Recent progresses in two-dimensional correlation spectroscopy (2D-COS)

Molecular spectra and docking simulations investigated the binding mechanisms of tetracycline onto E. coli extracellular polymeric substances

Extracellular polymeric substances (EPS), which were an important fraction of natural organic matter (NOM), played an important role in various environmental processes. However, the heterogeneity, complexity, and dynamics of EPS make their interactions with antibiotics elusive. Using advanced multispectral technology, this study examined how EPS interacts with different concentrations of tetracycline (TC) in the soil system. Our results demonstrated that protein-like (C1), fulvic-like (C2), and humic-like (C3) fractions were identified from EPS. Two-dimensional synchronous correlation spectroscopy (2D-SF-COS) indicated that the protein-like fraction gave faster responses than the fulvic-like fraction during the TC binding process. The sequence of structural changes in EPS due to TC binding was revealed by two-dimensional Fourier Transformation Infrared correlation spectroscopy (2D-FTIR-COS) as follows: 1550 > 1660 > 1395 > 1240 > 1087 cm-1. It is noteworthy that the sensitivity of the amide group to TC has been preserved, with its intensity gradually increasing to become the primary binding site for TC. The integration of hetero-2DCOS maps with moving window 2D correlation spectroscopy (MW2DCOS) provided a unique insight into understanding the correlation between EPS fractions and functional groups during the TC binding process. Moreover, molecular docking (MD) discovered that the extracellular proteins would provide plenty of binding sites with TC through salt bridges, hydrogen bonds, and π-π base-stacking forces. With these results, systematic investigations of the dynamic changes in EPS components under different concentrations of antibiotic exposure demonstrated the advanced capabilities of multispectral technology in examining intricate interactions with EPS in the soil environment.

Novel Developments and Progress in Two-Dimensional Correlation Spectroscopy (2D-COS)

This first of the two-part series of the comprehensive survey review on the progress of the two-dimensional correlation spectroscopy (2D-COS) field during the period 2021-2022, covers books, reviews, tutorials, novel concepts and theories, and patent applications that appeared in the last two years, as well as some inappropriate use or citations of 2D-COS. The overall trend clearly shows that 2D-COS is continually growing and evolving with notable new developments. The technique is well recognized as a powerful analytical tool that provides deep insights into systems in many science fields.

Diverse Applications of Two-Dimensional Correlation Spectroscopy (2D-COS)

This second of the two-part series of a comprehensive survey review provides the diverse applications of two-dimensional correlation spectroscopy (2D-COS) covering different probes, perturbations, and systems in the last two years. Infrared spectroscopy has maintained its top popularity in 2D-COS over the past two years. Fluorescence spectroscopy is the second most frequently used analytical method, which has been heavily applied to the analysis of heavy metal binding, environmental, and solution systems. Various other analytical methods including laser-induced breakdown spectroscopy, dynamic mechanical analysis, differential scanning calorimetry, capillary electrophoresis, seismologic, and so on, have also been reported. In the last two years, concentration, composition, and pH are the main effects of perturbation used in the 2D-COS fields, as well as temperature. Environmental science is especially heavily studied using 2D-COS. This comprehensive survey review shows that 2D-COS undergoes continuous evolution and growth, marked by novel developments and successful applications across diverse scientific fields.

Nondestructive identification and classification of starch types based on multispectral techniques coupled with chemometrics

Starch is the main source of energy and nutrition. Therefore, some merchants often illegally add cheaper starches to other types of starches or package cheaper starches as higher priced starches to raise the price. In this study, 159 samples of commercially available wheat starch, potato starch, corn starch and sweet potato starch were selected for the identification and classification based on multispectral techniques, including near-infrared (NIR), mid-infrared (MIR) and Raman spectroscopy combined with chemometrics, including pretreatment methods, characteristic wavelength selection methods and classification algorithms. The results indicate that all three spectral techniques can be used to discriminate starch types. The Raman spectroscopy demonstrated superior performance compared to that of NIR and MIR spectroscopy. The accuracy of the models after characteristic wavelength selection is generally superior to that of the full spectrum, and two-dimensional correlation spectroscopy (2D-COS) achieves better model performance than other wavelength selection methods. Among the four classification methods, convolutional neural network (CNN) exhibited the best prediction performance, achieving accuracies of 99.74 %, 97.57 % and 98.65 % in NIR, MIR and Raman spectra, respectively, without pretreatment or characteristic wavelength selection.

Deciphering DOM-metal binding using EEM-PARAFAC: Mechanisms, challenges, and perspectives

Dissolved organic matter (DOM) is a pivotal component of the biogeochemical cycles and can combine with metal ions through chelation or complexation. Understanding this process is crucial for tracing metal solubility, mobility, and bioavailability. Fluorescence excitation emission matrix (EEM) and parallel factor analysis (PARAFAC) has emerged as a popular tool in deciphering DOM-metal interactions. In this review, we primarily discuss the advantages of EEM-PARAFAC compared with other algorithms and its main limitations in studying DOM-metal binding, including restrictions in spectral considerations, mathematical assumptions, and experimental procedures, as well as how to overcome these constraints and shortcomings. We summarize the principles of EEM to uncover DOM-metal association, including why fluorescence gets quenched and some potential mechanisms that affect the accuracy of fluorescence quenching. Lastly, we review some significant and innovative research, including the application of 2D-COS in DOM-metal binding analysis, hoping to provide a fresh perspective for possible future hotspots of study. We argue the expansion of EEM applications to a broader range of areas related to natural organic matter. This extension would facilitate our exploration of the mobility and fate of metals in the environment.

In-Loco Optical Spectroscopy through a Multiple Digital Lock-In on a Linear Charge-Coupled Device (CCD) Array

Accurate and reliable measurements of optical properties are crucial for a wide range of industrial and commercial applications. However, external illumination fluctuations can often make these measurements challenging to obtain. This work proposes a new technique based on digital lock-in processing that enables the use of CCD spectrometers in optical spectroscopy applications, even in uncontrolled lighting conditions. This approach leverages digital lock-in processing, performed on each pixel of the spectrometer's CCD simultaneously, to mitigate the impact of external optical interferences. The effectiveness of this method is demonstrated by testing and recovering the spectrum of a yellow LED subjected to other light sources in outdoor conditions, corresponding to a Signal-to-Noise Ratio of -70.45 dB. Additionally, it was possible to demonstrate the method's applicability for the spectroscopic analysis of gold nanoparticles in outdoor conditions. These results suggest that the proposed technique can be helpful for a wide range of optical measurement techniques, even in challenging lighting conditions.

Rapid Vector-Based Peak Fitting and Resolution Enhancement for Correlation Analyses of Raman Hyperspectra

Spectroscopic peak parameters are important since they provide information about the analyte under study. Besides obtaining these parameters, peak fitting also resolves overlapped peaks. Thus, the obtained parameters should permit the construction of a higher-resolution version of the original spectrum. However, peak fitting is not an easy task due to computational reasons and because the true nature of the analyte is often unknown. These difficulties are major impediments when large hyperspectral data sets need to be processed rapidly, such as for manufacturing process control. We have developed a novel and relatively fast two-part algorithm to perform peak fitting and resolution enhancement on such data sets. In the first part of the algorithm, estimates of the total number of bands and their parameters were obtained from a representative spectrum in the data set, using a combination of techniques. Starting with these parameter estimates, all the spectra were then iteratively and rapidly fitted with Gaussian bands, exploiting intrinsic features of the Gaussian distribution with vector operations. The best fits for each spectrum were retained. By reducing the obtained bandwidths and commensurately increasing their amplitudes, high-resolution spectra were constructed that greatly improved correlation-based analyses. We tested the performance of the algorithm on synthetic spectra to confirm that this method could recover the ground truth correlations between highly overlapped peaks. To assess effective peak resolution, the method was applied to low-resolution spectra of glucose and compared to results from high-resolution spectra. We then processed a larger spectral data set from mammalian cells, fixed with methanol or air drying, to demonstrate the resolution enhancement of the algorithm on complex spectra and the effects of resolution-enhanced spectra on two-dimensional correlation spectroscopy and principal component analyses. The results indicated that the algorithm would allow users to obtain high-resolution spectra relatively fast and permit the recovery of important aspects of the data's intrinsic correlation structure.

Two-Dimensional Clustering of Spectral Changes for the Interpretation of Raman Hyperspectra

Two-dimensional correlation spectroscopy (2D-COS) is a technique that permits the examination of synchronous and asynchronous changes present in hyperspectral data. It produces two-dimensional correlation coefficient maps that represent the mutually correlated changes occurring at all Raman wavenumbers during an implemented perturbation. To focus our analysis on clusters of wavenumbers that tend to change together, we apply a k-means clustering to the wavenumber profiles in the perturbation domain decomposition of the two-dimensional correlation coefficient map. These profiles (or trends) reflect peak intensity changes as a function of the perturbation. We then plot the co-occurrences of cluster members two-dimensionally in a manner analogous to a two-dimensional correlation coefficient map. Because wavenumber profiles are clustered based on their similarity, two-dimensional cluster member spectra reveal which Raman peaks change in a similar manner, rather than how much they are correlated. Furthermore, clustering produces a discrete partitioning of the wavenumbers, thus a two-dimensional cluster member spectrum exhibits a discrete presentation of related Raman peaks as opposed to the more continuous representations in a two-dimensional correlation coefficient map. We demonstrate first the basic principles of the technique with the aid of synthetic data. We then apply it to Raman spectra obtained from a polystyrene perchlorate model system followed by Raman spectra from mammalian cells fixed with different percentages of methanol. Both data sets were designed to produce differential changes in sample components. In both cases, all the peaks pertaining to a given component should then change in a similar manner. We observed that component-based profile clustering did occur for polystyrene and perchlorate in the model system and lipids, nucleic acids, and proteins in the mammalian cell example. This confirmed that the method can translate to "real world" samples. We contrast these results with two-dimensional correlation spectroscopy results. To supplement interpretation, we present the cluster-segmented mean spectrum of the hyperspectral data. Overall, this technique is expected to be a valuable adjunct to two-dimensional correlation spectroscopy to further facilitate hyperspectral data interpretation and analysis.

Molecular size-dependent compositions and lead (II) binding behaviors of two origins of organic fertilizers-derived dissolved organic matter

The application of organic fertilizers caused large amounts of dissolved organic matter (DOM) entering the soil environment and influencing the behaviors and fates of heavy metals. Here, we investigated the molecular weight-dependent (high molecular weight [HMW], 1 kDa-0.7 µm; low molecular weight [LMW], <1 kDa) compositions and lead (Pb) binding behaviors of DOM derived from sheep manure-based (SMOF) and shrimp peptide-based organic fertilizers (SPOF) using chromophoric and fluorescent spectroscopy, Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) and two-dimensional correlation spectroscopy (2D-COS). Results showed that SMOF released more DOM with higher aromaticity and hydrophobicity, containing more fluvic-like components, carboxylic-rich alicyclic molecules (CRAMs) and lignin phenolic compounds compared to SPOF-DOM with more microbially-transformed heteroatom-containing compounds (CHON, CHONS and CHOS). Furthermore, there was more aromatic compounds with ample carboxyl and hydroxyl groups in HMW-DOM but abundant protein-like components and heteroatom-containing compounds (CHONS and CHOS) in LMW-DOM. SMOF-DOM exhibited more obvious MW-dependent heterogeneity in molecular components compared to SPOF-DOM with higher molecular diversity. Moreover, 2D-COS indicated phenol and carboxyl groups in SMOF-DOM and polysaccharides in SPOF-DOM exhibited superior binding affinities for Pb. Pb binding to HMW-DOM derived from SMOF first occurred in the phenolic groups in fulvic-like substances, while polysaccharides in LMW-DOM first participated in the binding of Pb. In contrast, irrespective of MWs, polysaccharides and humic-like substances with aromatic (CC) groups in SPOF-DOM displayed a faster response to Pb. Furthermore, the polysaccharides which preferentially participated in the binding of Pb to SPOF-DOM and SMOF-derived LMW-DOM may pose a higher risk of Pb in the environment. These results were helpful to understand the effects of sources and size-dependent compositions of DOM on the associated risks of heavy metals in the environments.

Comprehensive modified approaches to reducing the interference of moisture from an FTIR spectrum and the corresponding second derivative spectrum

We proposed a modified and improved approach to removing the interference of moisture from an IR spectrum and the corresponding second derivative spectrum. The temperature fluctuation in the air of the optical path and baseline-drift lead to the small but persistent presence of the interference of moisture. The problem has been successfully addressed by adopting a double-matching strategy. Additionally, two-dimensional correlationspectra (2D-COS) are generated using the second derivative or third derivative spectrum of the negative base 10 logarithms of the single-beam spectra, thereby removing the linear slope or quadratic portion of baseline-drift. Using the newly adopted approach, the residual interferences of moisture are attenuated. We applied the new approach to the IR spectra and the second derivative spectra of neat hexadecanol and biaxially oriented polypropylene (BOPP) film, and some promising preliminary results are obtained. In hexadecanol, two highly overlapping peaks at 1464 and 1463 cm^-1 are revealed. In BOPP, the envelope at 1458 cm^-1 is found to be composed of a number of sub-peaks.

Binding characteristics of Pb and Zn to low-temperature feces-based biochar-derived DOM revealed by EEM-PARAFAC combined with general and moving-window two-dimensional correlation analyses

Pyrolysis carbonization of human feces has shown potential for converting feces biomass into a soil amendment. However, little is known about the interactions of DOM derived from feces-based biochar produced at low-temperature with heavy metals (HMs). In this study, the binding properties of Pb(II) and Zn(II) with DOM derived from feces-based biochar produced at low pyrolysis temperatures were investigated using EEM-PARAFAC combined with general, and moving-window two-dimensional correlation analyses (2D-COS). The results revealed that DOM from biochar produced at 280 °C exhibited a higher Pb(II) and Zn(II) affinity and more binding sites than DOM produced at 380 °C. The fulvic-like and humic-like components exhibited obvious fluorescence quenching after the heavy metal addition, and the complexes formed with Pb(II) and Zn(II) were more stable. C-H groups exhibited the fastest response to Pb(II) and Zn(II) binding in the FB280 DOM, while the COO- groups of carboxylic acids in the FB380 DOM exhibited the fastest response to Pb(II) and Zn(II). Moreover, the mutation concentration range of components and functional groups in DOM, as analyzed by MW2D-COS, was greater for Zn(II) than for Pb(II). These results provide a more detailed molecular-level understanding of the interaction mechanisms between heavy metals and feces-based biochar-derived DOM and the effect of HM concentration on DOM binding. Further, these results will help to provide a reasonable reference for feces management and feces-based biochar in controlling soil HMs.

Continuing progress in the field of two-dimensional correlation spectroscopy (2D-COS): Part III. Versatile applications

In this review, the comprehensive summary of two-dimensional correlation spectroscopy (2D-COS) for the last two years is covered. The remarkable applications of 2D-COS in diverse fields using many types of probes and perturbations for the last two years are highlighted. IR spectroscopy is still the most popular probe in 2D-COS during the last two years. Applications in fluorescence and Raman spectroscopy are also very popularly used. In the external perturbations applied in 2D-COS, variations in concentration, pH, and relative compositions are dramatically increased during the last two years. Temperature is still the most used effect, but it is slightly decreased compared to two years ago. 2D-COS has been applied to diverse systems, such as environments, natural products, polymers, food, proteins and peptides, solutions, mixtures, nano materials, pharmaceuticals, and others. Especially, biological and environmental applications have significantly emerged. This survey review paper shows that 2D-COS is an actively evolving and expanding field.

Continuing progress in the field of two-dimensional correlation spectroscopy (2D-COS), part I. Yesterday and today

This comprehensive survey review, as the first of three parts, compiles past developments and early concepts of two-dimensional correlation spectroscopy (2D-COS) and subsequent evolution, as well as its early applications in various fields for the last 35 years. It covers past review articles, books, proceedings, and numerous research papers published on 2D-COS. 2D-COS continues to evolve and grow with new significant developments and versatile applications in diverse scientific fields. The healthy, vigorous, and diverse progress of 2D-COS studies in many fields confirms that it is well accepted as a powerful analytical technique to provide the in-depth understanding of systems of interest.

Two-trace two-dimensional (2T2D) correlation applied to a number of spectra beyond a simple pair

The application of two-trace two-dimensional (2T2D) correlation analysis to a number of spectra consisting of more than a simple pair is explored, especially when such spectra are randomly collected without knowing the sampling order. Calculation and interpretation of 2T2D correlation spectra are briefly reviewed, and a systematic procedure to identify the set of characteristic bands, which are mutually asynchronous and least overlapped with each other, is described. 2T2D correlation is applied to individual spectra by selecting a representative reference spectrum, such as the average of the whole dataset. A slice of an asynchronous 2T2D spectrum at a characteristic band is devoid of the spectral contribution from the species represented by the band. Since 2T2D analysis may be applied to the whole set of spectra, and each 2T2D asynchronous spectrum yields a set of slices for different characteristic bands, it is possible to generate a series of 2T2D slices obtained at a given characteristic band. By applying the generalized 2D correlation or a successive 2T2D analysis to such slices, one can obtain excellent estimates of the pure component spectra of the mixture, which are comparable to the results from other curve resolution techniques.

Effects of montmorillonite (MMT) on the crystallization behavior of poly(L-lactic acid) (PLLA) by variable-temperature FTIR coupled with difference spectrometry, PCMW2D and 2DCOS analyses

Effects of montmorillonite (MMT) on the crystallization behavior of poly(L-lactic acid) (PLLA) were investigated by variable-temperature FTIR spectroscopy. The variations of carbonyl band (1800-1720 cm^-1) of different PLLA/MMT nanocomposites were focused due to its strong intensity and the involved abundant structure information. Difference spectrometry was used to evaluate the structural variations of PLLA after introducing MMT, and perturbation correlation moving window two-dimensional analysis (PCMW2D) split the temperature range into two sub-regions, i.e., 32-116 ℃ and 116-152 ℃, on the basis of the spectral variation. Two-dimensional correlation spectroscopy (2DCOS) was further applied to such sub-regions in order to find the change order between varied PLLA polymorphs. The results showed that less addition of MMT (≤3%) would lead to a well-exfoliated structure, which not only had no nucleation effect for PLLA, but also delayed the cold crystallization to a higher temperature compared with the one of pure PLLA. However, a higher addition of MMT (≥5%) would lead to an intercalated structure, which acted as a nucleating agent and thus advanced the cold crystallization to a lower temperature. Nevertheless, the introduction of MMT cannot affect the phase transition order between the amorphous, the intermediate, the α'- and the α'-PLLAs based on 2DCOS results.

Estimating more than two pure component spectra from only two mixture spectra using two-dimensional correlation

A procedure is described to estimate the pure component spectra of mixtures from only a pair of available spectra even when there are more than two component species present in the system. In contrast, traditional multivariate curve resolution (MCR) technique cannot be used for such a case. The method relies on the use of two-trace two-dimensional (2T2D) correlation spectroscopy. Asynchronous 2T2D spectrum is used to identify the characteristic bands most strongly associated with the individual mixture component species. Correlation coefficients derived from the synchronous 2T2D spectrum are used to obtain a set of correlative filtering functions to distribute the spectral intensity of the average spectrum among the estimates of the pure component spectra. Efficacy of the method was demonstrated using a pair of ATR IR spectra obtained for two solution mixtures containing three main ingredients with very similar compositions. Relatively congested and overlapped spectral region was used first for the demonstration, and reasonable resolution was accomplished yielding a set of the estimates of pure component spectra with most of the expected pertinent features included. The analysis was then extended to a broader spectral region containing well-isolated spectral signatures of individual components for positive validation. While traditional MCR technique seems to perform better with a large number of spectra, this technique can be effectively used in conjunction with MCR to improve its stability and performance, especially under some challenging conditions.

Binding characteristics of copper onto biochar-derived DOM using general, heterospectral and moving-window two-dimensional correlation analyses

Dissolved organic matter released from biochar (biochar-derived DOM, BDOM) could dominate the environmental behavior and fate of trace metals by forming BDOM-metal complexes. Here general, heterospectral as well as moving-window (MW) two-dimensional correlation spectroscopy (2DCOS) analyses of synchronous fluorescence and Fourier transform infrared spectra were employed to explore the heterogeneous binding characteristics between sludge BDOM and Cu(II). The results revealed that Cu-BDOM binding first occurred in the fulvic-like (368-380 nm), then humic-like (428 nm) fluorescent fractions, followed by infrared groups of phenolic hydroxyl groups, carboxylate, COH of polysaccharide groups, CC of aromatic carbon, CH of aliphatics and COC of aliphatic ethers. The binding affinity of the hydrophilic groups was stronger than that of hydrophobic groups in BDOM towards Cu(II). Fluorescence components in BDOM played a decisive role in the binding of Cu(II) with trace concentration (1 μM), while infrared functional groups made a substantial contribution in the complexation of Cu(II) with higher concentration (10-100 μM). The concentration of final configuration transformation point (11.7 μmol/mg in this study) by MW2DCOS analysis was suggested as an actual binding threshold that was helpful for evaluating their environmental behaviors.

Process monitoring of photocatalytic degradation of 2,4-dinitrotoluene by Au-decorated Fe3O4@TiO2 nanoparticles: surface-enhanced Raman scattering method

Recently, the degradation and detection of 2,4-dinitrotoluene (2,4-DNT) capable of producing 2,4,6-trinitrotoluene (TNT) for environmental and human health risks have been developed. We prepared photoresponsive Au-decorated Fe₃O₄@TiO₂ nanoparticles (Fe₃O₄@TiO₂-Au NPs) under sunlight simulated Xe lamp irradiation. The photodegradation process of 2,4-DNT by Fe₃O₄@TiO₂-Au NPs was successfully monitored by surface-enhanced Raman scattering (SERS). Since SERS monitoring shows intrinsic information about the molecular structure, it was possible to predict the photodegradation of 2,4-DNT. The 2,4-DNT photodegradation mechanism based on two-dimensional correlation spectroscopy (2D-COS), which provides very beneficial information for a deeper understanding of systems, has been identified. We confirmed that Fe₃O₄@TiO₂-Au NPs can be widely used in organic pollutant degradation under sunlight. Furthermore, the combination of SERS based process monitoring and 2D-COS can be a convincing analytical technique for photodegradation studies of organic pollutants.

Sorption of Pb(II) onto biochar is enhanced through co-sorption of dissolved organic matter

Biochar plays an important role in controlling migration of pollutants in soils. However, little information is available on the interactions between soil-derived dissolved organic matter (DOM), biochar and soluble metal species. The aim of this work was to present the adsorption process of soil DOM by biochar (corn straw biochar produced at 700 °C) and to determine whether co-sorption of DOM would change the affinity for Pb(II). The adsorption rates of biochar and biochar + DOM for Pb(II) were best fitted with a pseudo-second order kinetic model, and the equilibrium adsorption isotherm data agreed well with both the Langmuir and Freundlich models. Adsorption of DOM to biochar reached equilibrium after 15 h with an uptake of 52% of the supplied DOM. We used fluorescence excitation-emission matrices (EEMs) with parallel factor (PARAFAC) analysis to demonstrate that protein-like, fulvic acid-like and humic acid-like substances were the primary constituents of the DOM, which were quenched over time in the presence of biochar. Synchronous fluorescence spectra indicated that the protein-like structures were the predominant fluorescence substances in DOM. Two-dimensional correlation spectroscopy (2D-COS) showed the binding of DOM to biochar resulted in the quenching of fluorescence in the order: protein-like substances > humic-like substances (280 > 355 nm). Data supports the notion that DOM can increase the adsorption capacity of biochar for metal-ions.

Tracking structural changes of protein residues by two-dimensional correlation surface-enhanced Raman spectroscopy

In-situ tracking structural changes of protein residues was developed by two-dimensional correlation surface-enhanced Raman spectroscopy (2DC-SERS). The change order of SERS fingerprints during artificial nitrification of edible bird's nest (EBN) was interpreted as the structural changes of amino acid residues. It inherently realizes reliable recognition of natural EBN and artificially dyed fakes. Both this direct structural tracking of protein residues and the indirect azo dye testing of nitrites/nitrosamines could be used as indicators for discriminating different EBN before and after the artificial dyeing. Limit of detection (LOD) for nitrite and NDMA is about 40.6 ppb and 88.1 ppb, respectively. A conceptual logical circuit of the OR gate was constructed by considering the protein structural indicator (INPUT1) and the nitrite indicator (INPUT2) as two independent inputs for automatic recognition of different EBN samples. A data-driven analog soft independent modeling (DD-SIMCA) model could quickly distinguish normal EBN from A-EBN with 98% specificity.

A new approach to removing interference of moisture from FTIR spectrum

An approach is developed to remove the interference of moisture from FTIR spectra. The interference arises from two aspects: the fluctuation on the temperature of the HeNe laser and the fluctuation on the transient concentration of moisture in the light - path of an FTIR spectrometer. The temperature fluctuation on the HeNe laser produces a systematic spectral shift between single-beam sample and background spectra, which often makes spectral subtraction method invalid in removing the interference of moisture. Herein, the Carbo similarity metric (the C_AB value) is used to reflect the subtle spectral shift. A database of single-beam background spectra is established based on the concept of big-data and the pigeon-hole theory. The spectral shift is corrected by selecting suitable single-beam background spectra from the database to match with the given single-beam sample spectrum according to the C_AB value. The interference caused by the fluctuation of the transient concentration of moisture is removed using a comprehensive 2D-COS method. We apply the approach on two polymeric samples to retrieve high-quality spectra and reliable second derivative spectra without the interference of moisture. The present work provides a new opportunity of obtaining the reliable second derivative spectra in the spectral region masked by moisture.

Critical Evaluation of Spectral Resolution Enhancement Methods for Raman Hyperspectra

Overlapping peaks in Raman spectra complicate the presentation, interpretation, and analyses of complex samples. This is particularly problematic for methods dependent on sparsity such as multivariate curve resolution and other spectral demixing as well as for two-dimensional correlation spectroscopy (2D-COS), multisource correlation analysis, and principal component analysis. Though software-based resolution enhancement methods can be used to counter such problems, their performances often differ, thereby rendering some more suitable than others for specific tasks. Furthermore, there is a need for automated methods to apply to large numbers of varied hyperspectral data sets containing multiple overlapping peaks, and thus methods ideally suitable for diverse tasks. To investigate these issues, we implemented three novel resolution enhancement methods based on pseudospectra, over-deconvolution, and peak fitting to evaluate them along with three extant methods: node narrowing, blind deconvolution, and the general-purpose peak fitting program Fityk. We first applied the methods to varied synthetic spectra, each consisting of nine overlapping Voigt profile peaks. Improved spectral resolution was evaluated based on several criteria including the separation of overlapping peaks and the preservation of true peak intensities in resolution-enhanced spectra. We then investigated the efficacy of these methods to improve the resolution of measured Raman spectra. High resolution spectra of glucose acquired with a narrow spectrometer slit were compared to ones using a wide slit that degraded the spectral resolution. We also determined the effects of the different resolution enhancement methods on 2D-COS and on chemical contrast image generation from mammalian cell spectra. We conclude with a discussion of the particular benefits, drawbacks, and potential of these methods. Our efforts provided insight into the need for effective resolution enhancement approaches, the feasibility of these methods for automation, the nature of the problems currently limiting their use, and in particular those aspects that need improvement.

Tracking the Micro-Heterogeneity and Hydrogen-Bonding Interactions in Hydroxyl-Functionalized Ionic Liquid Solutions: A Combined Experimental and Computational Study

Ionic liquids (ILs) are an important class of media that are usually used in combination with polar solvents to reduce costs and tune their physicochemical properties. In this regard, it is essential to understand the influence of adding solvents on the properties of ILs. In this work, the micro-heterogeneity and H-bonding interactions between a hydroxyl-functionalized IL, [HOEmim][TFSI], and acetonitrile (ACN) were investigated by attenuated total reflection Fourier transform infrared spectroscopy and molecular simulations. All studied IL-ACN mixtures were found to deviate from the ideal mixtures. The degree of deviations reaches the maximum at about x(ACN)=0.7 with the presence of both homogeneous clusters of pure IL/ACN and heterogeneous clusters of IL-ACN. With the addition of ACN to IL, the mixtures undergo the transformation from "ACN solvated in [HOEmim][TFSI]" to "[HOEmim][TFSI] solvated in ACN". It is found that the newly formed H-bonding interactions between the IL and ACN is the main factor that contributes to the red shifts of O-H, C₂ -H, C_4,5 -H, and C_alkyl -H of [HOEmim]⁺ cation, and the blue shifts of C-D, C≡N of ACN, and C-F, S=O of [TFSI]^- anion. These in-depth studies on the mixtures of hydroxyl-functionalized IL and acetonitrile would help to understand the micro-heterogeneity and H-bonding interactions of miscible solutions and shed light on exploring their applications.

Degradation of polyethylene plastic in soil and effects on microbial community composition

Plastics pollution in global soil systems is becoming a severely global issue and potential threat to terrestrial ecosystem serves and human health. Herein, in order to determine the degradability and ecological effects of polyethylene (PE) films, we measured the weight loss and characterization of PE films and analyzed variation in microbial community. The results of weight loss, SEM and FTIR spectra exhibited that PE films had unique degradation performance under different conditions. Simultaneously, we investigated the effects of PE films on the microbial community, and the microbiota colonizing on plastics. PE films may change the soil microbial community composition in soil, and hold the post of unique matrix for microbial colonization. These results indicate that the degradation of PE films and microbial community composition in soil can be affected by different conditions (soil layer, time and plants). By assessing the alteration of microbial community composition and PE films in soil, this work will contribute to enhance our understanding on the potential risks of plastics on soil ecosystems and provide a scientific basis for understanding the ecological effect of plastics on soil functions.

Wide angle X-ray diffraction patterns and 2D-correlation spectroscopy of crystallization in proton irradiated poly(ether ether ketone)

Proton irradiated poly (ether ether ketone) (PEEK) films were crystallized to different extents, and subsequently characterized by wide angle X-ray diffraction technique. The data were analyzed by two-dimensional correlation mapping (2D-CM), in particular: Generalized, hybrid and multiple perturbations correlation approaches. Two asynchronous correlation peaks at (19.1, 18.7) ^o and at (22.5, 19.1) ^o were utilized as a measure the crystal perfection and the preferred process; orientation/crystal growth respectively. Proton irradiation not only favored the formation of crystal form II, but also changed the type of orientation within the irradiated films. Differential scanning calorimetry and Raman spectroscopic analysis confirmed the contribution of the previous two factors. Raman spectra indicated that the intensity of both bands at 1595 and 1608 cm⁻¹ decreased on samples crystallized from the melt, but increased on cold crystallized samples. 2D-CM combined with other suitable techniques is a promising in evaluating the structure of polymers and revealing the effect of proton irradiation.

Augmented Two-Dimensional Correlation Spectroscopy for the Joint Analysis of Correlated Changes in Spectroscopic and Disparate Sources

Here, we present an augmented form of two-dimensional correlation spectroscopy, that integrates in a single format data from spectroscopic and multiple non-spectroscopic sources for analysis. The integration is affected by augmenting every spectrum in a hyperspectral data set with relevant non-spectroscopic data to permit two-dimensional correlation analysis^(2D-COS) of the ensemble of augmented spectra. A k-means clustering is then applied to the results of the perturbation domain decomposition to determine which Raman peaks cluster with any of the non-spectroscopic data. We introduce and explain the method with the aid of synthetic spectra and synthetic non-spectroscopic data. We then demonstrate this approach with data using Raman spectra from human embryonic stem cell aggregates undergoing directed differentiation toward pancreatic endocrine cells and parallel bioassays of hormone mRNA expression and C-peptide levels in spent medium. These pancreatic endocrine cells generally contain insulin or glucagon. Insulin has disulfide bonds that produce Raman scattering near 513 cm^-1, but no tryptophan. For insulin-positive cells, we found that the application of multisource correlation analysis revealed a high correlation between insulin mRNA and Raman scattering in the disulfide region. In contrast, glucagon has no disulfide bonds but does contain tryptophan. For glucagon-positive cells, we also observed a high correlation between glucagon mRNA and tryptophan Raman scattering (∼757 cm^-1). We conclude with a discussion of methods to enhance spectral resolution and its effects on the performance of multisource correlation analysis.

Covariance NMR: Theoretical concerns, practical considerations, contemporary applications and related techniques

The family of resolution enhancement and spectral reconstruction techniques collectively known as covariance NMR continues to expand, along with the list of applications for these techniques. Recent advances in covariance NMR include the utilization of covariance to reconstruct pure shift NMR spectra, and the growing use of covariance NMR in processing non-uniformly sampled data, especially in solid state NMR and metabolomics. This review describes theoretical and practical considerations for direct and indirect covariance NMR techniques, and summarizes recent additions to the covariance NMR family. The review also outlines some of the applications of covariance NMR, and places covariance NMR in the larger context of methods that use statistical and algebraic approaches to enhance and combine various kinds of spectroscopic data, including tensor-based approaches for multidimensional NMR and heterocovariance spectroscopy.

Quantitative analysis of the surficial and adhesion properties of the Gram-negative bacterial species Comamonas testosteroni modulated by c-di-GMP

Cyclic diguanylate monophosphate (c-di-GMP) is a ubiquitous intracellular secondary messenger which governs the transition from a bacterial cell's planktonic state to biofilm formation by stimulating the production of a variety of exopolysaccharide material by the bacterial cell. A range of genes involved in c-di-GMP signaling in the Gram-negative species Comamonas testosteroni have been identified previously, yet the physical-chemical properties of the produced extracellular polymeric substances (EPS) and the bacterial adhesion characteristics regulated by c-di-GMP are not well understood. Here, we modulated the in vivo c-di-GMP levels of Comamonas testosteroni WDL7 through diguanylate cyclase (YedQ) and phosphodiesterase (YhjH) gene editing. The strains and their adhesion properties were characterized by Fourier-transform infrared and two-dimensional correlation spectroscopy analysis (FTIR-2D CoS), contact angle and zeta potential measurements, atomic force microscopy (AFM) and extended-Derjaguin-Landau-Verwey-Overbeek (ExDLVO) analysis. Our results show that high c-di-GMP levels promoted the secretion of long-chain hydrophobic and electroneutral extracellular polysaccharides and proteins. The protein molecules on WDL7/pYedQ2 promoted the bacterial self-aggregation and adhesion onto negatively charged surfaces. In contrast, the reduction of intracellular c-di-GMP concentrations resulted in a nearly 80 % decrease in the adhesion of bacterial cells, although little change in the surface hydrophobicity or surface charge properties were observed for these cells relative to the wild type. These results indicate that the reduced adsorption of WDL7/YhjH that we observed may be caused by the flagellum-accelerated mobility at low c-di-GMP concentrations. Taken together, these results improve our mechanistic understanding of the effects of c-di-GMP in controlling bacterial physical-chemical properties and initial biofilm development.

A DFT study of fulvic acid binding with bivalent metals: Cd, Cu, Mg, Ni, Pb, Zn

3,7,8-Trihydroxy-3-methyl-10-oxo-4,10-dihydro-1H,3H-pyrano [4,3-b]chromene-9-carboxylic acid is a structurally well-characterized fulvic acid (FA) capable to act as a polyfunctional bidentate ligand in the complexes with metal ions. Investigations of the formation mechanisms and structure of the above-mentioned FA complexes with bivalent metals [Cd(II), Cu(II), Mg(II), Ni(II), Pb(II) and Zn(II)] are presently an actual and trending topic in the modern chemistry of humic and fulvic acids. Furthermore, the importance of the theoretical DFT investigations of binding of metals with fulvic acids is stipulated by the lack of the relevant experimental structural data for such complexes. The quantum chemical calculations have shown that, of the four possible FA tautomers, the two FA forms are more stable. The wavefunction analysis and computed reactivity descriptors (electrostatic potential, Hirshfeld surface analysis, natural population analysis charges, and condensed Fukui indexes) give the insight on the properties and reactive ability of these two different forms of the FA. The computed thermochemical parameters of the ion-exchange reaction explain the metal binding affinity and selectivity of the FA forms.

Development of a novel quantitative function between spectral value and metmyoglobin content in Tan mutton

This study was aimed to establish a quantitative function between spectral reflectance values and metmyoglobin (MetMb) content in Tan mutton during refrigeration. Near-infrared hyperspectral data combined with generalized two-dimensional correlation spectroscopy (G2D-COS) method to identify characteristic bands and investigate the sequence of chemical waveband changes. Characteristic wavebands identified by G2D-COS analysis had the best performance in predicting the content of MetMb, with a high R²p of 0.849, a low RMSEP of 2.695 and a high RPD of 2.786. The results showed that the G2D-COS may be a powerful tool for describing intensity changes of MetMb band. The partial least square regression method was used to develop the relationships between the spectral values and MetMb content in Tan mutton meat for predicting MetMb content. This study has provided a convenient and rapid non-destructive quantitative method for assessing the color of Tan mutton meat.

A Study on Blend Ratio-dependent Far-IR and Low-frequency Raman Spectra and WAXD Patterns of Poly(3-hydroxybutyrate)/poly(4-vinylphenol) Using Homospectral and Heterospectral Two-dimensional Correlation Spectroscopy

An intensive analysis of far-infrared (far-IR), low-frequency Raman, and wide angle X-ray diffraction (WAXD) data has been performed by two-dimensional correlation spectroscopy (2D-COS) as a function of the blend ratio of poly(3-hydroxybutyrate)/poly(4-vinylphenol) (PHB/PVPh). Homospectral 2D-COS revealed that a weak band at 128 cm^-1 in the far-IR spectra appeared more clearly in the 2D correlation spectra. Heterospectral 2D-COS (far-IR/low-frequency Raman and far-IR/WAXD) provided very important results that were hardly detected in the conventional 2D-COS. A far-IR peak at 130 cm^-1 in the heterospectral 2D-COS had negative correlations with the peaks in the low-frequency Raman spectra at 81, 100, and 110 cm^-1 and WAXD profile 8.78 and 11.01°. These results indicated that those peaks have different origins; the 130 cm^-1 peak comes from the intermolecular C=O···H-O hydrogen bond between PHB and PVPh, while those for low-frequency Raman and WAXD peaks are the features of PHB crystalline structure.

Integration of Partial Least Squares Regression and Hyperspectral Data Processing for the Nondestructive Detection of the Scaling Rate of Carp (Cyprinus carpio)

The scaling rate of carp is one of the most important factors restricting the automation and intelligence level of carp processing. In order to solve the shortcomings of the commonly-used manual detection, this paper aimed to study the potential of hyperspectral technology (400-1024.7 nm) in detecting the scaling rate of carp. The whole fish body was divided into three regions (belly, back, and tail) for analysis because spectral responses are different for different regions. Different preprocessing methods, including Savitzky-Golay (SG), first derivative (FD), multivariate scattering correction (MSC), and standard normal variate (SNV) were applied for spectrum pretreatment. Then, the successive projections algorithm (SPA), regression coefficient (RC), and two-dimensional correlation spectroscopy (2D-COS) were applied for selecting characteristic wavelengths (CWs), respectively. The partial least square regression (PLSR) models for scaling rate detection using full wavelengths (FWs) and CWs were established. According to the modeling results, FD-RC-PLSR, SNV-SPA-PLSR, and SNV-RC-PLSR were determined to be the optimal models for predicting the scaling rate in the back (the coefficient of determination in calibration set (R_C²) = 96.23%, the coefficient of determination in prediction set (R_P²) = 95.55%, root mean square error by calibration (RMSEC) = 6.20%, the root mean square error by prediction (RMSEP)= 7.54%, and the relative percent deviation (RPD) = 3.98), belly (R_C² = 93.44%, R_P² = 90.81%, RMSEC = 8.05%, RMSEP = 9.13%, and RPD = 3.07) and tail (R_C² = 95.34%, R_P² = 93.71%, RMSEC = 6.66%, RMSEP = 8.37%, and RPD = 3.42) regions, respectively. It can be seen that PLSR integrated with specific pretreatment and dimension reduction methods had great potential for scaling rate detection in different carp regions. These results confirmed the possibility of using hyperspectral technology in nondestructive and convenient detection of the scaling rate of carp.

PC 2D-COS: A Principal Component Base Approach to Two-Dimensional Correlation Spectroscopy

This paper introduces the newly developed principal component powered two-dimensional (2D) correlation spectroscopy (PC 2D-COS) as an alternative approach to 2D correlation spectroscopy taking advantage of a dimensionality reduction by principal component analysis. It is shown that PC 2D-COS is equivalent to traditional 2D correlation analysis while providing a significant advantage in terms of computational complexity and memory consumption. These features allow for an easy calculation of 2D correlation spectra even for data sets with very high spectral resolution or a parallel analysis of multiple data sets of 2D correlation spectra. Along with this reduction in complexity, PC 2D-COS offers a significant noise rejection property by limiting the set of principal components used for the 2D correlation calculation. As an example for the application of truncated PC 2D-COS a temperature-dependent Raman spectroscopic data set of a fullerene-anthracene adduct is examined. It is demonstrated that a large reduction in computational cost is possible without loss of relevant information, even for complex real world data sets.

Isothermal crystalline polymorphs of poly(l-lactic acid) by FTIR coupled with two-dimensional correlation spectroscopy and perturbation-correlation moving-window two-dimensional analysis

The evolutions of the crystalline polymorphs of poly(l-lactic acid) (PLLA) at 85 °C and 145 °C were respectively studied by Fourier transform infrared (FTIR) spectroscopy coupled with two-dimensional correlation spectroscopy (2DCOS) and perturbation-correlation moving-window two-dimensional (PCMW2D) analysis in the carbonyl stretching band region (1820-1720 cm^-1). The perturbation region was divided into a few sub-regions by PCMW2D based on the spectral variations. Further 2DCOS analyses were implemented on these sub-regions. Four stages were identified for crystallization at 85 °C, in which the transformation of amorphous PLLA to α'-PLLA was found in the initial stage (0-30 min), while some of α'-PLLA was also changed to α-PLLA in the growth stage (30-150 min). For isothermal crystallization at 145 °C, the amorphous and the intermediate PLLAs were first changed to the crystalline forms in the initial period (0-30 min), then alternate changes between α-, α'- and intermediate phases occurred in the other periods with the extension of crystallization.

Detection of pesticide in water using two-dimensional fluorescence correlation spectroscopy and N-way partial least squares

To determine the concentration of carbaryl and chlorothalonil in water, the potential of two-dimensional (2D) fluorescence correlation spectra with N-way partial least squares (N-PLS) was investigated. A total of 40 mixture solutions of carbaryl and chlorothalonil were prepared and 27 of them were used to form a calibration set. The excitation-emission matrix (EEM) fluorescence spectra of all samples were measured. Under the excitation perturbation, 2D fluorescence correlation spectra of all samples were calculated and formed a 2D correlation spectral matrix. And N-PLS models for determination of carbaryl and chlorothalonil in water were built based on 2D correlation fluorescence spectral matrix and traditional EEM spectral matrix, respectively. Finally, the performances of N-PLS models using both methods were compared. For chlorothalonil, the root mean square error of calibration (RMSEC) were 3.43 × 10^-6 g L^-1 and 5.08 × 10^-6 g L^-1, the root mean square errors of prediction (RMSEP) were 5.86 × 10^-6 g L^-1 and 8.99 × 10^-6 g L^-1 for 2D correlation spectra and EEM spectra, respectively. For carbaryl, the RMSEC were 5.24 × 10^-7 g L^-1 and 6.18 × 10^-7, the RMSEP were 9.20 × 10^-7 g L^-1 and 9.63 × 10^-7 g L^-1 for 2D correlation spectra and EEM spectra, respectively. From the results of this study, it may conclude that 2D fluorescence correlation spectra is superior over the traditional EEM fluorescence spectra in terms of predictability and can be used as an alternative method for detection of organic pollutants in environment.

Two-dimensional correlation analysis of highly spatially resolved simultaneous IR and Raman spectral imaging of bioplastics composite using optical photothermal Infrared and Raman spectroscopy

Optical photothermal infrared (O-PTIR) and Raman spectroscopy and imaging was used to explore the spatial distributions of molecular constituents of a laminate sample consisting of the bioplastics, polyhydroxyalkanoate (PHA) and polylactic acid (PLA), near the interfacial boundary. Highly spatially resolved simultaneous IR and Raman spectra were sequentially collected at 100 nm increments along a line traversing the interface. The set of spectra were subjected to 2D-COS analysis to extract the detailed nature of the spatial distribution of the laminate constituents. It was revealed that the laminate is not a simple binary system of two non-interacting polymers, but consists of different constituents with more complex spatial distributions. Some portion of PLA seems to penetrate into the PHA layer. The crystallinity of PHA near the interface is reduced compared to the rest of the PHA layer. The result suggests the existence of some partial molecular mixing even for these seemingly immiscible polymer pairs. The mixing probably occurs at the segmental level confined to only several hundred nanometers of space at the interface. Such partial mixing may explain the high compatibility between the two bioplastics.

Glycerol's generalized two-dimensional correlation IR/NIR spectroscopy and its principal component analysis

In this manuscript, the fundamental vibration, the combination vibration and the first overtone vibration of the glycerol hydroxyl were studied by near-infrared and infrared spectroscopy. The composition and variation of hydrogen bond were analyzed by two-dimensional correlation spectroscopy and principal component analysis. The analysis revealed five types of hydroxyl and verified the existence of independent, intramolecular, as well as intermolecular, hydrogen bond hydroxyl. The principal component analysis showed that there were three main forms of glycerol association: the first and second principal components explained the majority of the spectral features, and the third was mainly the independent hydroxyl. The results provided insight into the structure of glycerol and illustrated the potential for using these tools in analyzing bonding in even more complex systems.

Silica Removal Using Magnetic Iron-Aluminum Hybrid Nanomaterials: Measurements, Adsorption Mechanisms, and Implications for Silica Scaling in Reverse Osmosis

Composite magnetic aluminum hydroxide at iron oxide nanomaterials, Al(OH)₃@Fe₃O₄, with a well-defined core-shell structure, were used as pretreatment adsorbents for the removal of silica in brackish water. The Al(OH)₃ outer shell confers silica adsorption capacity, and the superparamagnetic Fe₃O₄ core allows material separation and magnetic recovery. The as-prepared nanomaterials (2 g L^-1) remove ∼95 and ∼80% silica from Si-rich solutions with 0.5 and 2 mM initial silica concentrations, respectively. Regeneration under basic conditions was evaluated, and post-adsorption treatment with 0.05 M NaOH yielded optimal material reusability. After four regeneration cycles, the Al(OH)₃@Fe₃O₄ nanomaterials retain their magnetic property while still being able to remove ∼40% silica from solutions at an adsorbent concentration of 2 g L^-1. The mechanism of silica adsorption onto the surface of the nanomaterials was probed using several spectroscopic techniques. ATR-FTIR (attenuated total reflection-Fourier transform infrared) integrated with two-dimensional correlation analysis shows that silica species vary from Q² to Q⁴ with adsorption time corresponding to silica polymerization. ²⁹Si solid-state NMR spectra show an upfield chemical shift displacement of the Q² signal, which indicates the formation of Q⁴ units, suggesting silica polymerization onto the Al(OH)₃ shell. In addition, a laboratory-scale reverse osmosis setup was used to evaluate Al(OH)₃@Fe₃O₄ as pretreatment materials for silica removal. Results show that silica scaling was significantly alleviated, and water recovery was enhanced when feed waters were pretreated with the magnetic nanomaterials. Taken together, our study highlights the promise of magnetic Al(OH)₃@Fe₃O₄ nanomaterials in treating brackish water and achieving higher water recovery for inland desalination.

Chemometrics and Related Fields in Python

The Python programing language is becoming a promising tool for data analysis in various fields. However, little attention has been paid to using Python in the field of analytical chemistry, though recent advances in instrumental analysis require robust and reliable data analysis. In order to overcome the difficulty in accurate analysis, multivariate analysis, or chemometrics, has been widely applied to various kinds of data obtained by instrumental analysis. In the present work, the potential usefulness of Python for chemometrics and related fields in chemistry is reviewed. Many practical tools for chemometrics, e.g., principal component analysis (PCA), partial least squares (PLS), support vector machine (SVM), etc., are included in the scikit-learn machine learning (ML) library for Python. Other useful libraries such as pyMCR for multivariate curve resolution (MCR), 2Dpy for two-dimensional correlation spectroscopy (2D-COS), etc. can be obtained from GitHub. For these reasons, a computational environment for chemometrics is easily constructed in Python.

Characterizing Properties and Environmental Behaviors of Dissolved Organic Matter Using Two-Dimensional Correlation Spectroscopic Analysis

Dissolved organic matter (DOM) exists ubiquitously in environments and plays critical roles in pollutant mitigation, transformation, and organic geochemical cycling. Understanding its properties and environmental behaviors is critically important to develop water treatment processes and environmental remediation strategies. Generalized two-dimensional correlation spectroscopy (2DCOS), which has numerous advantages, including enhancing spectral resolution and discerning specific order of structural change under an external perturbation, could be used as a powerful tool to interpret a wide range of spectroscopic signatures relating to DOM. A suite of spectroscopic signatures, such as UV-vis, fluorescence, infrared, and Raman spectra that can be analyzed by 2DCOS, is able to provide additional structural information hiding behind the conventional one-dimensional spectra. In this article, the most recent advances in 2DCOS applications for analyzing DOM-related environmental processes are reviewed, and the state-of-the-art novel spectroscopic techniques in 2DCOS are highlighted. Furthermore, the main limitations and requirements of current approaches for exploring DOM-related environmental processes and how these limitations and drawbacks can be addressed are explored. Finally, suggestions and new approaches are proposed to significantly advance the development of 2DCOS in analyzing the properties and behaviors of DOM in natural and engineered environments.

Studies on Chemical IR Images of Poly(hydroxybutyrate⁻co⁻hydroxyhexanoate)/Poly(ethylene glycol) Blends and Two-Dimensional Correlation Spectroscopy

Biodegradable poly-[(R)-3-hydroxybutyrate-co-(R)-3-hydroxyhexanoates] (PHBHx) have been widely studied for their applications in potentially replacing petroleum-based thermoplastics. In this study, the effect of the high molecular weight (Mn = 3400) poly(ethylene glycol) (PEG) blended in the films of PHBHx with different ratios of PEG was investigated using chemical FTIR imaging. Chemical IR images and FTIR spectra measured with increasing temperature revealed that PEG plays an important role in changing the kinetics of PHBHx crystallization. In addition, two-dimensional correlation spectra clearly showed that thermal properties of PHBHx/PEG blend film changed when the blending ratio of PHBHx/PEG were 60/40 and 50/50. Consequently, PEG leads to changes in the thermal behavior of PHBHx copolymers.