Two-dimensional Correlation Spectroscopy Research Articles

Single-step propane transformation on vanadium-supported catalyst revealed by operando DRS UV–vis study

The present study investigated the influence of silica support texture on the catalytic performance of VOx/silica catalysts in the selective oxidation of propane. Two- and three-dimensional mesoporous materials, SBA-15 and MCF, were synthesized using hydrothermal methods to create different pore structures. Vanadium (approximately 5 wt%) was incorporated into the silica matrices (MCF, SBA-15) and SiO2 (used as a reference system) through impregnation. The surface composition, valence state, and reducibility of the VOx species accommodated in VOx/silica catalysts were characterized using X-ray diffraction (XRD), diffuse reflectance ultraviolet–visible spectroscopy (UV–vis DRS), temperature-programmed reduction with hydrogen (H2-TPR), X-ray photoelectron spectroscopy (XPS), and Fourier transform infrared spectroscopy (FT-IR) techniques. The catalytic properties of the catalysts were evaluated in the propane selective oxidation conducted at 470 °C, utilizing a mixture of oxidants (N2O and O2). Propane oxidation resulted in propane conversion of 34 %, propene selectivity of 49 %, and propene oxide selectivity of 20 %, corresponding to space–time yield of propene reaching 37 gC3H6 kgcat−1·h−1and propene oxide equal to 20 gPO kgcat−1h−1. The textural properties of the support materials noticeably influenced the stability of the catalysts. The vanadium catalysts supported on three-dimensional mesocellular silica foams (MCF) demonstrated superior activity, achieving turnover frequency (TOF) values of 9.0 × 10−4 s−1 for propene and 6.67 × 10−4 s−1 for propene oxide (PO). The unique three-dimensional ultra-large pores of the MCF material facilitated the generation of significant amounts of propene oxide, along with propene, while minimizing the formation of CO, CO2, and other oxygen-bearing by-products. Operando UV–vis studies supported by two-dimensional correlation analysis (2D COS) were employed to explain the diversity in the catalytic performance of VOx/silica catalysts. It has been found that the oligomeric vanadium species are the active sites in propane oxidative dehydrogenation, whereas the monomeric forms participate in propene oxide formation. Moreover, the reduction of V5+ to V4+ and even V3+ was identified as a crucial step in the reaction.A reaction scheme has been proposed based on catalytic experiments and spectroscopic insights.

A method for freshness detection of pork using two-dimensional correlation spectroscopy images combined with dual-branch deep learning

This study proposes a new visible near-infrared spectral analysis method by abandoning the traditional feature engineering-based method. It presents a dual-branch convolutional neural network (CNN) with bilinear pooling, combined with synchronous-asynchronous two-dimensional correlation spectroscopy (2D-COS) images, for quantifying pork freshness. 2D-COS images revealed spectral correlations of pork with different freshness at different bands, effectively separating overlapping bands and amplifying spectral differences. A dual-branch CNN using a bilinear pool integrates synchronous and asynchronous features to capture the interactions between features for quantitative analysis. Results demonstrate that the dual-branch CNN achieves high accuracy (R2p=0.9579 and RMSEP=0.8093 mg/100 g) for Total Volatile Basic Nitrogen (TVB-N) content prediction. The mechanism of TVB-N prediction is explained using Gradient-weighted Class Activation Mapping (Grad-CAM), demonstrating the reliability of the proposed method to replace human experience for feature extraction and modeling analysis. In conclusion, this study proposes a new approach for food inspection tasks that is convenient, efficient and human-expertise-independent.

Streamlined Multi-Attribute Assessment of an Array of Clinical-Stage Antibodies: Relationship Between Degradation and Stability.

Clinical antibodies are an important class of drugs for the treatment of both chronic and acute diseases. Their manufacturability is subject to evaluation to ensure product quality and efficacy. One critical quality attribute is deamidation, a non-enzymatic process that is observed to occur during thermal stress, at low or high pH, or a combination thereof. Deamidation may induce antibody instability and lead to aggregation, which may pose immunogenicity concerns. The introduction of a negative charge via deamidation may impact the desired therapeutic function (i) within the complementarity-determining region, potentially causing loss of efficacy; or (ii) within the fragment crystallizable region, limiting the effector function involving antibody-dependent cellular cytotoxicity. Here we describe a transformative solution that allows for a comparative assessment of deamidation and its impact on stability and aggregation. The innovative streamlined method evaluates the intact protein in its formulation conditions. This breakthrough platform technology is comprised of a quantum cascade laser microscope, a slide cell array that allows for flexibility in the design of experiments, and dedicated software. The enhanced spectral resolution is achieved using two-dimensional correlation, co-distribution, and two-trace two-dimensional correlation spectroscopies that reveal the molecular impact of deamidation. Eight re-engineered immunoglobulin G4 scaffold clinical antibodies under control and forced degradation conditions were evaluated for deamidation and aggregation. We determined the site of deamidation, the overall extent of deamidation, and where applicable, whether the deamidation event led to self-association or aggregation of the clinical antibody and the molecular events that led to the instability. The results were confirmed using orthogonal techniques for four of the samples.

Utilization of UV–Vis and synchronous fluorescence heterospectral two-dimensional correlation analysis on the structural variation of landfill leachate during membrane bioreactor-reverse osmosis process

Landfill leachate, a complex organic effluent generated during the sanitary landfilling process, poses a significant environmental threat if not properly managed. This study utilized fluorescence and UV–Vis spectroscopy in conjunction with two-dimensional correlation spectroscopy (2DCOS) to investigate the removal and transformation properties of landfill leachate during membrane bioreactor (MBR) and reverse osmosis (RO) processes. The results revealed that the MBR process effectively reduced the concentrations of chemical oxygen demand and total organic carbon, but had limited impact on complex structures and bio-refractory organic matter. Conversely, the RO process demonstrated effective trapping of organic pollutants, including both fluorescent and non-fluorescent components. Furthermore, 2DCOS analysis identified the sequence of changes in landfill leachate substances: unsaturated aldehydes and ketones > fulvic-like component/protein-like component > aromatic compounds. These findings offer valuable insights for the development of economical and efficient landfill leachate treatment procedures.

A rapid method for identification of Lanxangia tsaoko origin and fruit shape: FT-NIR combined with chemometrics and image recognition.

Lanxangia tsaoko's accurate classifications of different origins and fruit shapes are significant for research in L. tsaoko difference between origin and species as well as for variety breeding, cultivation, and market management. In this work, Fourier transform-near infrared (FT-NIR) spectroscopy was transformed into two-dimensional and three-dimensional correlation spectroscopies to further investigate the spectral characteristics of L. tsaoko. Before building the classification model, the raw FT-NIR spectra were preprocessed using multiplicative scatter correction and second derivative, whereas principal component analysis, successive projections algorithm, and competitive adaptive reweighted sampling were used for spectral feature variable extraction. Then combined with partial least squares-discriminant analysis (PLS-DA), support vector machine (SVM), decision tree, and residual network (ResNet) models for origin and fruit shape discriminated in L. tsaoko. The PLS-DA and SVM models can achieve 100% classification in origin classification, but what is difficult to avoid is the complex process of model optimization. The ResNet image recognition model classifies the origin and shape of L. tsaoko with 100% accuracy, and without the need for complex preprocessing and feature extraction, the model facilitates the realization of fast, accurate, and efficient identification.

Applying synchronous fluorescence with Gaussian band fitting and MW-2DCOS to assess removals of DOM in acrylic fiber wastewater treatment process

Acrylic fiber wastewater (AFW) with hypertoxicity and recalcitrant are treated by advanced oxidation biological techniques. Some water quality parameters as online indicators to monitor pollutant removals, fail to trace removals of organic pollutants. Hence a practicable tool is indispensable for insight into removals of organic pollutants. Synchronous fluorescence spectroscopy (SFS) combined with Gaussian band fitting and two-dimensional correlation spectroscopy (2D-COS) was developed to evaluate removals of dissolved organic matter (DOM) fractions in an AFW treatment plant. Five fluorescence components were identified from DOM by SFS with principal component analysis and Gaussian band fitting, i.e., low-molecular-weight (LMWF1 and LMWF2), microbial humic-like (MHLF) and high-molecular-weight substances (HMWF1 and HMWF2). LMWF2 and HMWF2 were the representative components (52.96%) in the AFW, whose removal efficiencies (94.05% and 73.11%) were much higher than those of LMWF1 (40.16%) and HMWF1 (17.45%). Nitriles and amides occurred in LMWF2, whereas the aromatics in four DOM fractions. Based on 2D-COS, the changing order of DOM fractions was MHLF→ HMWF1→ nitriles→ LMWF2→ HMWF2→ amides→ LMWF1→ aromatics. The nitriles and HMWF1 were continuously removed in treatment process. LMWF2 was degraded by anaerobic and nitrifying bacteria, while the degradation of amides and HMWF2 by anaerobic and aerobic microorganisms. Furthermore, the nitriles could reduce microbial activities, which was mainly removed with the degradation of LMWF2. SFS combined with Gaussian band fitting and 2D-COS could be an applicable approach to monitor variations of DOM fractions and evaluate its removals in the AFW. These findings could be conducive to developing an online equipment in WWTP.

Probing the molecular interaction between photoaged polystyrene microplastics and fulvic acid

As emerging contaminants, microplastics (MPs) are becoming a matter of global concern, and they have complex interactions with dissolved organic matter (DOM) widely present in aqueous environments. Here, we investigate the molecular interactions between aged polystyrene microplastics (PS-MPs) and fulvic acid (FA) under neutral conditions using a series of analytical techniques. The structural changes of FA and the binding interactions of PS-MPs with FA at a molecular level were explored by fluorescence and FT-IR combined with two-dimensional correlation spectroscopy (2D-COS). Results showed that photoaging of PS-MPs changed the sequence of structural variations with FA. Atomic force microscopy-infrared spectroscopy (AFM-IR) strongly demonstrated that the surface roughness of both pristine and aged PS-MPs greatly increased after FA addition. Meanwhile, AFM-IR and Raman spectroscopy revealed a stronger interaction between aged PS-MPs and FA. The content of oxygen-containing functional groups in PS-MPs increased after aging and after binding with FA, and surface distribution of these functional groups also changed. XPS analyses indicated that the oxygen content in PS-MPs increased after the interaction with FA and the increase in oxygen content was even greater in aged PS-MPs. Overall, these research findings are useful to understand the environmental impacts of DOM-MPs interactions and to address the uncertainty of MPs aging effect on their environmental behavior in aquatic ecosystems.

New insights into the effect of pyrolysis temperature on the spectroscopy properties of dissolved organic matter in manure-based biochar.

Dissolved organic matter (DOM) derived from biochar takes a crucial role in transport and bioavailability toward contaminants; hence, it is undeniable that a thorough analysis of its properties is important. So far, the effect of pyrolysis temperature on the functional groups, components, and evolutionary sequence of manure-based biochar DOM has not been adequately investigated. Here, DOM was released from two typical livestock manures (cow and pig) at five pyrolysis temperatures (300 ~ 700°C), and it was explored in depth with the aid of moving window 2D correlation spectroscopy (MW-2D-COS) and heterogeneous 2D correlation spectroscopy (hetero-2D-COS). The results demonstrated that the concentration, aromaticity, and hydrophobicity of DOM were greater at high temperatures, and more DOM was liberated from cow manure-based biochar at identical temperature. Protein-like compounds dominated at high temperatures. The pyrolysis temperatures of final configuration transformation points of the fulvic acid-like component and the aromatic ring C=C in DOM were 400°C and 500°C, respectively. Moreover, Fourier transform infrared spectroscopy combined with two-dimensional correlation analysis indicated that the functional group evolution of DOM depends on the pyrolysis temperature and feedstock type. The study provides a new perspective on manure management and environmental applications of biochar.

Tabular Two-Dimensional Correlation Analysis for Multifaceted Characterization Data.

We propose tabular two-dimensional correlation spectroscopy analysis for extracting features from multifaceted characterization data, essential for understanding material properties. This method visualizes similarities and phase lags in structural parameter changes through heatmaps, combining hierarchical clustering and asynchronous correlations. We applied the proposed method to data sets of carbon nanotube (CNT) films annealed at various temperatures and revealed the complexity of their hierarchical structures, which include elements such as voids, bundles, and amorphous carbon. Our analysis addresses the challenge of attempting to understand the sequence of structural changes, especially in multifaceted characterization data where 11 structural parameters derived from eight characterization methods interact with complex behavior. The results show how phase lags (asynchronous changes from stimuli), and parameter similarities can illuminate the sequence of structural changes in materials, providing insights into phenomena such as the removal of amorphous carbon and graphitization in annealed CNTs. This approach is beneficial even with limited data and holds promise for a wide range of material analyses, demonstrating its potential in elucidating complex material behaviors and properties.

Two-Dimensional Fluctuation Correlation Spectroscopy (2D-FlucCS): A Method to Determine the Origin of Relaxation Rate Dispersion.

Relaxation rate dispersion, i.e., nonexponential or multicomponent kinetics, is observed in complex systems when measuring relaxation kinetics. Often, the origin of rate dispersion is associated with the heterogeneity in the system. However, both homogeneous (where all molecules experience the same rate but inherently nonexponential) and heterogeneous (where all molecules experience different rates) systems can exhibit rate dispersion. A multidimensional correlation analysis method has been demonstrated to detect and quantify rate dispersion observed in molecular rotation, diffusion, solvation, and reaction kinetics. One-dimensional (1D) autocorrelation function detects rate dispersion and measures its extent. Two-dimensional (2D) autocorrelation function measures the origin of rate dispersion and distinguishes homogeneous from heterogeneous. In a heterogeneous system, implicitly there exist subensembles of molecules experiencing different rates. A three-dimensional (3D) autocorrelation function measures subensemble exchange if present and reveals if the system possesses static or dynamic heterogeneity. This perspective discusses the principles, applications, and potential and also presents a future outlook of two-dimensional fluctuation correlation spectroscopy (2D-FlucCS). The method is applicable to any experiment or simulation where a time series of fluctuation in an observable (emission, scattering, current, etc.) around a mean value can be obtained in steady state (equilibrium or nonequilibrium), provided the system is ergodic.

Enhanced Spectral Resolution and Two-Dimensional Correlation Spectroscopy (2D-COS).

The apparent enhancement of spectral resolution is one of the attractive features of two-dimensional correlation spectroscopy (2D-COS). Highly overlapped adjacent bands often encountered in one-dimensional spectra may be effectively differentiated and identified by spreading peaks along the second dimension. This differentiating feature or selectivity is especially prominent in asynchronous spectra, where even a slight difference in the variation patterns of overlapped bands in response to a given perturbation results in the generation of cross-peaks. While cross-peaks in asynchronous spectra can identify signals originating from different moieties or bands, they do not effectively specify which regions of spectra actually share the same molecular origin. Overreliance on asynchronous spectra alone risks the potential false negative assessment or lack of sufficient specificity, leading to the failure of classifying signals into a reasonable set of component groups. The combined use of synchronous and asynchronous spectra coupled with the scaling techniques, elimination of anti-correlated negative synchronous peaks, and a robust line shape narrowing method provides a means to achieve both selectivity and specificity for resolution-enhancement of 2D-COS.

Adsorption characteristics and mechanisms of water-soluble polymers (PVP and PEG) on kaolin and montmorillonite minerals

The excessive use and accumulation of water-soluble polymers (WSPs, known as “liquid plastics”) in the environment can pose potential risks to both ecosystems and human health, but the environmental fate of WSPs remains unclear. Here, the adsorption behavior of WSPs with different molecular weight on kaolinite (Kaol) and montmorillonite (Mt) were examined. The results showed that the adsorption of PEG and PVP on minerals were controlled by hydrogen bond and van der Waals force. The Fourier transform infrared (FTIR) spectra and two-dimensional correlation spectroscopy (2D-COS) analysis revealed that there were interactions between the Al-O and Si-O groups of the minerals and the polar O- or N-containing functional groups as well as the alkyl groups of PEG and PVP. The adsorption characteristics of WSPs were closely related to their molecular weight and the pore size of minerals. Due to the relatively large mesopore size of Kaol, both PEG and PVP were absorbed into inner spaces, for which the adsorption capacity increased with molecular weight of the polymers. For Mt, all types of PEG could enter its micropores, while PVP with larger molecular weights appeared to be confined externally, leading to a decrease in the adsorption capacity of PVP with increasing molecular weight. The findings of this study provide a theoretical basis for scientific evaluation of environmental processes of WSPs.

Investigation of selective SERS enhancement mechanism of Au nanospheres and Au nanorods based on 2T2D-SERS correlation spectroscopy

The selective enhancement mechanism in surface-enhanced Raman scattering (SERS) is demonstrated. Two different types of single nanoparticles (Au nanosphere and Au nanorod) were used to investigate the role of the localized surface plasmon resonance (LSPR) in SERS spectra by using the two-trace two-dimensional (2T2D) correlation spectroscopy. The SERS intensities of three probe molecules, 4-mercaptobenzoic acid (4-MBA), 4-aminothiophenol (4-ATP), and 4-bromobenzenethiol (4-BBT), respectively, were enhanced but slightly different when adsorbed on Au nanospheres and Au nanorods. 2T2D correlation SERS spectra clearly showed that even with the same shape of Au nanoparticles, the main factors influencing the SERS enhancement can vary depending on the specific type of SERS tags used. Such subtle difference could not be clearly identified by the conventional spectral analysis. This result sheds light on potential applications of 2T2D correlation spectroscopy. For 4-MBA molecules, the a1 and b2 modes are mainly affected by the Au nanospheres and Au nanorods. For 4-ATP molecules, the a1 and b2 modes related to C–S stretching combined with C–C stretching band are mainly affected by Au nanorods and Au nanospheres. For 4-BBT molecules, the a1 and b2 modes of C–C (aromatic ring) stretching band are mainly affected by Au nanorods and Au nanospheres. This study offers valuable insights into the relationship between nanoparticle shape and SERS enhancement.

Molecular-level insights into the temperature-dependent formation dynamics and mechanism of water-soluble dissolved organic carbon derived from biomass pyrolysis smoke

Water soluble organic carbon (WSOC) derived from biomass pyrolytic smoke is deposited through atmospheric aerosols, negatively affecting aquatic ecological quality and safety. However, the temperature-dependent molecular diversity and dynamic formation of smoke-derived WSOC remain poorly understood in water. Herein, we explored the molecular-level formation mechanism of pyrolytic smoke-derived WSOC in water to explain the evolution, heterogeneous correlations, and sequential responses of molecules and functional groups to increasing pyrolysis temperature. Two-dimensional correlation spectroscopy was used to innovatively establish the characteristic correlations between spectroscopy and Fourier transform-ion cyclotron resonance mass spectrometry. Temperature-dependent formation of WSOC exhibited diversity in absorbance/fluorescent components, unique/common molecules, and their chemical parameters, showing the simultaneous formation and degradation reactions. The common WSOC molecules with lower and higher degrees of oxidation showed significant positive and negative correlations with the fluorescent components, respectively. The primary sequential response of WSOC molecules to increasing pyrolysis temperature (lignin-like molecules → unsaturated hydrocarbons, condensed aromatic molecules → lipid-like/aliphatic-/peptide-like molecules) corresponded to the temperature response of functional groups (carboxylic/alcoholic → polysaccharides → aromatics/amides/phenolic/aliphatic groups), demonstrating well synergistic relationships between them. These novel findings will contribute to the comprehensive understanding and assessments of potential environmental behavior or risks of WSOC in aquatic ecosystems.

The structural transformation reversibility of biogas slurry derived dissolved organic matter and its binding properties with norfloxacin under temperature fluctuation

The widespread use of biogas slurry could potentially raise the environmental risk of antibiotics. Dissolved organic matter (DOM), as the most active part of biogas slurry, was able to interact with antibiotics and play a crucial role in the structure and function of soil and aquatic ecosystems. The recent shifts in global climate patterns have garnered significant attention due to their substantial impact on temperature, thereby exerting a direct influence on the characteristics of DOM and subsequently on the environmental behavior of antibiotics. However, there is limited research concerning the impact of temperature on the binding of DOM and antibiotics. Thus, this study aimed to explore the temperature-dependent structural transformation and driving factors of biogas slurry-derived DOM (BSDOM). Additionally, the binding characteristics between BSDOM and the commonly used antibiotic norfloxacin (NOR) at different temperatures were studied by using multi spectroscopic methods and two-dimensional correlation spectroscopy (2D-COS) analysis. The results suggested that the temperature-dependent structural transformation of BSDOM was reversible, with a slight lag in the transition temperature under cooling (13 °C for heating and 17 °C for cooling). Heating promoted the conversion of protein-like to humic-like substances while cooling favored the decomposition of humic-like substances. BSDOM and NOR were static quenching, with oxygen-containing functional groups such as C-O and -OH playing an important role. Temperature influenced the order of binding, the activity of the protein fraction, and its associated functional groups. At temperatures of 25 °C and 40 °C, the fluorescent components were observed to exhibit consistent binding preferences, whereby the humic-like component demonstrated a greater affinity for NOR compared to the protein-like component. However, the functional group binding order exhibited an opposite trend. At 10 °C, a new protein-like component appeared and bound preferentially to NOR, when no C-O stretch corresponding to the amide was observed. The finding will contribute to a comprehensive understanding of the interaction mechanisms between DOM and antibiotics under climate change, as well as providing a theoretical basis to reduce the environmental risks of biogas slurry and antibiotics.

Three Stages of Dynamic Assembly Process of Dipeptide-Based Supramolecular Gel Revealed by In Situ Infrared Spectroscopy.

The exploration of short peptide-based assembly is vital for understanding protein-misfolding-associated diseases and seeking strategies to attenuate aggregate formation. While, the molecular mechanism of their structural evolution remains poorly studied in view of the dynamic and unpredictable assembly process. Herein, infrared (IR) spectroscopy, which serves as an in situ and real-time analytical technique, was intelligently employed to investigate the mechanism of phase transition and aggregate formation during the dynamic assembly process of diphenylalanine. Combined with other spectroscopy and electron microscopy technologies, three stages of gel formation and the main driving forces in different stages were revealed. A variety of stoichiometric methods such as continuous wavelet transform, principal component analysis, and two-dimensional correlation spectroscopy techniques were conducted to analyze the original time-dependent IR spectra to obtain detailed information on the changes in the amide bands and hydration layer. The microenvironment of hydrogen bonding among amide bands was significantly changed with the addition of pyridine derivatives, resulting in great differences in the properties of co-assembled gels. This work not only provides a universal analytical way to reveal the dynamic assembly process of dipeptide-based supramolecular gel but also expands their applications in supramolecular regulation and high-throughput screens in situ.

Application of biological soil crusts for efficient cadmium removal from acidic mine wastewater

Utilizing an acid-resistant biological soil crust (BSC) species that we discovered, we developed a device capable of efficiently removing cadmium (Cd) from mine wastewater with varying levels of acidity. Our research has demonstrated that this particular BSC species adapts to acidic environments by regulating the balance of fatty acids and acid-resistant enzymes. At a Cd concentration of 5 mg/L, the BSC grew well. When the initial Cd concentration was 2 mg/L, and the flow rate was set at 1 mL/min (at pH levels of 3, 4, and 5), BSC had a high removal rate of Cd, and the removal rate increased with the increase of pH (from 90% to 97%). Chemisorption is the primary removal mechanism in the initial stage, where the functional groups and minerals on the surface of the BSC play a significant role. In addition, BSC also adapts to Cd stress by changing bacterial community structure. It was discovered through infrared spectroscopy and two-dimensional correlation analysis that hydrophilic groups, specifically phosphate and carboxyl groups, exhibited the highest reactivity during the Cd binding process. Protein secondary structure analysis confirmed that as the pH increased, the adsorption capacity of the BSC increased; making biofilm formation easier. This study presents a novel approach for the treatment of acidic wastewater.

Insights into biomass mild gasification based on mass-energy balance: Establishing condition, thermodynamic mechanisms, and product properties

To alleviate the challenge of gas and char quality competition arising from over oxidation during the biomass gasification. A new concept of biomass mild gasification based on mass and energy balance was proposed, wherein the energy produced during the oxidation stage precisely equals the energy consumed in the pyrolysis and reduction stages. An energy balance index (EBI), obtained through the combined use of TG-DSC-MS-FTIR and Gaussian model fitting to represent the relationship between mass loss and endothermic/exothermic properties among different reaction stages, was optimized by response surface methodology (RSM) to obtain the critical conditions of biomass mild gasification. Under two typical mild gasification conditions, the quality characteristics of mild gasification biochar were analyzed and the gas evolution characteristics and sequence were determined through the combination of two-dimensional correlation spectroscopy (2D COS). The results indicated that mild gasification can be achieved within a heating rate range of 21 °C/min to 26 °C/min, with the oxidation temperature ranging from 776 °C to 869 °C. Under mild gasification conditions, the biochar produced at 850 °C-20 °C/min had higher COOH content, which can regulate soil pH, promote organic matter degradation, and enhance biological activity. Under the 800 °C-25 °C/min mild gasification condition, more CO and CH4 were generated during the pyrolysis stage, but slightly more CO2 was also produced during both the pyrolysis and oxidation stages. This work provides comprehensive thinking on the precious control of gasification process, which lays a foundation for co-generation of high-quality char and gas.

Enhanced 3-D asynchronous correlation data preprocessing method for Raman spectroscopy of Chinese handmade paper

We have developed a novel 3D asynchronous correlation method (3D-ACM) designed for the classification and identification of Chinese handmade paper samples using Raman spectra and machine learning. The 3D-ACM approach involves two rounds of tensor product and Hilbert transform operations. In the tensor product process, the outer product of the spectral data from different samples within the same category is computed, establishing inner connections among all samples within that category. The Hilbert transform introduces a 90-degree phase shift, resulting in a true three-dimensional spectral data structure. This expansion significantly increases the number of equivalent frequency points and samples within each category. This enhancement substantially boosts spectral resolution and reveals more hidden information within the spectral data. To maximize the potential of 3D-ACM, we employed six machine learning models: principal component analysis (PCA) with linear regression (LR), support vector machine (SVM) with LR, k-Nearest Neighbors (KNN), random forest (RF), and convolutional neural network (CNN). When applied to the 3D-ACM data preprocessing method, R-squared values of PLS-LR, KNN, RF and CNN supervised models, approached or equaled 1. This indicates exceptional performance comparable to unsupervised models like PCA. 3D-ACM stands as a versatile mathematical technique not confined to spectral data. It also eliminates the necessity for additional experimental setups or external control conditions, distinct from traditional two-dimensional correlation spectroscopy. Moreover, it preserves the original experimental data, setting it apart from conventional data preprocessing methods. This positions 3D-ACM as a promising tool for future material classification and identification in conjunction with machine learning.

Two-Dimensional Correlation Spectroscopy (2D-COS) Analysis of Evolving Hyperspectral Images.

The evolutionary behavior is examined for heterogeneously distributed hyperspectral images of a simulated biological tissue sample comprising lipid-like and protein-like components during the aging process. Taking a simple planar average of a spectral image loses useful information about the spatially resolved nature of the data. In contrast, multivariate curve resolution (MCR) analysis of a spectral image at a given stage of aging produces a set of loadings of major component groups. Each loading represents the combined spectral contributions of a mixture of similar but not identical constituents (i.e., lipid-like and protein-like components). Temporal analysis of individual component groups using two-dimensional correlation spectroscopy (2D-COS) and MCR provides much-streamlined results without interferences from the overlapped contributions. Grouping of data into separate components also allows for the effective comparison of the parallel processes of lipid oxidation and protein denaturation involving a number of constituents using the heterocomponent 2D-COS analysis. The complex interplays of lipid constituents and protein secondary structures during the tissue aging process are unambiguously highlighted. The possibility of extending this approach to a much more general form of applications using a moving window analysis is also discussed.