Peter Kurzweil

The detection of organic carbon in wastewater is essential for process monitoring and regulatory assessment. Yet conventional chemical oxygen demand (COD) and total organic carbon (TOC) methods remain reagent-dependent, slow, and unsuitable for inline operation. Photoelectrochemical (PEC) sensing based on TiO₂ offers a reagent-free alternative, but its response to wastewater-relevant dissolved organic matter (DOM) and real effluent matrices is still poorly understood. In this study, a TiO₂-based PEC system was systematically evaluated using four representative model compounds—glucose, potassium hydrogen phthalate, L-tryptophan, and urea—covering major fractions typically present in municipal wastewater. For the first time, representative wastewater-associated organic compound classes, conductivity effects, and the transferability of the PEC response to real wastewater effluent were systematically investigated. The photocurrent response showed distinct, highly linear concentration–signal relationships for each substance, suggesting a dominant contribution of surface-associated electronic effects. Conductivity variations across a relevant range had no measurable influence on sensitivity or photocurrent magnitude, indicating that the PEC response is not governed by bulk ionic transport but primarily is an interfacial process at the site of TiO₂. When applied to real wastewater effluent, the sensor exhibited an excellent linear correlation with dilution level (R² = 0.9954), demonstrating a linear response within a defined matrix and an LOD of 1.12 mg L⁻¹ COD. For the investigated model compounds, LOD values ranged from 1.06 to 3.00 mg L⁻¹ COD, while a linear response was maintained up to approximately 80–100 mg L⁻¹ COD. These findings establish TiO₂-based PEC sensing as a promising platform for the reagent-free, online monitoring of organic loads in wastewater treatment.

Unsaturated polyester resin (UPR)–quartz composites have become increasingly important in structural, sanitary, and architectural applications. However, their manufacturing processes still rely heavily on empirical knowledge. This review compiles recent developments in materials science, curing kinetics, and digital manufacturing, outlining a pathway toward data-driven, adaptive production of quartz-filled thermosets. The chemical and physical fundamentals of UPR polymerization are summarized, including the influence of initiator systems, filler characteristics, and thermal management on network formation. Challenges associated with highly filled formulations—such as viscosity control, dispersion, shrinkage, and exothermic peak prediction—are discussed in detail. Recent advances in digital twins (DTs) and artificial intelligence (AI) are reviewed, demonstrating how physics-based simulations, machine learning models, and hybrid mechanistic–data-driven approaches improve the prediction of rheology, curing behavior, and quality outcomes in thermoset polymer processes. A practical application example demonstrates the prediction of peak time in quartz–UPR composites using Random Forest and Gradient Boosting ensemble models. Two prediction scenarios are evaluated: Scenario A with gel time by Leave-One-Out cross-validation, and Scenario B without gel time, representing post-mixing and pre-process prediction contexts, respectively. Stratified bootstrap augmentation improves Gradient Boosting in both scenarios. Principal component analysis confirms that the curing process is governed by three independent physical dimensions: curing reactivity, thermal environment and resin thermal state.