Naphthochromenones: Organic Bimodal Photocatalysts Engaging in Both Oxidative and Reductive Quenching Processes

Twelve naphthochromenone photocatalysts (PCs) were synthesized on gram scale. They absorb across the UV/Vis range and feature an extremely wide redox window (up to 3.22 eV) that is accessible using simple visible light irradiation sources (CFL or LED). Their excited‐state redox potentials, PC*/PC^.? (up to 1.65 V) and PC^.+/PC* (up to ?1.77 V vs. SCE), are such that these novel PCs can engage in both oxidative and reductive quenching mechanisms with strong thermodynamic requirements. The potential of these bimodal PCs was benchmarked in synthetically relevant photocatalytic processes with extreme thermodynamic requirements. Their ability to efficiently catalyze mechanistically opposite oxidative/reductive photoreactions is a unique feature of these organic photocatalysts, thus representing a decisive advance towards generality, sustainability, and cost efficiency in photocatalysis.     

Synthesis,structural analysis,and photophysical properties of bi-1,2,3-triazoles

Structural Chemistry - Structural insights of a group of bi-1,2,3-triazoles derived from oxidative CuAAC are described through an X-ray crystallography study, distinguishing a dihedral angle which...     

A Promising Thermodynamic Study of Hole Transport Materials to Develop Solar Cells: 1,3-Bis(N-carbazolyl)benzene and 1,4-Bis(diphenylamino)benzene

The thermochemical study of the 1,3-bis(N-carbazolyl)benzene (NCB) and 1,4-bis(diphenylamino)benzene (DAB) involved the combination of combustion calorimetric (CC) and thermogravimetric techniques. The molar heat capacities over the temperature range of (274.15 to 332.15) K, as well as the melting temperatures and enthalpies of fusion were measured for both compounds by differential scanning calorimetry (DSC). The standard molar enthalpies of formation in the crystalline phase were calculated from the values of combustion energy, which in turn were measured using a semi-micro combustion calorimeter. From the thermogravimetric analysis (TGA), the rate of mass loss as a function of the temperature was measured, which was then correlated with Langmuir’s equation to derive the vaporization enthalpies for both compounds. From the combination of experimental thermodynamic parameters, it was possible to derive the enthalpy of formation in the gaseous state of each of the title compounds. This parameter was also estimated from computational studies using the G3MP2B3 composite method. To prove the identity of the compounds, the ¹H and ¹³C spectra were determined by nuclear magnetic resonance (NMR), and the Raman spectra of the study compounds of this work were obtained.     

K-line X-ray fluorescence from highly charged iron ions under dense astrophysical plasma conditions

In the present work, we report an investigation of plasma environment effects on the atomic parameters associated with the K-vacancy states in highly charged iron ions within the astrophysical context of accretion disks around black holes. More particularly, the sensitivity of K-line X-ray fluorescence parameters (wavelengths, radiative transition probabilities, and Auger rates) in Fe XVII–Fe XXV ions has been estimated for plasma conditions characterized by an electron temperature ranging from 10⁵ to 10⁷ K and an electron density ranging from 10¹⁸ to 10²² cm⁻³. In order to do this, relativistic multiconfiguration Dirac-Fock atomic structure calculations have been carried out by considering a time averaged Debye-Hückel potential for both the electron–nucleus and electron–electron interactions.     

A Comparative Analysis of Machine Learning Techniques for Muon Count in UHECR Extensive Air-Showers

The main goal of this work is to adapt a Physics problem to the Machine Learning (ML) domain and to compare several techniques to solve it. The problem consists of how to perform muon count from the signal registered by particle detectors which record a mix of electromagnetic and muonic signals. Finding a good solution could be a building block on future experiments. After proposing an approach to solve the problem, the experiments show a performance comparison of some popular ML models using two different hadronic models for the test data. The results show that the problem is suitable to be solved using ML as well as how critical the feature selection stage is regarding precision and model complexity.     

Machine Learning Algorithm to Predict Acidemia Using Electronic Fetal Monitoring Recording Parameters

Background: Electronic fetal monitoring (EFM) is the universal method for the surveillance of fetal well-being in intrapartum. Our objective was to predict acidemia from fetal heart signal features using machine learning algorithms. Methods: A case–control 1:2 study was carried out compromising 378 infants, born in the Miguel Servet University Hospital, Spain. Neonatal acidemia was defined as pH < 7.10. Using EFM recording logistic regression, random forest and neural networks models were built to predict acidemia. Validation of models was performed by means of discrimination, calibration, and clinical utility. Results: Best performance was attained using a random forest model built with 100 trees. The discrimination ability was good, with an area under the Receiver Operating Characteristic curve (AUC) of 0.865. The calibration showed a slight overestimation of acidemia occurrence for probabilities above 0.4. The clinical utility showed that for 33% cutoff point, missing 5% of acidotic cases, 46% of unnecessary cesarean sections could be prevented. Logistic regression and neural networks showed similar discrimination ability but with worse calibration and clinical utility. Conclusions: The combination of the variables extracted from EFM recording provided a predictive model of acidemia that showed good accuracy and provides a practical tool to prevent unnecessary cesarean sections.     

Production and Digestibility Studies of β-Galactosyl Xylitol Derivatives Using Heterogeneous Catalysts of LacA β-Galactosidase from Lactobacillus Plantarum WCFS1

The synthesis of β-galactosyl xylitol derivatives using immobilized LacA β-galactosidase from Lactobacillus plantarum WCFS1 is presented. These compounds have the potential to replace traditional sugars by their properties as sweetener and taking the advantages of a low digestibility. The enzyme was immobilized on different supports, obtaining immobilized preparations with different activity and stability. The immobilization on agarose-IDA-Zn-CHO in the presence of galactose allowed for the conserving of 78% of the offered activity. This preparation was 3.8 times more stable than soluble. Since the enzyme has polyhistidine tags, this support allowed the immobilization, purification and stabilization in one step. The immobilized preparation was used in synthesis obtaining two main products and a total of around 68 g/L of β-galactosyl xylitol derivatives and improving the synthesis/hydrolysis ratio by around 30% compared to that of the soluble enzyme. The catalyst was recycled 10 times, preserving an activity higher than 50%. The in vitro intestinal digestibility of the main β-galactosyl xylitol derivatives was lower than that of lactose, being around 6 and 15% for the galacto-xylitol derivatives compared to 55% of lactose after 120 min of digestion. The optimal amount immobilized constitutes a very useful tool to synthetize β-galactosyl xylitol derivatives since it can be used as a catalyst with high yield and being recycled for at least 10 more cycles.     

Deformation of canonical morphisms and the moduli of surfaces of general type

In this article we study the deformation of finite maps and show how to use this deformation theory to construct varieties with given invariants in a projective space. Among other things, we prove a criterion that determines when a finite map can be deformed to a one-to-one map. We use this criterion to construct new surfaces of general type with birational canonical map, for different c₁²c_{1}^{2} and χ (the canonical map of the surfaces we construct is in fact a finite, birational morphism). Our general results enable us to describe some new components of the moduli of surfaces of general type. We also find infinitely many moduli spaces having one component whose general point corresponds to a canonically embedded surface and another component whose general point corresponds to a surface whose canonical map is a degree 2 morphism.