Optically transparent wood is a type of composite material, combining wood as a renewable resource with the optical and mechanical properties of synthetic polymers. During this study, the effect of monochromatic UV-C (λ—250 nm) radiation on transparent wood was evaluated. Samples of basswood were treated using a lignin modification method, to preserve most of the lignin, and subsequently impregnated with refractive-index-matched types of acrylic polymers (methyl methacrylate, 2-hydroxyethyl methacrylate). Optical (transmittance, colour) and mechanical (shore D hardness) properties were measured to describe the degradation process over 35 days. The transmittance of the samples was significantly decreased during the first seven days (12% EMA, 15% MMA). The average lightness of both materials decreased by 10% (EMA) and 17% (MMA), and the colour shifted towards a red and yellow area of CIE L*a*b* space coordinates. The influence of UV-C radiation on the hardness of the samples was statistically insignificant (W+MMA 84.98 ± 2.05; W+EMA 84.89 ± 2.46), therefore the hardness mainly depends on the hardness of used acrylic polymer. The obtained results can be used to assess the effect of disinfection of transparent wood surfaces with UV-C radiation (e.g., due to inactivation of SARS-CoV-2 virus) on the change of its aesthetic and mechanical properties.

Heat release rate (HRR) is the principal fire characteristic of materials. There are three known methods for the measurement of HRR (based on oxygen consumption, mass loss rate, and combustion products temperature rise). The method based on oxygen consumption is considered to be the reference. However, this method is expensive and for a large part of laboratories and universities unavailable. The simplest method is based on combustion products’ temperature rise. However, this method has a fundamental problem with the temperature dependence of the heat capacity of combustion products and the thermal inertia of the measurement system. This problem has been solved by training neural networks to predict molar heat capacity and the amount of substance (chemical amount) flow rate of combustion products in the cone calorimeter exhaust duct. Data were obtained for six different wood species: birch (Betula verrucosa Ehrh.), oak (Quercus robur L.) spruce (Picea abies (L.) H. Karst.), locust (Robinia pseudoacacia L.), poplar (Populus nigra × P. maximowiczii L.), and willow (Salix alba L.) woods at heat fluxes from 25 to 50 kW m⁻² have been used for neural network training. Data from three other wood species iroko (Milicia excelsa (Welw.) C.C. Berg), pine (Pinus sylvestris L.), and paulownia (Paulownia tomentosa (Thunb.) Steud.) woods have been used for testing of trained neural network. The average percentage ratio of the predicted to the true value of HRR (during the test) has been 103.8%. In addition to that, some key average fire characteristics of wood have been determined: critical heat flux 20.7 kW m⁻², effective heat of combustion 14.01 MJ kg⁻¹, and the average value of molar heat capacity of combustion products 0.045 kJ mol⁻¹ K⁻¹.

The aim of the research described in this article was to optimize the basic sulphur process of lignin removal from the raw radially cut basswood (Tilia Cordata) pieces of various thicknesses. Lignin removal took place chemically in several consecutive steps in which the influence of individual parameters was investigated (solutions of NaOH + Na ₂ SO ₃ , KOH + Na ₂ SO ₃ , its concentrations, time of leaching, efficacy of whitening agents, effect of sample washing between individual baths, etc.). Through experiments, it was found that the change of fresh NaOH + Na ₂ SO ₃ solution during the experiment had no significant effect. In contrast, skipping the washing of the samples with boiling distilled water after the hydroxide bath had a significant effect on the rate and efficiency of lignin removal with H ₂ O ₂ in the following step. When comparing the lignin removal efficiency of NaOH + Na ₂ SO ₃ and KOH + Na ₂ SO ₃ , the delignification process was clearly demonstrated to be more effective using the KOH + Na ₂ SO ₃ solution. Application of the above-mentioned procedures has helped to streamline the lignin removal process from solid basswood.

Transparent wood is a modern bio-renewable material with great potential for both science and industrial applications. However, the fire hazard of transparent wood is still almost unexplored. This study aims to investigate the impact of pristine basswood modification to epoxy-based transparent wood on the fire hazard and to train neural networks for the prediction of heat release rate from mass loss rate of pristine basswood, epoxy-based transparent wood, and epoxy resin. Transparent wood was prepared by lignin modification in pristine small-leaved basswood (Tilia cordata Mill.) and subsequent vacuum infiltration by epoxy resin. The fire hazard of the samples was determined by the cone calorimeter at four heat fluxes of 20–50 kW m⁻². The fire hazard of investigated materials was compared based on the critical heat flux, ignition temperature, heat release rate, effective heat of combustion and time to flashover. Transparent wood showed higher resistance to ignition (higher critical heat flux and ignition temperature) than pristine wood. However, other parameters (heat release rate and effective heat of combustion) were higher (worse) and the time to flashover was lower (worse) for transparent wood than for pristine wood. Trained neural networks for predicting heat release rate from the mass loss rate of wood (both pristine and transparent) and epoxy resin showed coefficients of determination from 0.70 to 0.92. Trained neural networks with a coefficient of determinations above 0.90 are usable for low-cost heat release rate measurements in both science and industrial applications.

Electric cables can contribute to the spread of fire through the insulating layer. This paper focuses on their properties characterizing the initiation of fire. Samples of ethylene-based cable insulation were tested using a cone calorimeter by exposing them to external heat flows of six different values (25 kW m ⁻² – 50 kW m ⁻² ). Time to initiate flame burning was observed. The critical heat flux (depending on the method of calculation was in the range 2.94 kW m ⁻² – 4.59 kW m ⁻² ) and the thermal response parameter (342 kW s ^−0.5 m ⁻² ) was calculated from the time of initiation and external heat flow dependence.