Processes

The selective identification of CH₄ in alkane gas mixtures remains challenging due to overlapping infrared absorption spectra among alkane species. This study introduces a novel algorithmic filter paradigm that fundamentally shifts from hardware-based to software-defined selectivity in Nondispersive Infrared (NDIR) sensing. Instead of relying on costly, fixed-wavelength optical filters, we employ a simplified four-source NDIR platform that deliberately captures composite spectral signals from mixed gases. A CNN-SVM hybrid model then serves as the algorithmic filter: the Convolutional Neural Network extracts discriminative features from overlapping spectra, while the Support Vector Machine performs robust classification. This integrated system achieved 89% accuracy in CH₄ identification within complex alkane mixtures. By replacing expensive optical components with intelligent algorithms, this work demonstrates a cost-effective, flexible, and scalable approach.

For the first time, Eriolobus trilobatus bark from Turkey has been characterized in terms of its chemical, extractive, fuel, and ash characteristics using SEM–EDS, wet chemical analysis, phenolic analysis, FT-IR, TGA, XRF, XRD, BET surface area measurement, proximate analysis, and ash fusion temperature (AFT) determination. The results showed that the bark contains 13% ash, dominated by calcium oxalate, and 15% extractives, largely composed of polar phenolic compounds with moderate radical-scavenging potential. Thermal decomposition of bark proceeds in four distinct stages, associated with the sequential degradation of extractives/hemicelluloses, cellulose, lignin/suberin, and inorganic fractions. The higher calorific value of 14.9 MJ/kg indicates moderate fuel quality compared with conventional woody biomass. Ash is mesoporous with a CaO-rich structure highly suitable for catalytic applications in biodiesel production and biomass gasification. Ash fusion analysis revealed a high flow temperature (1452 °C), indicating a very low slagging risk during thermochemical conversion. Overall, E. trilobatus bark is a promising material for value-added biorefinery pathways, enabling processes for the production of biochars, CaO-based catalysts, phenolic extracts, and sustainable energy. The valorization of E. trilobatus bark not only enhances the economic potential of forestry residues but also provides environmental co-benefits through carbon soil amendment and landscape applications.

Medicinal and aromatic plants continue to attract attention as rich sources of natural bioactive compounds with potential health benefits. Among them, Greek mountain tea (Sideritis scardica) is widely recognized for its high content of phytochemicals, which have been associated with various biological activities. In this study, Natural Deep Eutectic Solvents (NADESs) were investigated as a sustainable and efficient alternative to conventional solvents for the extraction of such compounds, aiming to the development of a more efficient extraction process. Six task-specific designed NADESs were prepared and evaluated for their extraction efficiency, based on the Total Phenolic Content (TPC) and Total Flavonoid Content (TFC) of the extract. The most promising NADES, comprising betaine and 1,3-propandeiol, was selected for process optimization using a Box–Behnken design and key extraction parameters were systematically examined to maximize TPC and TFC. The extract obtained under the proposed conditions (S/L = 20 mg/g, 240 min, 40% water as cosolvent) exhibited a TPC and TFC value of 49.2 mg_GAE/g and 45.9 mg_CAE/g, respectively, approximately two times higher than the values of a hydroethanolic extract, obtained under the same conditions (TPC = 26.6 mg_GAE/g, TF = 19.9 mg_CAE/g). The optimum extract was further analyzed using HPLC-DAD to determine its phytochemical profile and was compared with the conventional hydroethanolic extract, revealing the role of the selected media on the extracted compounds.