The Calycophyllum spruceanum, known in Brazil, as “pau-mulato”, is a rapidly growing forest species native to the Amazon that produces dense wood used in floors, plywood, civil and naval construction and firewood, as it has high calorific value. However, studies on the species for energy production are scarce. Thus, our study aimed to evaluate the chemical and energetic properties of wood and charcoal from young “pau-mulato” trees (Calycophyllum spruceanum Benth.), from forest thinning waste, produced at different carbonization runs. The material was collected in an experimental plantation of Embrapa Acre, located in the municipality of Rio Branco, State of Acre, northern Brazil, composed of 300 six-year-old trees, planted in spacing of 3 × 2 m. For the characterization of wood, the chemical and energetic composition were analyzed. The wood, at the age studied, had chemical and energetic properties compatible with other species native to the Amazonian region. Moreover, three carbonization runs were tested, with heating rates at 1.67; 1.17; and 0.83 °C min− 1, to determine the yield in charcoal, condensable gases, non-condensable; apparent and energetic density; proximate analysis, and higher heating value of the charcoal produced. Due to the high-yield charcoal results (38.47
The growing demand for renewable energy sources has stimulated interest in biomass as a sustainable alternative, particularly the use of residues from the wood-processing industry. This study evaluated the energy potential of sawdust from two tropical hardwood species—maçaranduba (Manilkara spp.) and sucupira (Bowdichia spp.)—sourced from small-scale sawmills in Camaragibe, Pernambuco, Brazil. The samples underwent analyses for moisture content, bulk density, proximate composition (volatile matter, fixed carbon, and ash), structural chemical composition (extractives, lignin, and holocellulose), and calorific parameters, following standardized protocols (ASTM, NBR, and TAPPI). Beyond reporting conventional fuel properties, the study interprets these results from an application-oriented perspective for decentralized bioenergy use. Both residues exhibited favorable energy characteristics, including high calorific value and high volatile matter content. Maçaranduba showed slightly higher gross calorific value and markedly lower ash content, whereas sucupira presented lower moisture content and, consequently, a 3.5
Fibrous lignocellulosic substrates such as wood used in the marine environment, eg., wooden docks, are highly susceptible to biofilm formation, which can progress to fouling, resulting in material degradation and increased maintenance costs. They are primarily composed of cellulose microfibrils that are incorporated into a hemicellulose–lignin matrix with surface hydroxyl groups leading to polymer–fiber interfacial composites. This study focuses on developing three hydrophilic polymeric formulations for protective coatings using combinations of (3-Aminopropyl) triethoxysilane (APTES) with Polyethylene glycol (PEG) and Polydimethylsiloxane (PDMS) leading to a Polyethylene glycol – Polydimethylsiloxane (P-PDMS) formulation; APTES with Polyvinyl alcohol (PVA) and PDMS forming a Polyvinyl alcohol – Polydimethylsiloxane (PV-PDMS) formulation and APTES with a blend of PEG, PVA, and PDMS to form a Polyethylene glycol – Polyvinyl alcohol and Polydimethylsiloxane (P-PV-PDMS) formulation. The wood substrates were initially treated with APTES as a coupling agent due to the amino group binding to the cellulose on the wood surface and the polymeric formulation. The coated surfaces were evaluated for environmental testing through mechanical durability tests, including solvent resistance, sandpaper abrasion, and salt immersion, during which the P-PV-PDMS formulation demonstrated superior resilience. Surface and structural properties were analysed using water contact angle (WCA) measurements, Attenuated Total Reflectance – Fourier Transform Infrared Spectroscopy (ATR-FTIR), Thermogravimetric analysis (TGA) and Biological Atomic Force Microscopy (Bio-AFM), validating the functional attributes of the coatings. Further, biofilm assays were performed to check the ability of the coating to inhibit biofilm formation through which P-PV-PDMS resulted in reduced biofilm attachment highlighting the synergistic hydrating polymer interactions. These findings highlight the potential of these eco-friendly silane–polymer coatings as durable protective systems for extending the service life of wood in marine environments.
This study details the fabrication and characterization of low-cost bio-based composite panels designed for sustainable construction and eco-friendly packaging. The non-structural boards utilize agricultural by-products, reinforcing a gelatinized corn starch-chitosan-glycerol matrix with alkali-treated banana pseudo-stem fibers and silica-rich Rice Husk Ash (RHA) as an interstitial filler. Four formulations were evaluated via compression molding, ranging from 0 wt
Accurate identification of timber species is essential for biodiversity conservation, sustainable forest management, and timber supply chain control. This study evaluated four pre-trained convolutional neural networks—ResNet50, VGG16, InceptionV3, and MobileNetV2 and a Vision Transformer (ViT) as fixed feature extractors for the automated identification of Amazonian timber species. The resulting deep representations were classified using an Artificial Neural Network (ANN), Support Vector Classifier (SVC), Linear Discriminant Analysis (LDA), and Random Forest (RF). The dataset comprised 864 macroscopic images of wood cross-sections representing six species and five genera from the Brazilian Amazon. Two image-representation strategies were compared: original images and 20 random 224 × 224-pixel patches extracted from each image. Patch-level class probabilities were aggregated at the image level through soft voting, and predictive performance was evaluated using tree-level Leave-One-Group-Out Cross-Validation. The patch-based strategy improved classifier performance and produced more consistent recognition across species, particularly by increasing the recall of classes with the highest error rates when original images were used. Among the evaluated feature extractors, ViT representations yielded the best results when combined with ANN, SVC, and LDA. The ViT + SVC model achieved the highest predictive performance, with 98.29
