of the nanocrystallite microstructure in a combinatorial Co-Cr-Fe-Ni compositionally complex alloy (CCA) film. The layer was produced by a multiple beam sputtering physical vapor deposition (PVD) technique on a Si single crystal substrate with the diameter of about 10 cm. This new processing technique is able to produce combinatorial CCA films where the elemental concentrations vary in a wide range on the disk surface. The most important benefit of the combinatorial sample is that it can be used for the study of the correlation between the chemical composition and the microstructure on a single specimen. The microstructure can be characterized quickly in many points on the disk surface using synchrotron XRD. However, the evaluation of the diffraction patterns for the crystallite size and the density of lattice defects (e.g., dislocations and twin faults) using X-ray line profile analysis (XLPA) is not possible in a reasonable amount of time due to the large number (hundreds) of XRD patterns. In the present study, a machine learning-based X-ray line profile analysis (ML-XLPA) was developed and tested on the combinatorial Co-Cr-Fe-Ni film. The new method is able to produce maps of the characteristic parameters of the nanostructure (crystallite size, defect densities) on the disk surface very quickly. Since the novel technique was developed and tested only for face-centered cubic (FCC) structures, additional work is required for the extension of its applicability to other materials. Nevertheless, to the knowledge of the authors, this is the first ML-XLPA evaluation method in the literature, which can pave the way for further development of this methodology.