Unmasking the heterogeneity of carbohydrates in heartwood, sapwood, and bark of Eucalyptus

Advanced machine learning-driven characterization of new natural cellulosic Lablab purpureus fibers through PCA and K-means clustering techniques

The increasing demand for sustainable and eco-friendly materials has spurred significant interest in natural fibers as alternatives to synthetic reinforcements in composite applications. This study aims to explore the potential of Lablab purpureus fibers (LPFs) as sustainable materials by employing advanced characterization techniques and machine learning-driven analysis. Chemical analysis identified LPFs' primary composition as cellulose (72.34 %), hemicellulose (11.46 %), and lignin (8.99 %), with minor components including wax (3.45 %) and ash (2.59 %). The average fiber diameter was measured at 237.95 μm, with a density of 1.24 g/cm3, making LPFs lightweight yet robust. Mechanical testing across varying relative humidity (RH) levels revealed a decrease in tensile properties, with fracture stress declining from 420 MPa at 24 % RH to 350 MPa at 81 % RH. X-ray diffraction (XRD) analysis demonstrated a crystallinity index (CI) of 74.62 % and a crystalline size of 8.73 nm, indicating high structural integrity. Fourier Transform Infrared (FTIR) spectroscopy, combined with Principal Component Analysis (PCA), provided insights into the chemical bonds within the fibers, confirming the presence of cellulose I and cellulose II polymorphs. Thermogravimetric Analysis (TGA) highlighted thermal degradation stages, with hemicellulose decomposition at 220-315 °C, cellulose decomposition at 315-400 °C, and lignin degradation above 400 °C, showcasing thermal stability up to 320 °C. Hydrothermal absorption behavior, analyzed through K-means clustering, revealed distinct absorption patterns, with a maximum moisture uptake of 12.3 % at 81 % RH. Biodegradability tests indicated increased decomposition with higher RH, peaking at 81 % RH with a weight loss of 68.57 % over 16 days. Scanning Electron Microscopy (SEM) revealed intricate fiber morphology, including layered transitions, internal voids, and a honeycomb-like surface structure. Compared to other natural fibers such as Cissus quadrangularis (CI: 82.73 %) and lavender (CI: 65 %), LPFs exhibit a balanced combination of mechanical strength, thermal stability, and biodegradability, making them promising candidates for biocomposites and eco-friendly materials. These findings, supported by machine learning-driven insights, position LPFs as a sustainable alternative to synthetic fibers in industrial applications.

Novel CRM cosine similarity mapping strategy for simultaneous in-situ visual profiling lignocellulose in plant cell walls

Confocal Raman microscopy (CRM) is a promising in-situ visual technique that provides detailed insights into multiple lignocellulosic components and structures in plant cell walls at the micro-nano scale. In this study, we propose a novel CRM cosine similarity (CS) mapping strategy for the simultaneous in-situ visual profiling of lignin, cellulose, and hemicellulose in plant cell walls. The main stages of this strategy include: 1) a modified Otsu algorithm for extracting the regions of interest (ROI); 2) a modified subtraction method for cleaning the background signals in the ROI spectra; 3) a lignin signal subtraction method based on the pixel correction factor for eliminating the interference of strong lignin signals with weak cellulose and hemicellulose signals in the Raman full spectra of the cell walls; 4) second-order derivative spectral preprocessing for enhancing the discrimination between the characteristic peaks of cellulose and hemicellulose; 5) a CS mapping algorithm for simultaneous in-situ profiling of lignin, cellulose, and hemicellulose in plant cell walls. The effectiveness of the strategy is verified by characterizing the Brittle Culm1 (BC1) gene-mutant rice stem (IL349-BC1-KO) with known bioinformatics. This approach provides methodological support for in-situ visualization and analysis in fields such as plant or crop science at the micro-nano scale.

Extraction and characterization of Bougainvillea glabra fibers: A study on chemical, physical, mechanical and morphological properties

Bougainvillea glabra fibers (BGFs) present a promising avenue for sustainable material development owing to their abundance and favorable properties. This study entails a thorough investigation into the composition, physical characteristics, mechanical behavior, structural properties, thermal stability, and hydrothermal absorption behavior of BGFs. Chemical analysis reveals the predominant presence of cellulose (68.92 %), accompanied by notable proportions of hemicellulose (12.64 %), lignin (9.56 %), wax (3.72 %), moisture (11.78 %), and ash (1.75 %). Physical measurements ascertain a mean fiber diameter of approximately 232.63 ± 8.59 μm, while tensile testing demonstrates exceptional strength, with stress values ranging from 120 ± 18.26 MPa to a maximum of 770 ± 23.19 MPa at varying strains. X-ray diffraction (XRD) elucidates a crystalline index (CI) of 68.17 % and a crystallite size (CS) of 9.42 nm, indicative of a well-defined crystalline structure within the fibers. Fourier-transform infrared spectroscopy (FTIR) confirms the presence of characteristic functional groups associated with cellulose, hemicellulose, wax, and water content. Thermogravimetric analysis (TGA) delineates distinct thermal degradation stages, with onset temperatures ranging from 102.76 °C for water loss to 567.55 °C for ash formation. Furthermore, hydrothermal absorption behavior exhibits temperature and time-dependent trends, with absorption percentages ranging from 15.26 % to 32.19 % at temperatures between 30 °C and 108 °C and varying exposure durations. These comprehensive findings provide essential insights into the properties and potential applications of BGFs in diverse fields such as bio-composites, textiles, and environmentally friendly packaging solutions.

Understanding the dynamic evolution of hemicellulose during Pinus taeda L. growth

Pinus taeda L. is a fast-growing softwood with significant commercial value. Understanding structural changes in hemicellulose during growth is essential to understanding the biosynthesis processes occurring in the cell walls of this tree. In this study, alkaline extraction is applied to isolate hemicellulose from Pinus taeda L. stem segments of different ages (1, 2, 3, and 4 years old). The results show that the extracted hemicellulose is mainly comprised of O-acetylgalactoglucomannan (GGM) and 4-O-methylglucuronoarabinoxylan (GAX), with the molecular weights and ratios (i.e., GGM:GAX) of GGM and GAX increasing alongside Pinus taeda L. age. Mature Pinus taeda L. hemicellulose is mainly composed of GGM, and the ratio of (mannose:glucose) in the GGM main chain gradually increases from 2.45 to 3.60 with growth, while the galactose substitution of GGM decreases gradually from 21.36% to 14.65%. The acetylation of GGM gradually increases from 0.33 to 0.45 with the acetyl groups mainly substituting into the O-3 position in the mannan. Furthermore, the contents of arabinose and glucuronic acid in GAX gradually decrease with growth. This study can provide useful information to the research in genetic breeding and high-value utilization of Pinus taeda L.

Beyond What Meets the Eye: Imaging and Imagining Wood Mechanical-Structural Properties

Wood presents a hierarchical structure, containing features at all length scales: from the tracheids or vessels that make up its cellular structure, through to the microfibrils within the cell walls, down to the molecular architecture of the cellulose, lignin, and hemicelluloses that comprise its chemical makeup. This structure renders it with high mechanical (e.g., modulus and strength) and interesting physical (e.g., optical) properties. A better understanding of this structure, and how it plays a role in governing mechanical and other physical parameters, will help to better exploit this sustainable resource. Here, recent developments on the use of advanced imaging techniques for studying the structural properties of wood in relation to its mechanical properties are explored. The focus is on synchrotron nuclear magnetic resonance spectroscopy, X-ray diffraction, X-ray tomographical imaging, Raman and infrared spectroscopies, confocal microscopy, electron microscopy, and atomic force microscopy. Critical discussion on the role of imaging techniques and how fields are developing rapidly to incorporate both spatial and temporal ranges of analysis is presented.

Rapid identification model based on decision tree algorithm coupling with 1H NMR and feature analysis by UHPLC-QTOFMS spectrometry for sandalwood

Sandalwood is one of the most valuable woods in the world. However, today's counterfeits are widespread, it is difficult to distinguish authenticity. In this paper, similar genus (Dalbergia and Pterocarpus) and confused species (Gluta sp.) of sandalwood were quickly and efficiently identified. Rapid identification model based on ¹H NMR and decision tree (DT) algorithm was firstly developed for the identification of sandalwood, and the accuracy was improved by introducing the AdaBoost algorithm. The accuracy of the final model was above 95%. And the feature components between different species of sandalwood were further explored using UHPLC-QTOFMS and NMR spectrometry. The results showed that 183 compounds were identified, among which 99 were known components, 84 were unknown components. The ¹H NMR and ¹³C NMR signals of 505 samples were assigned, among them, 14 compounds were attributed, characteristic chemical shift intervals with great differences in the model were analysed. Furthermore, the fragmentation pattern of different compounds from sandalwood, in both positive and negative ion ESI modes, was summarized. The results showed a potential and rapid tool based on DT, NMR spectroscopy and UHPLC-QTOFMS, which had performed great potential for rapid identification and feature analysis of sandalwood.