Tobias Schnabel

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.

The photocatalytic efficiency of TiO₂ was significantly enhanced by coupling with WO₃ to form a TiO₂/WO₃ heterostructure, designed to operate effectively under UV-LED irradiation. The nanocomposites were synthesized via a hydrothermal route, and their activity was evaluated through the degradation of the pharmaceutical pollutant venlafaxine. Contaminants are rarely addressed in photocatalytic studies. Unlike a simple physical mixture of commercial TiO₂ and WO₃ powders, the hydrothermally synthesized TiO₂/WO₃ photocatalyst exhibited superior efficiency, attributable to its nanoscale dimensions achieved via the hydrothermal route, which promoted improved charge carrier separation, enhanced surface homogeneity, and the formation of an effective heterojunction interface. An optimization study varying the WO₃ content (5, 10, 20, and 30 wt.%) within the TiO₂ revealed that the 10 wt.% WO₃ composition achieved the highest performance, with ~52% venlafaxine degradation within 60 min. SEM, TEM, FTIR, Raman spectroscopy, XRD, and UV-Vis DRS revealed the successful incorporation of WO3 into the TiO₂ matrix, confirming phase purity and composition-dependent structural evolution of the nanocomposite, and evidencing extended light absorption and superior charge-transfer properties. Importantly, the optimized photocatalyst thin film retained excellent stability and reusability, maintaining high degradation efficiency over three consecutive cycles with negligible activity loss, which avoids slurry separation. These findings establish hydrothermally synthesized TiO₂/10%WO₃ thin film heterostructures as effective and sustainable photocatalytic platforms for the removal of pharmaceutical pollutants in wastewater under UV-LED irradiation.

Hexavalent chromium (Cr(VI)) is a prevalent and highly toxic pollutant, posing a serious threat to human health and ecosystems. This study addresses this issue by exploring the photocatalytic reduction of Cr(VI) using hydrothermally synthesized nickel cobaltite (NiCo2O4) spinel–supported films. The research fills the gap for efficient visible light photocatalytic materials for Cr(VI) reduction, with the aim to synthesize, characterize, and assess NiCo2O4-supported films’ photocatalytic activity. Synthesis was achieved via a hydrothermal method on fluorine-doped tin oxide (FTO) and titanium dioxide (TiO2) over FTO substrate. The films were characterized using several techniques and their photocatalytic activity was tested under UV-A and visible light, with Cr(VI) concentration monitored periodically up to 240 min. The NiCo2O4/TiO2 film demonstrated superior photoreduction performance under both UV and visible light radiation compared to TiO2 and NiCo2O4, achieving photoreduction of Cr(VI) by 76% under UV light with a rate constant of 5.79 × 10−3 min−1 and 60% under visible light with a rate constant of 4.74 × 10−3 min−1. In conclusion, hydrothermally synthesized NiCo2O4/TiO2 film shows promising photocatalytic performance for Cr(VI) reduction, marking a significant advancement in photocatalysis and water treatment technologies. Future research will focus on stability assessment, synthesis process optimization, and real-world water treatment application.

The effects of various additives (Y₂O₃, Ga₂O₃, and WO₃) on photocatalytic degradation efficiency under UV light-emitting diodes (LEDs) and the optical properties of TiO₂ Degussa P25 were investigated using ketoprofen and diclofenac, two non-steroidal anti-inflammatory drugs commonly detected in German rivers. Experimental results demonstrated that thin films containing these additives exhibited similar photocatalytic degradation efficiencies as pure TiO₂, achieving a 30% degradation of ketoprofen over 150 min. In contrast, the Y₂O₃/TiO₂ thin film showed significantly improved performance, achieving a 46% degradation of ketoprofen in 180 min. Notably, the Y₂O₃/TiO₂ system was three times more effective in degrading diclofenac compared to pure TiO₂. Additionally, the Y₂O₃/TiO₂ photocatalyst retained its activity over three successive cycles with only a slight decrease in efficiency. The photocatalytic degradation of both organic pollutants followed first-order kinetics with all photocatalysts. The investigation included SEM imaging to assess the surface homogeneity of the thin films and UV-vis solid-state spectroscopy to evaluate the impact of the additives on the energy band gap of TiO₂.

A significant proportion of machine learning methodologies for recommendation systems are grounded in the fundamental principle of matching, utilizing perceptual and similarity-based learning approaches. These methods include both the extraction of features from data through representation learning and the derivation of similarity matching functions via neural function learning. While these models are important for recommendation systems, their foundational design philosophy primarily captures correlational signals within the data. Transitioning from correlation-based learning to causal learning in recommendation systems represents a critical area to explore, as causal models enable extrapolation beyond observational data in both representation learning and ranking tasks. Specifically, causal learning offers potential enhancements to the recommender system community across multiple dimensions, including, but not limited to, explainable, unbiased, fairness-aware, robust, and cognitive reasoning models for recommendation. This special issue is dedicated to exploring the research and practical applications of causal inference within the realms of recommendation and broader ranking scenarios. It has attracted interest from an array of researchers and practitioners on disseminating the latest developments in causal modeling for recommender systems. Moreover, it has attracted the interest of professionals from various fields such as Information Retrieval, Machine Learning, Artificial Intelligence, Natural Language Processing, Data Science, and others.

In this work, the degradation performance for the photocatalytic oxidation of eight micro-pollutants (amisulpride, benzotriazole, candesartan, carbamazepine, diclofenac, gabapentin, methlybenzotriazole, and metoprolol) within real secondary effluent was investigated using three different reactor designs. For all reactor types, the influence of irradiation power on its reaction rate and energetic efficiency was investigated. Flat cell and batch reactor showed almost similar substance specific degradation behavior. Within the immersion rotary body reactor, benzotriazole and methyl-benzotriazole showed a significantly lower degradation affinity. The flat cell reactor achieved the highest mean degradation rate, with half time values ranging from 5 to 64 min with a mean of 18 min, due to its high catalysts surface to hydraulic volume ratio. The EE/O values were calculated for all micro-pollutants as well as the mean degradation rate constant of each experimental step. The lowest substance specific energy per order (EE/O) values of 5 kWh/m³ were measured for benzotriazole within the batch reactor. The batch reactor also reached the lowest mean values (11.8–15.9 kWh/m³) followed by the flat cell reactor (21.0–37.0 kWh/m³) and immersion rotary body reactor (23.9–41.0 kWh/m³). Catalyst arrangement and irradiation power were identified as major influences on the energetic performance of the reactors. Low radiation intensities as well as the use of submerged catalyst arrangement allowed a reduction in energy demand by a factor of 3–4. A treatment according to existing treatment goals of wastewater treatment plants (80% total degradation) was achieved using the batch reactor with a calculated energy demand of 7000 Wh/m³.

Much of the complexity of Recommender Systems (RSs) comes from the fact that they are used as part of more complex applications and affect user experience through a varied range of user interfaces. However, research focused almost exclusively on the ability of RSs to produce accurate item rankings while giving little attention to the evaluation of RS behavior in real-world scenarios. Such narrow focus has limited the capacity of RSs to have a lasting impact in the real world and makes them vulnerable to undesired behavior, such as reinforcing data biases. We propose EvalRS as a new type of challenge, in order to foster this discussion among practitioners and build in the open new methodologies for testing RSs "in the wild".