Band 73, Heft 4

Conventional near-infrared (NIR) spectroscopy has shown its potential to separate wood species nondestructively based on the aggregate effect of light absorption and scattering values. However, wood has an aligned microstructure, and there is a large refractive index (RI) mismatch between the wood cell wall substance ( n ≈1.55) and the cell lumen (air≈1.0, water≈1.33). Light scattering is dominant over absorption (μ′s≫μa)$({\mu '_s} \gg {\mu _a})$ in wood, and this fact can be utilized for complex classification purposes. In this study, an NIR hyperspectral imaging (HSI) camera combined with one focused halogen light source (Ø 1 mm) was designed to evaluate the light scattering patterns of five softwood (SW) and 10 hardwood (HW) species in the wavelength range from 1002 to 2130 nm. Several parameters were combined to improve the data quality, such as image histogram plots of defined spaced bins (associated with diffuse reflectance values of light), variance calculation on the frequency (the number of pixels in each bin) of each histogram and the principal component analysis (PCA) of all the variance values at each wavelength. The identification accuracy of the quadratic discriminant analysis (QDA) under the five-fold cross-validation method was 94.1%, based on the first three principal component (PC) scores.

The predictability of modulus of elasticity (MOE), modulus of rupture (MOR) and density of 120 samples of Scots pine ( Pinus sylvestris L.) were investigated using various non-destructive variables (such as time of flight of stress wave, natural frequency of longitudinal vibration, penetration depth, pullout resistance, visual grading and concentrated knot diameter ratio), and based on multivariate algorithms, applying WEKA as machine learning software. The algorithms used were: multivariate linear regression (MLR), Gaussian, Lazy, artificial neural network (ANN), Rules and decision Tree. The models were quantified based on the root-mean-square error (RMSE) and the coefficient of determination (R 2 ). To avoid model overfitting, the modeling was built and the results validated via the so-called 10-fold cross-validation. MLR with the “greedy method” for variable selection based on the Akaike information metric (MLRak) significantly reduced the RMSE of MOR and MOE compared to univariate linear regressions (ULR). However, this reduction was not significant for density prediction. The predictability of MLRak was not improved by any other of the tested algorithms. Specifically, non-linear models, such as multilayer perceptron, did not contribute any significant improvements over linear models. Finally, MLRak models were simplified by discarding the variables that produce the lowest RMSE increment. The resulted models could be even further simplified without significant RMSE increment.

Time-of-flight (ToF) measurements were conducted on twelve 76×226-mm 2 in cross-section, 4.52-m long specimens of Norway spruce timber pieces from a dismantled 19 th -century building in Barcelona (Catalonia, Spain). Two commercially available acoustic devices were used: the USLab ultrasound device with conical 22 and 45 kHz sensors and the MicroSecond Timer (MST) stress wave device with spike sensors. ToF were obtained for the full-length (4.52 m) specimens in an end-to-end arrangement and for lengths of 0.5, 1, 1.5, 2, 3 and 4 m on the same surface and on opposite surfaces. The differences between the velocities obtained from end-to-end and semi-direct measurements were less than 4.5%. Apparent velocity dependence on length was observed in short-distance measurements. This could be corrected by determining a time lag (tL) from a linear regression between the ToF and the distance. Estimation of the modulus of elasticity (MOE) from the dynamic MOE (MOE dyn ) is acceptable (r 2 =0.45–0.69) depending on the measurement procedure, and the best results for in situ timber are obtained in edge surface velocity. Although modulus of rupture (MOR) estimation from MOE dyn improves slightly when knottiness is included, it has low predictive capacity.

Pinus nigra var. laricio bark and its hot-water extracts (HWE) obtained at an analytical and pilot plant scale have been characterized in terms of phenolic extractives, condensed tannins (CTs), carbohydrates and inorganic compounds. Analytical extractions with aqueous acetone were also performed for comparison with HWE. The bark contains 35.5 g kg −1 CT, and two-thirds of it could be extracted. Analytical HWE at 75°C led to a total yield of 56.4 g kg −1 . The extracts are mainly composed of phenolic compounds (50.7%) and pectins (19.7%). CTs amount to 17.9% of the extracts and are procyanidins with a mean degree of polymerization (DP) of about 9. Non-tannin phenolic oligomers also occurred in the extracts, which could be identified by pyrolysis gas chromatography mass spectrometry (Py-GC/MS) as lignin fragments. Matrix-assisted laser desorption/ionization-time-of-flight-mass spectrometry (MALDI-TOF MS) revealed that the CT is a flavanol derivative in methylated form. Further characterization and tailoring of the HWE properties is needed in the context of their specific application.

The molar mass (MM) and polydispersity indices (PDI) of acetylated hardwood kraft lignin (Ac-HWKL) and Ac-HWKL fractions were measured by size-exclusion chromatography with multi-angle laser light scattering (SEC-MALLS). The detectors worked at LL 658 nm and LL 785 nm . The MM of Ac-HWKL measured at 785 nm was much smaller than that measured at 658 nm. The number-average (Mn) and weight-average (Mw) molar masses of Ac-HWKL determined at 785 nm were approximately 6.2 and 6.5 times larger, respectively, than the values estimated using a conventional calibration curve created with authentic polystyrene standards in tetrahydrofuran (THF). Based on the Mw of Ac-HWKL fractions, the Mark-Houwink-Sakurada equation for Ac-HWKL was established to be [ η ]/ml g −1 =0.320 M 0.24 in THF and [ η ]/ml g −1 =0.142 M 0.26 in dimethyl sulfoxide (DMSO). These results demonstrate that Ac-HWKL has a more compact structure in an organic solvent than polystyrene.

Classical guitar necks were submitted to 40, 65 and 80% relative humidity (RH) during time periods ranging from 24 h to several months, and moisture-induced deformations were observed. Different types of classical guitar necks were investigated in terms of structure and reinforcement. The influences of the wood species and the neck reinforcements on the moisture content (MC)-induced deformation were in focus. First, before humidity exposure in the climatic chamber, the relative positions of three points marked on each sample were measured with a control ruler and noted. After exposure, the displacement of the reference points on the samples were verified periodically. The differences between the measurements in different stages and reference positions were calculated, resulting in relative displacement data of the measured points. During the experiments, the MC of the sample was monitored by weighing. The variation of RH leads to dimensional instability and moisture-induced stresses of the guitar necks, which affect the acoustic quality of a guitar. Steel enforcement of the guitar neck improves this situation.

The effect of thermal modification (TM) on the color of western hemlock wood and its physical and mechanical properties were investigated. The focus of this study was the prediction of material properties of thermally modified wood based on the color change via the “group method of data handling (GMDH)” neural network (NN). The NN was trained by color parameters for predicting the equilibrium moisture content (EMC), density, porosity, water absorption (WA), swelling coefficient, dynamic modulus of elasticity (MOE dyn ) and hardness. The color parameters showed a significant correlation with temperature and are well correlated with the heat treatment (HT) intensity. Color parameters combined with the GMDH-type NN successfully predicted the physical properties of the material. The best correlation was achieved with the swelling coefficient, EMC and WA. All these properties were significantly influenced by HT. The color parameters did not seem suitable for predicting the wood hardness and MOE dyn . The GMDH NN shows a higher model accuracy than the multivariate linear and partial least squares (PLS) regression models.

A self-cleaning surface treatment has been developed, which is prepared by an eco-friendly and effective two-step method. First, graphitic carbon nitride (g-C 3 N 4 ) was deposited on the wood surface by vacuum impregnation, followed by heat treatment. The morphology, weight change, dimensional stability, crystal and micro structure, and elemental composition of the wood samples were determined by photography, weighting, swelling rate determination, X-ray diffraction (XRD) and scanning electron microscopy and energy dispersive X-ray analysis (SEM-EDXA). Moreover, contact angle measurements and photocatalytic degradation experiments were carried out with Rhodamine B as the target. It was demonstrated that g-C 3 N 4 is successfully deposited on the wood surface, which lowers the heat treatment temperature and renders photocatalytic properties to the coating. The heat treatment at around 210°C strengthened the immobility of g-C 3 N 4 and increased the surface hydrophobicity. Moreover, the better photocatalytic ability of the surface is accompanied with self-cleaning effects.

Alkaline and silane treatments were applied to wood flour (WF) to enrich its adhesion to bio-based thermoplastic polyurethane (TPU) matrix. TPU/WF eco-composites were prepared at a constant ratio of 30% by the melt blending process. The mechanical, thermo-mechanical, melt-flow, water uptake and morphological properties of the materials were investigated. Silane-treated WF filled composite exhibited better mechanical performance with respect to untreated WF due to enhancement of adhesion between WF and TPU matrix after surface treatments. This sample also gave the lowest water absorption value among composites. The results confirmed that silane treatment of WF led to significant improvement on the mechanical and physical properties of TPU-based composites in addition to an improved water resistance for outdoor applications.

A combination of Kymene ® 736 (K736) resin [a paper wet strength agent from reactions of hexamethylenediamine (HMDA) and epichlorohydrin (ECH)] and defatted soy flour (DSF) was investigated as a formaldehyde-free wood adhesive. In focus was the pH influence on the pot life of the adhesive and the water resistance of the plywood prepared with the new adhesive. It was found that the pH of the DSF-K736 adhesive had to be ≥11 before the resulting plywood panels could meet industrial water resistance requirements for interior plywood. The DSF/K736 weight ratio significantly affected the water resistance of the resulting 5-ply plywood. The 11/1 was the highest ratio that still enabled the plywood to meet the water resistance requirements. The hot-pressing temperature of ≥110°C and the hot-pressing time of ≥4 min resulted in 5-ply plywood panels with water resistance properties. The probable mechanism is that K736 reacts with nucleophilic groups in DSF and highly crosslinked adhesive networks are formed. This supposition is supported by the presence of an ester band in the Fourier-transform infrared (FTIR) spectrum of the cured DSF-K736 adhesive.

Titanium dioxide (TiO 2 )/graphene oxide (GO)-treated wood was fabricated through a one-step hydrothermal-vacuum dipping technique, in which silica sol serves as a dispersant and linker owing to its good stability and high surface area, while the visible light activates TiO 2 /GO and negative oxygen ions (NOI) arise. This approach exhibits a super dye adsorption capacity and enhanced photocatalytic efficiency. In focus was the effect of the three-dimensional (3D) GO dopant on the NOI production, which was very high in this system. Namely, the concentration of NOI is up to 1710 ions cm −3 after 60 min visible light irradiation. Moreover, recycling experiments show that the properties of a TiO 2 /GO-wood system are stable. The TiO 2 /GO-treated wood is a healthy, environmentally friendly material which is promising for indoor decoration.