Test and Modelling of Solid Oxide Fuel Cell Durability: A Focus on Interconnect Role on Global Degradation

High-temperature fuel cells are a promising technology due to their high energy efficiency and low environmental impacts compared to conventional engines. Nevertheless, they have a limited lifetime which reduces the use to a few application fields. Among them, Solid Oxide Fuel Cells (SOFCs) have had a recent development at the industrial level in two possible configurations: an-ode-and electrolyte-supported design. Considering the impossibility to experimentally distinguish the effects of every degradation mechanism on global cell performance, each layer should be tested singularly through ex situ tests and then assembled into a virgin cell to evaluate its role on the whole system by in situ tests. However, this procedure results as quite complex, and some further micro-structural changes could occur during cell sintering. In order to overcome these constraints, the proposed approach paired ex situ experimental observations on a single element with modelling results on global SOFC. As a case study, CoMnO/Crofer22 APU and CuMnO/AISI 441 interconnect samples were tested, measuring their resistance variation for some hundreds of hours, followed by a detailed post-mortem microstructural analysis. Based on a previously validated local model, SIMFC (SIMulation of Fuel Cells), the durability of commercial anode-and electrolyte-supported cells was simulated, adding specific degradation functions only for the interconnects in order to highlight their influence on SOFC performance

Epilepsy, hippocampal sclerosis and febrile seizures linked by common genetic variation around SCN1A

Epilepsy comprises several syndromes, amongst the most common being mesial temporal lobe epilepsy with hippocampal sclerosis. Seizures in mesial temporal lobe epilepsy with hippocampal sclerosis are typically drug-resistant, and mesial temporal lobe epilepsy with hippocampal sclerosis is frequently associated with important co-morbidities, mandating the search for better understanding and treatment. The cause of mesial temporal lobe epilepsy with hippocampal sclerosis is unknown, but there is an association with childhood febrile seizures. Several rarer epilepsies featuring febrile seizures are caused by mutations in SCN1A, which encodes a brain-expressed sodium channel subunit targeted by many anti-epileptic drugs. We undertook a genome-wide association study in 1018 people with mesial temporal lobe epilepsy with hippocampal sclerosis and 7552 control subjects, with validation in an independent sample set comprising 959 people with mesial temporal lobe epilepsy with hippocampal sclerosis and 3591 control subjects. To dissect out variants related to a history of febrile seizures, we tested cases with mesial temporal lobe epilepsy with hippocampal sclerosis with (overall n = 757) and without (overall n = 803) a history of febrile seizures. Meta-analysis revealed a genome-wide significant association for mesial temporal lobe epilepsy with hippocampal sclerosis with febrile seizures at the sodium channel gene cluster on chromosome 2q24.3 [rs7587026, within an intron of the SCN1A gene, P = 3.36 × 10(-9), odds ratio (A) = 1.42, 95% confidence interval: 1.26-1.59]. In a cohort of 172 individuals with febrile seizures, who did not develop epilepsy during prospective follow-up to age 13 years, and 6456 controls, no association was found for rs7587026 and febrile seizures. These findings suggest SCN1A involvement in a common epilepsy syndrome, give new direction to biological understanding of mesial temporal lobe epilepsy with hippocampal sclerosis with febrile seizures, and open avenues for investigation of prognostic factors and possible prevention of epilepsy in some children with febrile seizures

Synthesis of medium-sized N-heterocycles through RCM of Fischer-type hydrazino carbene complexes

The synthesis of seven-, eight- and nine-membered heterocyclic rings was realized through the RCM reactions, with the Grubbs catalyst, of suitable prepd. Fischer hydrazino carbene complexe

A convenient procedure for the synthesis of tetrathia-[7]-helicene and the selective \u3b1-functionalization of its terminal thiophene ring

This paper describes a convenient preparation of tetrathia-[7]-helicene (TH[7]), the generation of the \u3b1-anion on the terminal thiophene ring, and the synthesis of the 2-formyl-tetrathia-[7]-helicene (2-CHO-TH[7]). The key intermediate trans-1,2-dibenzodithiophene-ethene, prepared in 97% yield by McMurry coupling of the 2-formyl-benzo[1,2-b;4,3-b\u2032]dithiophene, was transformed into TH[7] using a known procedure. The described method affords TH[7] in 46% overall yield, which is more than four times the yield previously reported in the literature. The \u3b1-anion of TH[7], which is easily generated on the \u3b1-position of one of the terminal thiophene rings, reacts with electrophilic reagents such as D2O and DMF. The latter reaction proved to be the best way to prepare 2-CHO-TH[7], a key intermediate for the preparation of new substituted heterohelicenes

A novel three-component reaction toward dihydrooxazolopyridines

Isocyano dihydropyridones accessible via a recently reported multicomponent reaction react with aldehydes and amines to afford dihydrooxazolopyridines in high yield. The scope and limitations of this novel multicomponent reaction were investigated. The efficient combination of two highly variable multicomponent reactions allows the construction of a very broad range of dihydrooxazolopyridines, an unexplored class of bicyclic compounds. The implications of the observed reactivity profile for the mechanism of this multicomponent reaction are discussed

A Novel Three-Component Reaction toward Dihydrooxazolopyridines

Isocyano dihydropyridones accessible via a recently reported multicomponent reaction react with aldehydes and amines to afford dihydrooxazolopyridines in high yield. The scope and limitations of this novel multicomponent reaction were investigated. The efficient combination of two highly variable multicomponent reactions allows the construction of a very broad range of dihydrooxazolopyridines, an unexplored class of bicyclic compounds. The implications of the observed reactivity profile for the mechanism of this multicomponent reaction are discussed

Synthesis of Conformationally Constrained Peptidomimetics using Multicomponent Reactions

A novel modular synthetic approach toward constrained peptidomimetics is reported. The approach involves a highly efficient three-step sequence including two multicomponent reactions, thus allowing unprecedented diversification of both the peptide moieties and the turn-inducing scaffold. The turn-inducing properties of the dihydropyridone scaffold were evaluated by molecular modeling, X-ray crystallography, and NMR studies of a resulting peptidomimetic. Although modeling studies point toward a type IV β-turn-like structure, the X-ray crystal structure and NMR studies indicate an open turn structure

A framework for enhancing industrial soft sensor learning models

Refinery industrial processes are very complex with nonlinear dynamics resulting from varying feedstock characteristics and also from changes in product prioritization. Along these processes, there are key properties of intermediate compounds that must be monitored and controlled since they directly affect the quality of the end products commercialized by these manufacturers. However, most of these properties can only be measured through time-consuming and expensive laboratory analysis, which is impossible to obtain in high frequencies, as required to properly monitor them. In this sense, developing soft sensors is the most common way to obtain high-frequency estimations for these measurements, helping advanced control systems to establish the correct setpoints for temperatures, pressures, and other sensors along the refining process, controlling the quality of end products. Since the amount of labeled data is scarce, most academic research has focused on employing semi- supervised learning strategies to develop machine learning (ML) models as soft sensors. Our research, on the other hand, goes in another direction. We aim to elaborate a framework that leverages the knowledge of domain experts and employs data augmentation techniques to build an enhanced fully labeled dataset that could be fed to any supervised ML algorithm to generate a quality soft sensor. We applied our framework together with Automated ML to train a model capable of predicting a specific key property associated with the production of Naphtha compounds in a refinery: the ASTM 95% distillation temperature of the Heavy Naphtha. Although our framework is model agnostic, we opted by using Automated ML for the optimization strategy, since it applies a diverse set of models to the dataset, reducing the bias of utilizing a single optimization algorithm. We evaluated the proposed framework on a case study carried out in an industrial refinery in Brazil, where the previous model in production for estimating the ASTM 95% distillation temperature of the Heavy Naphtha was based entirely on the physicochemical knowledge of the process. By adopting our framework with Automated ML, we were capable of improving the R2 score by 120%. The resulting ML model is currently operating in real-time inside the refinery, leading to significant economic gains