Molecular structural dataset of lignin macromolecule elucidating experimental structural compositions

Machine Learning-Based Process Optimization in Biopolymer Manufacturing: A Review

The integration of machine learning (ML) into material manufacturing has driven advancements in optimizing biopolymer production processes. ML techniques, applied across various stages of biopolymer production, enable the analysis of complex data generated throughout production, identifying patterns and insights not easily observed through traditional methods. As sustainable alternatives to petrochemical-based plastics, biopolymers present unique challenges due to their reliance on variable bio-based feedstocks and complex processing conditions. This review systematically summarizes the current applications of ML techniques in biopolymer production, aiming to provide a comprehensive reference for future research while highlighting the potential of ML to enhance efficiency, reduce costs, and improve product quality. This review also shows the role of ML algorithms, including supervised, unsupervised, and deep learning algorithms, in optimizing biopolymer manufacturing processes.

Assessment of Accelerated Aging Effect of Bio-Oil Fractions Utilizing Ultrahigh-Resolution Mass Spectrometry and k-Means Clustering of van Krevelen Compositional Space

Bio-oils contain a substantial number of highly oxygenated hydrocarbons, which often exhibit low thermal stability during storage, handling, and refining. The primary objectives of this study are to characterize the hydroxyl group in bio-oil fractions and to investigate the relationship between the type of hydroxyl group and accelerated aging behavior. A bio-oil was fractionated into five solubility-based fractions, classified in two main groups: water-soluble and water-insoluble fractions. These fractions were then subjected to chemoselective reactions to tag molecules containing hydroxyl groups and analyzed by negative-ion electrospray ionization 21 T Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS). The fractions were also subjected to accelerated aging experiments and characterized by FT-ICR MS and bulk viscosity measurements. Extracting insightful information from ultrahigh-resolution data to aid in predicting upgrading methodologies and instability behaviors of bio-oils is challenging due to the complexity of the data. To address this, an unsupervised learning technique, k-means clustering analysis, was used to semiquantify molecular compositions with a close Euclidean distance within the (O/C, H/C) chemical space. The combination of k-means analysis with findings from chemoselective reactions allowed the distinctive hydroxyl functionalities across the samples to be inferred. Our results indicate that the hexane-soluble fraction contained numerous molecules containing primary and secondary alcohols, while the water-soluble fraction displayed diverse groups of oxygenated compounds, clustered near to carbohydrate-like and pyrolytic humin-like materials. Despite its high oxygen content, the water-soluble fraction showed minimal changes in viscosity during aging. In contrast, a significant increase in viscosity was observed in the water-insoluble materials, specifically, the low- and high-molecular-weight lignin fractions (LMWL and HMWL, respectively). Among these two fractions, the HMWL exhibited the highest increase in viscosity after only 4 h of accelerated aging. Our results indicate that this aging behavior is attributed to an increased number of molecular compositions containing phenolic groups. Thus, the chemical compositions within the HMWL are the major contributors to the viscosity changes in the bio-oil under accelerated aging conditions. This highlights the crucial role of oxygen functionality in bio-oil aging, suggesting that a high oxygen content alone does not necessarily correlate with an increase of viscosity. Unlike other bio-oil categorization methods based on constrained molecule locations within the van Krevelen compositional space, k-means clustering can identify patterns within ultrahigh-resolution data inherent to the unique chemical fingerprint of each sample.

Effects of lignin syringyl to guaiacyl ratio on cottonwood biochar adsorbent properties and performance

Lignin syringyl to guaiacyl ratio (S/G) has long been suspected to have measurable impacts on biochar formation, but these effects are challenging to observe in biochars formed from whole biomass. When the model bioenergy feedstock Populus trichocarpa (cottonwood), with predictable lignin macromolecular structure tied to genetic variation, is used as feedstock for biochar production, these effects become visible. In this work, two P. trichocarpa variants having lignin S/G of 1.67 and 3.88 were ground and pyrolyzed at 700 °C. Water-demineralization of feedstock was used to simultaneously evaluate any synergistic influences of S/G and naturally-occurring potassium on biochar physicochemical properties and performance. The strongest effects of lignin S/G were observed on specific surface area (SBET) and oxygen-content, with S/G of 1.67 improving SBET by 11% and S/G of 3.88 increasing total oxygen content in demineralized biochars. Functional performance was evaluated by breakthrough testing in 1% NH3. Breakthrough times for biochars were nearly double that of a highly microporous activated carbon reference material, and biochar with S/G of 3.88 had 10% longer breakthrough time than its lower S/G corollary. Results support a combination of pore structure and oxygen-functionalities in controlling ammonia breakthrough for biochar.

Modifying lignin: A promising strategy for plant disease control

Lignin is a complex polymer found in the cell walls of plants, providing structural support and protection against pathogens. By modifying lignin composition and structure, scientists aim to optimize plant defense responses and increase resistance to pathogens. This can be achieved through various genetic engineering techniques which involve manipulating the genes responsible for lignin synthesis. By either up regulating or down regulating specific genes, researchers can alter the lignin content, composition, or distribution in plant tissues. Reducing lignin content in specific tissues like leaves can improve the effectiveness of defense mechanisms by allowing for better penetration of antimicrobial compounds. Overall, Lignin modification through techniques has shown promising results in enhancing various plants resistance against pathogens. Furthermore, lignin modification can have additional benefits beyond pathogen resistance. It can improve biomass processing for biofuel production by reducing lignin recalcitrance, making the extraction of sugars from cellulose more efficient. The complexity of lignin biosynthesis and its interactions with other plant components make it a challenging target for modification. Additionally, the potential environmental impact and regulatory considerations associated with genetically modified organisms (GMOs) require careful evaluation. Ongoing research aims to further optimize this approach and develop sustainable solutions for crop protection.

Atomistic Simulations of Polydisperse Lignin Melts Using Simple Polydisperse Residue Input Generator

Understanding the physics of lignin will help rationalize its function in plant cell walls as well as aiding practical applications such as deriving biofuels and bioproducts. Here, we present SPRIG (Simple Polydisperse Residue Input Generator), a program for generating atomic-detail models of random polydisperse lignin copolymer melts i.e., the state most commonly found in nature. Using these models, we use all-atom molecular dynamics (MD) simulations to investigate the conformational and dynamic properties of polydisperse melts representative of switchgrass (Panicum virgatum L.) lignin. Polydispersity, branching and monolignol sequence are found to not affect the calculated glass transition temperature, Tg. The Flory-Huggins scaling parameter for the segmental radius of gyration is 0.42 ± 0.02, indicating that the chains exhibit statistics that lie between a globular chain and an ideal Gaussian chain. Below Tg the atomic mean squared displacements are independent of molecular weight. In contrast, above Tg, they decrease with increasing molecular weight. Therefore, a monodisperse lignin melt is a good approximation to this polydisperse lignin when only static properties are probed, whereas the molecular weight distribution needs to be considered while analyzing lignin dynamics.

Lignins as Promising Renewable Biopolymers and Bioactive Compounds for High-Performance Materials

The recycling of biomass into high-value-added materials requires important developments in research and technology to create a sustainable circular economy. Lignin, as a component of biomass, is a multipurpose aromatic polymer with a significant potential to be used as a renewable bioresource in many fields in which it acts both as promising biopolymer and bioactive compound. This comprehensive review gives brief insights into the recent research and technological trends on the potential of lignin development and utilization. It is divided into ten main sections, starting with an outlook on its diversity; main properties and possibilities to be used as a raw material for fuels, aromatic chemicals, plastics, or thermoset substitutes; and new developments in the use of lignin as a bioactive compound and in nanoparticles, hydrogels, 3D-printing-based lignin biomaterials, new sustainable biomaterials, and energy production and storage. In each section are presented recent developments in the preparation of lignin-based biomaterials, especially the green approaches to obtaining nanoparticles, hydrogels, and multifunctional materials as blends and bio(nano)composites; most suitable lignin type for each category of the envisaged products; main properties of the obtained lignin-based materials, etc. Different application categories of lignin within various sectors, which could provide completely sustainable energy conversion, such as in agriculture and environment protection, food packaging, biomedicine, and cosmetics, are also described. The medical and therapeutic potential of lignin-derived materials is evidenced in applications such as antimicrobial, antiviral, and antitumor agents; carriers for drug delivery systems with controlled/targeting drug release; tissue engineering and wound healing; and coatings, natural sunscreen, and surfactants. Lignin is mainly used for fuel, and, recently, studies highlighted more sustainable bioenergy production technologies, such as the supercapacitor electrode, photocatalysts, and photovoltaics.