Axial-Equatorial Halide Ordering in Layered Hybrid Perovskites from Isotropic-Anisotropic 207 Pb NMR.

Bandgap-tuneable mixed-halide 3D perovskites are of interest for multi-junction solar cells, but suffer from photoinduced spatial halide segregation. Mixed-halide 2D perovskites are more resistant to halide segregation and are promising coatings for 3D perovskite solar cells. The properties of mixed-halide compositions depend on the local halide distribution, which is challenging to study at the level of single octahedra. In particular, it has been suggested that there is a preference for occupation of the distinct axial and equatorial halide sites in mixed-halide 2D perovskites. 207 Pb NMR can be used to probe the atomic-scale structure of lead-halide materials, but although the isotropic 207 Pb shift is sensitive to halide stoichiometry, it cannot distinguish configurational isomers. Here, we use 2D isotropic-anisotropic correlation 207 Pb NMR and relativistic DFT calculations to distinguish the [PbX6 ] configurations in mixed iodide-bromide 3D FAPb(Br1-x Ix )3 perovskites and 2D BA2 Pb(Br1-x Ix )4 perovskites based on formamidinium (FA+ ) and butylammonium (BA+ ), respectively. We find that iodide preferentially occupies the axial site in BA-based 2D perovskites, which may explain the suppressed halide mobility.

Atomic-level structure determination of amorphous molecular solids by NMR.

Structure determination of amorphous materials remains challenging, owing to the disorder inherent to these materials. Nuclear magnetic resonance (NMR) powder crystallography is a powerful method to determine the structure of molecular solids, but disorder leads to a high degree of overlap between measured signals, and prevents the unambiguous identification of a single modeled periodic structure as representative of the whole material. Here, we determine the atomic-level ensemble structure of the amorphous form of the drug AZD4625 by combining solid-state NMR experiments with molecular dynamics (MD) simulations and machine-learned chemical shifts. By considering the combined shifts of all 1H and 13C atomic sites in the molecule, we determine the structure of the amorphous form by identifying an ensemble of local molecular environments that are in agreement with experiment. We then extract and analyze preferred conformations and intermolecular interactions in the amorphous sample in terms of the stabilization of the amorphous form of the drug.

Chemical Shift-Dependent Interaction Maps in Molecular Solids.

Structure determination of molecular solids through NMR crystallography relies on the generation of a comprehensive set of candidate crystal structures and on the comparison of chemical shifts computed for those candidates with experimental values. Exploring the polymorph landscape of molecular solids requires extensive computational power, which leads to a significant bottleneck in the generation of the set of candidate crystals by crystal structure prediction (CSP) protocols. Here, we use a database of crystal structures with associated chemical shifts to construct three-dimensional interaction maps in molecular crystals directly derived from a molecular structure and its associated set of experimentally measured chemical shifts. We show how the maps obtained can be used to identify structural constraints for accelerating CSP protocols and to evaluate the likelihood of candidate crystal structures without requiring DFT-level chemical shift computations.

Light-Induced Halide Segregation in 2D and Quasi-2D Mixed-Halide Perovskites.

Photoinduced halide segregation hinders widespread application of three-dimensional (3D) mixed-halide perovskites. Much less is known about this phenomenon in lower-dimensional systems. Here, we study photoinduced halide segregation in lower-dimensional mixed iodide-bromide perovskites (PEA2MA n-1Pb n (Br x I1-x )3n+1, with PEA+: phenethylammonium and MA+: methylammonium) through time-dependent photoluminescence (PL) spectroscopy. We show that layered two-dimensional (2D) structures render additional stability against the demixing of halide phases under illumination. We ascribe this behavior to reduced halide mobility due to the intrinsic heterogeneity of 2D mixed-halide perovskites, which we demonstrate via 207Pb solid-state NMR. However, the dimensionality of the 2D phase is critical in regulating photostability. By tracking the PL of multidimensional perovskite films under illumination, we find that while halide segregation is largely inhibited in 2D perovskites (n = 1), it is not suppressed in quasi-2D phases (n = 2), which display a behavior intermediate between 2D and 3D and a peculiar absence of halide redistribution in the dark that is only induced at higher temperature for the quasi-2D phase.

Two-dimensional Pure Isotropic Proton Solid State NMR.

One key bottleneck of solid-state NMR spectroscopy is that 1 H NMR spectra of organic solids are often very broad due to the presence of a strong network of dipolar couplings. We have recently suggested a new approach to tackle this problem. More specifically, we parametrically mapped errors leading to residual dipolar broadening into a second dimension and removed them in a correlation experiment. In this way pure isotropic proton (PIP) spectra were obtained that contain only isotropic shifts and provide the highest 1 H NMR resolution available today in rigid solids. Here, using a deep-learning method, we extend the PIP approach to a second dimension, and for samples of L-tyrosine hydrochloride and ampicillin we obtain high resolution 1 H-1 H double-quantum/single-quantum dipolar correlation and spin-diffusion spectra with significantly higher resolution than the corresponding spectra at 100 kHz MAS, allowing the identification of previously overlapped isotropic correlation peaks.

Pure Isotropic Proton NMR Spectra in Solids using Deep Learning.

The resolution of proton solid-state NMR spectra is usually limited by broadening arising from dipolar interactions between spins. Magic-angle spinning alleviates this broadening by inducing coherent averaging. However, even the highest spinning rates experimentally accessible today are not able to completely remove dipolar interactions. Here, we introduce a deep learning approach to determine pure isotropic proton spectra from a two-dimensional set of magic-angle spinning spectra acquired at different spinning rates. Applying the model to 8 organic solids yields high-resolution 1 H solid-state NMR spectra with isotropic linewidths in the 50-400 Hz range.

Atomic-Level Structure of Zinc-Modified Cementitious Calcium Silicate Hydrate.

It has recently been demonstrated that the addition of zinc can enhance the mechanical strength of tricalcium silicates (C3S) upon hydration, but the structure of the main hydration product of cement, calcium silicate hydrate (C-S-H), in zinc-modified formulations remains unresolved. Here, we combine 29Si DNP-enhanced solid-state nuclear magnetic resonance (NMR), density functional theory (DFT)-based chemical shift computations, and molecular dynamics (MD) modeling to determine the atomic-level structure of zinc-modified C-S-H. The structure contains two main new silicon species (Q(1,Zn) and Q(2p,Zn)) where zinc substitutes Q(1) silicon species in dimers and bridging Q(2b) silicon sites, respectively. Structures determined as a function of zinc content show that zinc promotes an increase in the dreierketten mean chain lengths.

A Machine Learning Model of Chemical Shifts for Chemically and Structurally Diverse Molecular Solids.

Nuclear magnetic resonance (NMR) chemical shifts are a direct probe of local atomic environments and can be used to determine the structure of solid materials. However, the substantial computational cost required to predict accurate chemical shifts is a key bottleneck for NMR crystallography. We recently introduced ShiftML, a machine-learning model of chemical shifts in molecular solids, trained on minimum-energy geometries of materials composed of C, H, N, O, and S that provides rapid chemical shift predictions with density functional theory (DFT) accuracy. Here, we extend the capabilities of ShiftML to predict chemical shifts for both finite temperature structures and more chemically diverse compounds, while retaining the same speed and accuracy. For a benchmark set of 13 molecular solids, we find a root-mean-squared error of 0.47 ppm with respect to experiment for 1H shift predictions (compared to 0.35 ppm for explicit DFT calculations), while reducing the computational cost by over four orders of magnitude.

De Novo Crystal Structure Determination from Machine Learned Chemical Shifts.

Determination of the three-dimensional atomic-level structure of powdered solids is one of the key goals in current chemistry. Solid-state NMR chemical shifts can be used to solve this problem, but they are limited by the high computational cost associated with crystal structure prediction methods and density functional theory chemical shift calculations. Here, we successfully determine the crystal structures of ampicillin, piroxicam, cocaine, and two polymorphs of the drug molecule AZD8329 using on-the-fly generated machine-learned isotropic chemical shifts to directly guide a Monte Carlo-based structure determination process starting from a random gas-phase conformation.

Litter Detection with Deep Learning: A Comparative Study.

Pollution in the form of litter in the natural environment is one of the great challenges of our times. Automated litter detection can help assess waste occurrences in the environment. Different machine learning solutions have been explored to develop litter detection tools, thereby supporting research, citizen science, and volunteer clean-up initiatives. However, to the best of our knowledge, no work has investigated the performance of state-of-the-art deep learning object detection approaches in the context of litter detection. In particular, no studies have focused on the assessment of those methods aiming their use in devices with low processing capabilities, e.g., mobile phones, typically employed in citizen science activities. In this paper, we fill this literature gap. We performed a comparative study involving state-of-the-art CNN architectures (e.g., Faster RCNN, Mask-RCNN, EfficientDet, RetinaNet and YOLO-v5), two litter image datasets and a smartphone. We also introduce a new dataset for litter detection, named PlastOPol, composed of 2418 images and 5300 annotations. The experimental results demonstrate that object detectors based on the YOLO family are promising for the construction of litter detection solutions, with superior performance in terms of detection accuracy, processing time, and memory footprint.

Manifestaciones oculares de la toxocariasis en escolares del estado Anzoátegui en Venezuela / Ocular manifestations of toxocariasis in schoolchildren from the state from the Anzoátegui state in Venezuela

RESUMEN El objetivo de este estudio fue determinar manifestaciones oculares de la toxocariasis en escolares. Se realizó un estudio en dos escuelas del estado Anzoátegui en Venezuela en el 2019. Se empleó la prueba de ELISA para determinar los anticuerpos IgG contra Toxocara spp. Las familias completaron un cuestionario y los niños fueron evaluados clínicamente por pediatras y oftalmólogos. Participaron 118 niños, el 18,6% presentó anticuerpos anti-Toxocara spp. Las manifestaciones clínicas con asociación estadísticamente significativa fueron las reacciones alérgicas, epífora y disminución de la agudeza visual. En la evaluación oftalmológica se encontró queratitis, uveítis, iritis, granuloma retiniano, endoftalmitis, amaurosis, leucocoria, desprendimiento de retina y endotropía. Los hallazgos muestran una alta frecuencia de enfermedad ocular en niños con toxocariasis de un estado de Venezuela.

ABSTRACT The objective of this study was to determine ocular manifestations of toxocariasis in schoolchildren. A study was conducted in two schools in the Anzoátegui state in Venezuela in 2019. The ELISA test was used to determine IgG antibodies against Toxocara spp. The families completed a questionnaire, and the children were clinically evaluated by pediatricians and ophthalmologists. 118 children participated, 18.6% presented anti-Toxocara spp. The clinical manifestations with a statistically significant association were allergic reactions, epiphora, and decreased visual acuity. The ophthalmological evaluation found keratitis, uveitis, iritis, retinal granuloma, endophthalmitis, amaurosis, leukocoria, retinal detachment and endotropia. The findings show a high frequency of eye disease in children with toxocariasis from a state of Venezuela.

Bayesian probabilistic assignment of chemical shifts in organic solids.

A prerequisite for NMR studies of organic materials is assigning each experimental chemical shift to a set of geometrically equivalent nuclei. Obtaining the assignment experimentally can be challenging and typically requires time-consuming multidimensional correlation experiments. An alternative solution for determining the assignment involves statistical analysis of experimental chemical shift databases, but no such database exists for molecular solids. Here, by combining the Cambridge Structural Database with a machine learning model of chemical shifts, we construct a statistical basis for probabilistic chemical shift assignment of organic crystals by calculating shifts for more than 200,000 compounds, enabling the probabilistic assignment of organic crystals directly from their two-dimensional chemical structure. The approach is demonstrated with the 13C and 1H assignment of 11 molecular solids with experimental shifts and benchmarked on 100 crystals using predicted shifts. The correct assignment was found among the two most probable assignments in more than 80% of cases.

Structure determination of an amorphous drug through large-scale NMR predictions.

Knowledge of the structure of amorphous solids can direct, for example, the optimization of pharmaceutical formulations, but atomic-level structure determination in amorphous molecular solids has so far not been possible. Solid-state nuclear magnetic resonance (NMR) is among the most popular methods to characterize amorphous materials, and molecular dynamics (MD) simulations can help describe the structure of disordered materials. However, directly relating MD to NMR experiments in molecular solids has been out of reach until now because of the large size of these simulations. Here, using a machine learning model of chemical shifts, we determine the atomic-level structure of the hydrated amorphous drug AZD5718 by combining dynamic nuclear polarization-enhanced solid-state NMR experiments with predicted chemical shifts for MD simulations of large systems. From these amorphous structures we then identify H-bonding motifs and relate them to local intermolecular complex formation energies.

Nanoscale Phase Segregation in Supramolecular π-Templating for Hybrid Perovskite Photovoltaics from NMR Crystallography.

The use of layered perovskites is an important strategy to improve the stability of hybrid perovskite materials and their optoelectronic devices. However, tailoring their properties requires accurate structure determination at the atomic scale, which is a challenge for conventional diffraction-based techniques. We demonstrate the use of nuclear magnetic resonance (NMR) crystallography in determining the structure of layered hybrid perovskites for a mixed-spacer model composed of 2-phenylethylammonium (PEA+) and 2-(perfluorophenyl)ethylammonium (FEA+) moieties, revealing nanoscale phase segregation. Moreover, we illustrate the application of this structure in perovskite solar cells with power conversion efficiencies that exceed 21%, accompanied by enhanced operational stability.

[Ocular manifestations of toxocariasis in schoolchildren from the state from the Anzoátegui state in Venezuela]. / Manifestaciones oculares de la toxocariasis en escolares del estado Anzoátegui en Venezuela.

The objective of this study was to determine ocular manifestations of toxocariasis in schoolchildren. A study was conducted in two schools in the Anzoátegui state in Venezuela in 2019. The ELISA test was used to determine IgG antibodies against Toxocara spp. The families completed a questionnaire, and the children were clinically evaluated by pediatricians and ophthalmologists. 118 children participated, 18.6% presented anti-Toxocara spp. The clinical manifestations with a statistically significant association were allergic reactions, epiphora, and decreased visual acuity. The ophthalmological evaluation found keratitis, uveitis, iritis, retinal granuloma, endophthalmitis, amaurosis, leukocoria, retinal detachment and endotropia. The findings show a high frequency of eye disease in children with toxocariasis from a state of Venezuela.

El objetivo de este estudio fue determinar manifestaciones oculares de la toxocariasis en escolares. Se realizó un estudio en dos escuelas del estado Anzoátegui en Venezuela en el 2019. Se empleó la prueba de ELISA para determinar los anticuerpos IgG contra Toxocara spp. Las familias completaron un cuestionario y los niños fueron evaluados clínicamente por pediatras y oftalmólogos. Participaron 118 niños, el 18,6% presentó anticuerpos anti-Toxocara spp. Las manifestaciones clínicas con asociación estadísticamente significativa fueron las reacciones alérgicas, epífora y disminución de la agudeza visual. En la evaluación oftalmológica se encontró queratitis, uveítis, iritis, granuloma retiniano, endoftalmitis, amaurosis, leucocoria, desprendimiento de retina y endotropía. Los hallazgos muestran una alta frecuencia de enfermedad ocular en niños con toxocariasis de un estado de Venezuela.

Open and Closed Radicals: Local Geometry around Unpaired Electrons Governs Magic-Angle Spinning Dynamic Nuclear Polarization Performance.

The development of magic-angle spinning dynamic nuclear polarization (MAS DNP) has allowed atomic-level characterization of materials for which conventional solid-state NMR is impractical due to the lack of sensitivity. The rapid progress of MAS DNP has been largely enabled through the understanding of rational design concepts for more efficient polarizing agents (PAs). Here, we identify a new design principle which has so far been overlooked. We find that the local geometry around the unpaired electron can change the DNP enhancement by an order of magnitude for two otherwise identical conformers. We present a set of 13 new stable mono- and dinitroxide PAs for MAS DNP NMR where this principle is demonstrated. The radicals are divided into two groups of isomers, named open (O-) and closed (C-), based on the ring conformations in the vicinity of the N-O bond. In all cases, the open conformers exhibit dramatically improved DNP performance as compared to the closed counterparts. In particular, a new urea-based biradical named HydrOPol and a mononitroxide O-MbPyTol yield enhancements of 330 ± 60 and 119 ± 25, respectively, at 9.4 T and 100 K, which are the highest enhancements reported so far in the aqueous solvents used here. We find that while the conformational changes do not significantly affect electron spin-spin distances, they do affect the distribution of the exchange couplings in these biradicals. Electron spin echo envelope modulation (ESEEM) experiments suggest that the improved performance of the open conformers is correlated with higher solvent accessibility.

Single-Hessian thawed Gaussian approximation.

To alleviate the computational cost associated with on-the-fly ab initio semiclassical calculations of molecular spectra, we propose the single-Hessian thawed Gaussian approximation in which the Hessian of the potential energy at all points along an anharmonic classical trajectory is approximated by a constant matrix. The spectra obtained with this approximation are compared with the exact quantum spectra of a one-dimensional Morse potential and with the experimental spectra of ammonia and quinquethiophene. In all cases, the single-Hessian version performs almost as well as the much more expensive on-the-fly ab initio thawed Gaussian approximation and significantly better than the global harmonic schemes. Remarkably, unlike the thawed Gaussian approximation, the proposed method conserves energy exactly, despite the time dependence of the corresponding effective Hamiltonian, and, in addition, can be mapped to a higher-dimensional time-independent classical Hamiltonian system. We also provide a detailed comparison with several related approximations used for accelerating prefactor calculations in semiclassical simulations.

Prevalencia de sobrepeso y obesidad, según índice de masa corporal, en la escuela Bilingüe Interamericana de Cuenca en el año lectivo 2007 – 2008 y factores asociados / Prevalence of overweight and obesity according to BMI, the American Bilingual School of Cuenca in the academic year 2007 - 2008 and associated factors

Con un diseño descriptivo de corte transversal se incluyeron 530 escolares: 295 mujeres (55,7%) y 235 varones (44,3%) en los que se calculó la prevalencia de sobrepeso y obesidad y sus factores asociados. El IMC fue calculado mediante tablas de distribución percentilar del Centro Nacional de Estadísticas de Salud de los EUA y los factores asociados mediante Razón de Prevalencias (RP) con un intervalo de confianza del 95% (IC95%). la prevalencia de sobrepeso fue del 18,9% y de obesidad del 15,3%. El sobrepeso en las mujeres fue del 7,5% y en los varones del 11,3%. La obesidad fue del 4,5% en las mujeres y del 10,8% en los varones. Los factores asociados al sobrepeso fueron: la edad de 10 a 12 años, RP = 1,3 (IC95% 0,9 – 1,9); sexo masculino RP = 2,2 (IC95% 1,6 – 3,2); antecedentes de padres obesos RP = 1,4 (IC95% 0,3 – 5,5); condiciones económicas regulares RP = 1,3 (IC95% 0,9 – 1,9); hábitos nutricionales no saludables RP = 20,8 (IC95% 11,8 – 36,6) y poca actividad física RP 1,03 (IC95% 0,6 – 1.6). Los factores asociados a la obesidad fueron: la edad de 5 a 6 años, RP = 1,6 (IC95% 1,0 – 2,4); sexo masculino RP = 3,4 (IC95% 2,2 – 5,3); antecedentes de padres obesos RP = 1,1 (IC95% 0,3 – 3,5); las condiciones económicas regulares RP = 1,2 (IC95% 0,8 – 1,8) y los hábitos nutricionales no saludables RP = 235,5 (IC95% 33,1 – 1672). La asociación con hábitos nutricionales no saludables y sexo masculino fue significativa tanto en el sobrepeso como en la obesidad (P = 0,0001).

With a cross-sectional study 530 scholars were included: 295 women (55,7%) and 235 males (44,3%) in those was calculated the prevalence of overweight and obesity and their associate factors. The IMC was calculated by means of charts of distribution percentilar of the National Center of Statistical of Health of the USA and the associate factors by means of Reason of Prevalence’s (RP) with an interval of trust of 95% (IC95%). prevalence overweight was 18,9% and of obesity 15,3%. The overweight in the women was of 7,5% and in the males of 11,3%. The obesity was of 4,5% in the women and of 10,8% in the males. the factors associated to the overweight were the age from 10 to 12 years, RP = 1,3 (IC95% 0,9 - 1,9); masculine sex RP = 2,2 (IC95% 1,6 - 3,2); obese parents' antecedents RP = 1,4 (IC95% 0,3 - 5,5); you condition economic regular RP = 1,3 (IC95% 0,9 - 1,9); nutritional habits not healthy RP = 20,8 (IC95% 11,8 - 36,6) and little physical activity RP 1,03 (IC95% 0,6 - 1.6). the factors associated to the obesity were the age from 5 to 6 years, RP = 1,6 (IC95% 1,0 - 2,4); masculine sex RP = 3,4 (IC95% 2,2 - 5,3); obese parents' antecedents RP = 1,1 (IC95% 0,3 - 3,5); the regular economic conditions RP = 1,2 (IC95% 0,8 - 1,8) and the nutritional habits not healthy RP = 235,5 (IC95% 33,1 - 1672). the association with nutritional habits not healthy and masculine sex was significant as much in the overweight as in the obesity (P = 0,0001).