University of Upper Alsace (French: Université de Haute-Alsace, UHA) is a multidisciplinary teaching and research centre based in the two cities of Mulhouse and Colmar, France. Research and teaching at UHA concentrates mainly on science, technology, economics, management, arts and humanities. In 2017, UHA has more than 8000 students with about a hundred courses offered. The founding of UHA was driven by social and business players, among them was Jean-Baptiste Donnet.The special geographical situation of UHA, which lies close to the Swiss and German borders, is favourable to the emergence of single courses leading to double or triple degrees that are recognized in the neighbouring countries. Together with Albert Ludwigs University of Freiburg, University of Basel, Karlsruhe Institute of Technology, as well as Strasbourg University, the university of Upper Alsace is a member of the EUCOR, which is a trinational cross-border alliance of five universities on the Upper Rhine in the border region between Germany, France and Switzerland.
The role of silica on enhancing diclofenac (DCF) sodium uptake by SBA-supported chitosan (CN-SBA) from aqueous solutions was investigated. For this purpose, the use of SBA-15 and SBA-16-like mesoporous silica as inorganic supports for CN at different proportions resulted in CN-SBA-15 and CN-SBA-16 adsorbents with improved affinity towards DCF as compared to the starting components. These materials were characterized by powder X-ray diffraction (pXRD), nitrogen sorption measurements (BET, BJH) and thermogravimetric analysis (TGA). Their adsorption features for DCF capture were examined through the effects of the solution pH, adsorbent amount and adsorption time on the adsorption process of diclofenac sodium. UV-Vis spectrophotometry revealed fast adsorption kinetics reaching equilibrium with ca. four times higher DCF removal yield by chitosan-loaded SBA-15 and SBA-16 as compared to their bare counterparts. This improvement was explained in terms of additional interactions with the silica surface. The initial pH was found to strongly influence the adsorption capacity, the higher DCF uptakes being registered under acidic conditions, presumably due to a strong contribution of hydrophobic interaction with siloxy groups. Adsorption turned out to fit Langmuir’s model with maximum adsorption capacity in agreement with experimental data. These results open promising prospects for tailored affinity towards DCF according to the silica content.
Time series machine learning (TSML) is a growing research field that spans a wide range of tasks. The popularity of established tasks such as classification, clustering, and extrinsic regression has, in part, been driven by the availability of benchmark datasets. An archive of 30 multivariate time series classification datasets, introduced in 2018 and commonly known as the UEA archive, has since become an essential resource cited in hundreds of publications. We present a substantial expansion of this archive that more than quadruples its size, from 30 to 133 classification problems. We also release preprocessed versions of datasets containing missing values or unequal length series, bringing the total number of datasets to 147. Reflecting the growth of the archive and the broader community, we rebrand it as the Multiverse archive to capture its diversity of domains. The Multiverse archive includes datasets from multiple sources, consolidating other collections and standalone datasets into a single, unified repository. Recognising that running experiments across the full archive is computationally demanding, we recommend a subset of the full archive called Multiverse-core (MV-core) for initial exploration. To support researchers in using the new archive, we provide detailed guidance and a baseline evaluation of established and recent classification algorithms, establishing performance benchmarks for future research. We have created a dedicated repository for the Multiverse archive that provides a common aeon and scikit-learn compatible framework for reproducibility, an extensive record of published results, and an interactive interface to explore the results.
Hydrogen production through proton exchange membrane water electrolysis (PEMWE) is gaining traction due to its efficiency and sustainability. This work presents a comprehensive and experimentally validated multiphysics model integrating electrochemical, thermal, and fluidic dynamics to improve PEMWE performance predictions. The model accounts for key voltage losses and incorporates pressure and temperature effects through a coupled thermo fluidic submodel regulated by a PI controller. A set of polarization curves collected under different thermal and pressure conditions was used to calibrate the electrical submodel, allowing the identification of temperature and pressure dependent parameters that quantify the impact of operating conditions on overpotentials. The electrical behavior is accurately captured using advanced parameter optimization techniques, including PSO, GA, and L-BFGS-B, with L-BFGS-B outperforming the others in terms of convergence speed and fitting precision, resulting in modeling errors below 1%. The model's performance is validated experimentally on 1 kW and 5.5 kW PEMWEs, demonstrating its robustness, scalability, and accuracy. This work contributes to the optimization of PEMWE systems, offering a validated framework for real applications and future integration with renewable energy sources and advanced control strategies.
New data on the structural and catalytic properties of a series of mono-component cobalt and bi-component CoMn samples supported on SBA-15, prepared by different precursors are reported. The catalysts are characterized by SAXS, N2-physisorption, XRD, TEM, TPR, XPS, Uv-vis and FTIR methods and tested in combustion reactions of butane and n-hexane. It was established that the preparation of the Co-SBA-15 catalysts by different precursors does not significantly change the mesoporous structure. Co species are located in the mesopores, and their shrinkage occurs after the calcination to decompose of the cobalt precursor. Depending on the precursor used, different types of oxide phases are formed on the surface of the mesoporous support. Finely divided and weakly interacting with the support Co3O4 and CoSiO3 are formed on the surface of cobalt samples prepared from both precursors. In addition to the mentioned two phases finely dispersed CoO and a silica-like phase are formed on the surface of the one-component sample prepared from cobalt nitrate. The highest activity of the bi-component Co-Mn sample obtained by a mixed solution of an acetate precursor is explained by very low crystallinity of the supported metal oxide phases and their highest reducibility.
In this study, we designed a series of triphenylamine (TPA)-based oxime ester photoinitiators (Guo-1, Guo-2, Guo-3, and Guo-4), incorporating benzene or heterocyclic cores to connect TPA oxime ester units at various substitution positions and numbers in a divergent manner. Specifically, Guo-1 and Guo-2 consist of two TPA-based oxime esters linked at the 1,3-and 1,4-positions of a benzene ring, respectively, while Guo-4 features two TPA-based oxime esters linked at the 2,5-positions of a furan ring. Guo-3, in contrast, carries three TPA-based oxime esters attached at the 1,3,5-positions of a benzene ring. Additionally, the reference compound TP-1, containing a single TPA-based oxime ester, was selected for comparison. Featuring multiple TPA-based oxime ester groups, enhanced aromatic character of these compounds results in molar extinction coefficients that are at least fourfold greater than that of the single-substituted reference compound TP-1. Additionally, the effects of structural variation on their photochemical, electrochemical, and thermal properties, as well as theoretical calculations, were systematically investigated. Notably, at a comparable photoinitiator concentration (0.01 M) (<= 1 Phr) (parts per hundred parts of resin), Guo-3 exhibited the highest photoinitiation efficiency under both LED@365 nm and LED@405 nm, owing to its highest molar extinction coefficient and favorable cleavage energetics in both the singlet state and triplet state. In contrast, TP-1 failed to achieve stable photoreactivity, likely due to its lower molar extinction coefficient under the relatively low concentration of photoinitator applied. Additionally, the Guo-3-based formulation also demonstrated the lowest migration percentage under LED@405 nm. The present work contributes to the fundamental understanding of branching-based oxime esters, offering valuable insights into their molecular design and providing a basis for future development of high-performance photoinitiating systems.
