Pseudo-halide anion engineering for α-FAPbI3 perovskite solar cells.

Metal halide perovskites of the general formula ABX3-where A is a monovalent cation such as caesium, methylammonium or formamidinium; B is divalent lead, tin or germanium; and X is a halide anion-have shown great potential as light harvesters for thin-film photovoltaics1-5. Among a large number of compositions investigated, the cubic α-phase of formamidinium lead triiodide (FAPbI3) has emerged as the most promising semiconductor for highly efficient and stable perovskite solar cells6-9, and maximizing the performance of this material in such devices is of vital importance for the perovskite research community. Here we introduce an anion engineering concept that uses the pseudo-halide anion formate (HCOO-) to suppress anion-vacancy defects that are present at grain boundaries and at the surface of the perovskite films and to augment the crystallinity of the films. The resulting solar cell devices attain a power conversion efficiency of 25.6 per cent (certified 25.2 per cent), have long-term operational stability (450 hours) and show intense electroluminescence with external quantum efficiencies of more than 10 per cent. Our findings provide a direct route to eliminate the most abundant and deleterious lattice defects present in metal halide perovskites, providing a facile access to solution-processable films with improved optoelectronic performance.

TARO: tree-aggregated factor regression for microbiome data integration.

MOTIVATION: Although the human microbiome plays a key role in health and disease, the biological mechanisms underlying the interaction between the microbiome and its host are incompletely understood. Integration with other molecular profiling data offers an opportunity to characterize the role of the microbiome and elucidate therapeutic targets. However, this remains challenging to the high dimensionality, compositionality, and rare features found in microbiome profiling data. These challenges necessitate the use of methods that can achieve structured sparsity in learning cross-platform association patterns. RESULTS: We propose Tree-Aggregated factor RegressiOn (TARO) for the integration of microbiome and metabolomic data. We leverage information on the taxonomic tree structure to flexibly aggregate rare features. We demonstrate through simulation studies that TARO accurately recovers a low-rank coefficient matrix and identifies relevant features. We applied TARO to microbiome and metabolomic profiles gathered from subjects being screened for colorectal cancer to understand how gut microrganisms shape intestinal metabolite abundances. AVAILABILITY AND IMPLEMENTATION: The R package TARO implementing the proposed methods is available online at https://github.com/amishra-stats/taro-package.

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.

A Complete Picture of Cation Dynamics in Hybrid Perovskite Materials from Solid-State NMR Spectroscopy.

The organic cations in hybrid organic-inorganic perovskites rotate rapidly inside the cuboctahedral cavities formed by the inorganic lattice, influencing optoelectronic properties. Here, we provide a complete quantitative picture of cation dynamics for formamidinium-based perovskites and mixed-cation compositions, which are the most widely used and promising absorber layers for perovskite solar cells today. We use 2H and 14N quadrupolar solid-state NMR relaxometry under magic-angle spinning to determine the activation energy (Ea) and correlation time (τc) at room temperature for rotation about each principal axis of a series of organic cations. Specifically, we investigate methylammonium (MA+), formamidinium (FA+), and guanidinium (GUA+) cations in current state-of-the-art single- and multi-cation perovskite compositions. We find that MA+, FA+, and GUA+ all have at least one component of rotation that occurs on the picosecond timescale at room temperature, with MA+ and GUA+ also exhibiting faster and slower components, respectively. The cation dynamics depend on the symmetry of the inorganic lattice but are found to be insensitive to the degree of cation substitution. In particular, the FA+ rotation is invariant across all compositions studied here, when sufficiently above the phase transition temperature. We further identify an unusual relaxation mechanism for the 2H of MA+ in mechanosynthesized FAxMA1-xPbI3, which was found to result from physical diffusion to paramagnetic defects. This precise picture of cation dynamics will enable better understanding of the relationship between the organic cations and the optoelectronic properties of perovskites, guiding the design principles for more efficient perovskite solar cells in the future.

Optically Enhanced Solid-State 1H NMR Spectroscopy.

Low sensitivity is the primary limitation to extending nuclear magnetic resonance (NMR) techniques to more advanced chemical and structural studies. Photochemically induced dynamic nuclear polarization (photo-CIDNP) is an NMR hyperpolarization technique where light is used to excite a suitable donor-acceptor system, creating a spin-correlated radical pair whose evolution drives nuclear hyperpolarization. Systems that exhibit photo-CIDNP in solids are not common, and this effect has, up to now, only been observed for 13C and 15N nuclei. However, the low gyromagnetic ratio and natural abundance of these nuclei trap the local hyperpolarization in the vicinity of the chromophore and limit the utility for bulk hyperpolarization. Here, we report the first example of optically enhanced solid-state 1H NMR spectroscopy in the high-field regime. This is achieved via photo-CIDNP of a donor-chromophore-acceptor molecule in a frozen solution at 0.3 T and 85 K, where spontaneous spin diffusion among the abundant strongly coupled 1H nuclei relays polarization through the whole sample, yielding a 16-fold bulk 1H signal enhancement under continuous laser irradiation at 450 nm. These findings enable a new strategy for hyperpolarized NMR beyond the current limits of conventional microwave-driven DNP.

Dynamic Nuclear Polarization Enables NMR of Surface Passivating Agents on Hybrid Perovskite Thin Films.

Surface and bulk molecular modulators are the key to improving the efficiency and stability of hybrid perovskite solar cells. However, due to their low concentration, heterogeneous environments, and low sample mass, it remains challenging to characterize their structure and dynamics at the atomic level, as required to establish structure-activity relationships. Nuclear magnetic resonance (NMR) spectroscopy has revealed a wealth of information on the atomic-level structure of hybrid perovskites, but the inherent insensitivity of NMR severely limits its utility to characterize thin-film samples. Dynamic nuclear polarization (DNP) can enhance NMR sensitivity by orders of magnitude, but DNP methods for perovskite materials have so far been limited. Here, we determined the factors that limit the efficiency of DNP NMR for perovskite samples by systematically studying layered hybrid perovskite analogues. We find that the fast-relaxing dynamic cation is the major impediment to higher DNP efficiency, while microwave absorption and particle morphology play a secondary role. We then show that the former can be mitigated by deuteration, enabling 1H DNP enhancement factors of up to 100, which can be harnessed to enhance signals from dopants or additives present in very low concentrations. Specifically, using this new DNP methodology at a high magnetic field and with small sample volumes, we have recorded the NMR spectrum of the 20 nm (6 µg) passivating layer on a single perovskite thin film, revealing a two-dimensional (2D) layered perovskite structure at the surface that resembles the n = 1 homologue but which has greater disorder than in bulk layered perovskites.

Multi-Length Scale Structure of 2D/3D Dion-Jacobson Hybrid Perovskites Based on an Aromatic Diammonium Spacer.

Dion-Jacobson (DJ) iodoplumbates based on 1,4-phenylenedimethanammonium (PDMA) have recently emerged as promising light absorbers for perovskite solar cells. While PDMA is one of the simplest aromatic spacers potentially capable of forming a DJ structure based on (PDMA)An-1 Pbn I3n+1 composition, the crystallographic proof has not been reported so far. Single crystal structure of a DJ phase based on PDMA is presented and high-field solid-state NMR spectroscopy is used to characterize the structure of PDMA-based iodoplumbates prepared as thin films and bulk microcrystalline powders. It is shown that their atomic-level structure does not depend on the method of synthesis and that it is ordered and similar for all iodoplumbate homologues. Moreover, the presence of lower (n) homologues in thin films is identified through UV-Vis spectroscopy, photoluminescence spectroscopy, and X-ray diffraction measurements, complemented by cathodoluminescence mapping. A closer look using cathodoluminescence shows that the micron-scale microstructure corresponds to a mixture of different layered homologues that are well distributed throughout the film and the presence of layer edge states which dominate the emission. This work therefore determines the formation of DJ phases based on PDMA as the spacer cation and reveals their properties on a multi-length scale, which is relevant for their application in optoelectronics.

Negative binomial factor regression with application to microbiome data analysis.

The human microbiome provides essential physiological functions and helps maintain host homeostasis via the formation of intricate ecological host-microbiome relationships. While it is well established that the lifestyle of the host, dietary preferences, demographic background, and health status can influence microbial community composition and dynamics, robust generalizable associations between specific host-associated factors and specific microbial taxa have remained largely elusive. Here, we propose factor regression models that allow the estimation of structured parsimonious associations between host-related features and amplicon-derived microbial taxa. To account for the overdispersed nature of the amplicon sequencing count data, we propose negative binomial reduced rank regression (NB-RRR) and negative binomial co-sparse factor regression (NB-FAR). While NB-RRR encodes the underlying dependency among the microbial abundances as outcomes and the host-associated features as predictors through a rank-constrained coefficient matrix, NB-FAR uses a sparse singular value decomposition of the coefficient matrix. The latter approach avoids the notoriously difficult joint parameter estimation by extracting sparse unit-rank components of the coefficient matrix sequentially, effectively delivering interpretable bi-clusters of taxa and host-associated factors. To solve the nonconvex optimization problems associated with these factor regression models, we present a novel iterative block-wise majorization procedure. Extensive simulation studies and an application to the microbial abundance data from the American Gut Project (AGP) demonstrate the efficacy of the proposed procedure. In the AGP data, we identify several factors that strongly link dietary habits and host life style to specific microbial families.

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.

Crown Ether Modulation Enables over 23% Efficient Formamidinium-Based Perovskite Solar Cells.

The use of molecular modulators to reduce the defect density at the surface and grain boundaries of perovskite materials has been demonstrated to be an effective approach to enhance the photovoltaic performance and device stability of perovskite solar cells. Herein, we employ crown ethers to modulate perovskite films, affording passivation of undercoordinated surface defects. This interaction has been elucidated by solid-state nuclear magnetic resonance and density functional theory calculations. The crown ether hosts induce the formation of host-guest complexes on the surface of the perovskite films, which reduces the concentration of surface electronic defects and suppresses nonradiative recombination by 40%, while minimizing moisture permeation. As a result, we achieved substantially improved photovoltaic performance with power conversion efficiencies exceeding 23%, accompanied by enhanced stability under ambient and operational conditions. This work opens a new avenue to improve the performance and stability of perovskite-based optoelectronic devices through supramolecular chemistry.

Dietary Plant Metabolites Induced Epigenetic Modification as a Novel Strategy for the Management of Prostate Cancer.

Prostate cancer is a widespread malignancy among men, with a substantial global impact on morbidity and mortality. Despite advances in conventional therapies, the need for innovative and less toxic treatments remains a priority. Emerging evidence suggests that dietary plant metabolites possess epigenetic-modifying properties, making them attractive candidates for prostate cancer treatment. The present work reviews the epigenetic effects of dietary plant metabolites in the context of prostate cancer therapy. We first outline the key epigenetic mechanisms involved in prostate cancer pathogenesis, including histone modifications, DNA methylation, and miRNA or Long Noncoding RNA (lncRNA) dysregulation. Next, we delve into the vast array of dietary plant metabolites that have demonstrated promising anti-cancer effects through epigenetic regulation. Resveratrol, minerals, isothiocyanates, curcumin, tea polyphenols, soy isoflavones and phytoestrogens, garlic compounds, anthocyanins, lycopene, and indoles are among the most extensively studied compounds. These plant-derived bioactive compounds have been shown to influence DNA methylation patterns, histone modifications, and microRNA expression, thereby altering the gene expression allied with prostate cancer progression, cell proliferation, and apoptosis. We also explore preclinical and clinical studies investigating the efficacy of dietary plant metabolites as standalone treatments or in combination with traditional treatments for people with prostate cancer. The present work highlights the potential of dietary plant metabolites as epigenetic modulators to treat prostate cancer. Continued research in this field may pave the way for personalized and precision medicine approaches, moving us closer to the goal of improved prostate cancer management.

Targeting Lysosomes: A Strategy Against Chemoresistance in Cancer.

Chemotherapy is still the major method of treatment for many types of cancer. Curative cancer therapy is hampered significantly by medication resistance. Acidic organelles like lysosomes serve as protagonists in cellular digestion. Lysosomes, however, are gaining popularity due to their speeding involvement in cancer progression and resistance. For instance, weak chemotherapeutic drugs of basic nature permeate through the lysosomal membrane and are retained in lysosomes in their cationic state, while extracellular release of lysosomal enzymes induces cancer, cytosolic escape of lysosomal hydrolases causes apoptosis, and so on. Drug availability at the sites of action is decreased due to lysosomal drug sequestration, which also enhances cancer resistance. This review looks at lysosomal drug sequestration mechanisms and how they affect cancer treatment resistance. Using lysosomes as subcellular targets to combat drug resistance and reverse drug sequestration is another method for overcoming drug resistance that is covered in this article. The present review has identified lysosomal drug sequestration as one of the reasons behind chemoresistance. The article delves deeper into specific aspects of lysosomal sequestration, providing nuanced insights, critical evaluations, or novel interpretations of different approaches that target lysosomes to defect cancer.

Soil moisture precipitation feedbacks in the Eastern European Alpine region in convection-permitting climate simulations.

A novel convection permitting modelling framework that combines a pseudo-global warming approach with continuously forced deep soil moisture from prescribed perturbation storylines is applied in the Eastern European Alpine region and parts of the Pannonian Basin to investigate soil moisture precipitation (SMP) feedbacks on summertime precipitation and the feedbacks' role under changed climate conditions. A set of 1-year convection-permitting (3 km horizontal grid spacing) soil moisture sensitivity simulations with the regional climate model of the Consortium for Small-Scale Modelling in Climate Mode are conducted. In order to account for global warming, end-of-the-century climate change effects from four global climate models, projecting the greenhouse gas concentration scenario RCP 8.5, are imprinted. The simulations reveal that (1) the locations of precipitation events are highly sensitive to soil moisture modifications while intensities and the internal structure of precipitation events are nearly unaffected and (2) high precipitation intensities are more likely in combinations with positive temporal but distinctive (either strong positive or strong negative) spatial SMP coupling. Low precipitation intensities are in favour of combinations of negative temporal and positive spatial coupling. The analyses suggest that soil moisture at a given time acts as a guiding field for the location of the next precipitation event. Interestingly, this behaviour is independent of climate change, although the coupling strength's increase is 1.5-1.7 times larger than expected from linear climate change scaling when climate becomes 50% dryer. Finally, it is found that (1) local deviations in the climate change signal of summertime precipitation in the range of up to ±40% are caused by uncertainty in deep soil moisture in the range of ±10% and (2) these local deviations in the climate change signal are dominated by soil moisture uncertainty in future climate conditions.

Density regulation amplifies environmentally induced population fluctuations.

Background: Density-dependent regulation is ubiquitous in population dynamics, and its potential interaction with environmental stochasticity complicates the characterization of the random component of population dynamics. Yet, this issue has not received attention commensurate with its relevance for descriptive and predictive modeling of population dynamics. Here we use a Bayesian modeling approach to investigate the contribution of density regulation to population variability in stochastic environments. Methods: We analytically derive a formula linking the stationary variance of population abundance/density under Gompertz regulation in a stochastic environment with constant variance to the environmental variance and the strength of density feedback, to investigate whether and how density regulation affects the stationary variance. We examine through simulations whether the relationship between stationary variance and density regulation inferred analytically under the Gompertz model carries over to the Ricker model, widely used in population dynamics modeling. Results: The analytical decomposition of the stationary variance under stochastic Gompertz dynamics implies higher variability for strongly regulated populations. Simulation results demonstrate that the pattern of increasing population variability with increasing density feedback found under the Gompertz model holds for the Ricker model as well, and is expected to be a general phenomenon with stochastic population models. We also analytically established and empirically validated that the square of the autoregressive parameter of the Gompertz model in AR(1) form represents the proportion of stationary variance due to density dependence. Discussion: Our results suggest that neither environmental stochasticity nor density regulation can alone explain the patterns of population variability in stochastic environments, as these two components of temporal variation interact, with a tendency for density regulation to amplify the magnitude of environmentally induced population fluctuations. This finding has far-reaching implications for population viability. It implies that intense intra-specific resource competition increases the risk of environment-driven population collapse at high density, making opportune harvesting a sensible practice for improving the resistance of managed populations such as fish stocks to environmental perturbations. The separation of density-dependent and density-independent processes will help improve population dynamics modeling, while providing a basis for evaluating the relative importance of these two categories of processes that remains a topic of long-standing controversy among ecologists.

Relationship of the Poultry Microbiome to Pathogen Colonization, Farm Management, Poultry Production, and Foodborne Illness Risk Assessment.

Despite the continuous progress in food science and technology, the global burden of foodborne illnesses remains substantial, with pathogens in food causing millions of infections each year. Traditional microbiological culture methods are inadequate in detecting the full spectrum of these microorganisms, highlighting the need for more comprehensive detection strategies. This review paper aims to elucidate the relationship between foodborne pathogen colonization and the composition of the poultry microbiome, and how this knowledge can be used for improved food safety. Our review highlights that the relationship between pathogen colonization varies across different sections of the poultry microbiome. Further, our review suggests that the microbiome profile of poultry litter, farm soil, and farm dust may serve as potential indicators of the farm environment's food safety issues. We also agree that the microbiome of processed chicken samples may reveal potential pathogen contamination and food quality issues. In addition, utilizing predictive modeling techniques on the collected microbiome data, we suggest establishing correlations between particular taxonomic groups and the colonization of pathogens, thus providing insights into food safety, and offering a comprehensive overview of the microbial community. In conclusion, this review underscores the potential of microbiome analysis as a powerful tool in food safety, pathogen detection, and risk assessment.

Dynamic Nuclear Polarization of Inorganic Halide Perovskites.

The intrinsic low sensitivity of nuclear magnetic resonance (NMR) experiments limits their utility for structure determination of materials. Dynamic nuclear polarization (DNP) under magic angle spinning (MAS) has shown tremendous potential to overcome this key limitation, enabling the acquisition of highly selective and sensitive NMR spectra. However, so far, DNP methods have not been explored in the context of inorganic lead halide perovskites, which are a leading class of semiconductor materials for optoelectronic applications. In this work, we study cesium lead chloride and quantitatively compare DNP methods based on impregnation with a solution of organic biradicals with doping of high-spin metal ions (Mn2+) into the perovskite structure. We find that metal-ion DNP provides the highest bulk sensitivity in this case, while highly surface-selective NMR spectra can be acquired using impregnation DNP. The performance of both methods is explained in terms of the relaxation times, particle size, dopant concentration, and surface wettability. We envisage the future use of DNP NMR approaches in establishing structure-activity relationships in inorganic perovskites, especially for mass-limited samples such as thin films.

Climate change amplified the 2009 extreme landslide event in Austria.

Landslides are an important natural hazard in mountainous regions. Given the triggering and preconditioning by meteorological conditions, it is known that landslide risk may change in a warming climate, but whether climate change has already affected individual landslide events is still an open question, partly owing to landslide data limitations and methodological challenges in climate impact attribution. Here, we demonstrate the substantial influence of anthropogenic climate change on a severe event in the southeastern Alpine forelands with some estimated 952 individual landslides in June 2009. Our study is based on conditional event attribution complemented by an assessment of changes in atmospheric circulation. Using this approach, we simulate the meteorological event under observed and a range of counterfactual conditions of no climate change and explicitly predict the landslide occurrence probability for these conditions. We find that up to 10%, i.e., 95 landslides, can be attributed to climate change.

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.

Dissecting the Impact of the Gut Microbiome on Cancer Immunotherapy.

The gut microbiome has emerged as a key regulator of response to cancer immunotherapy. However, there is a gap in our understanding of the underlying mechanisms by which the microbiome influences immunotherapy. To this end, we developed a mathematical model based on i) gut microbiome data derived from preclinical studies on melanomas after fecal microbiota transplant, ii) mechanistic modeling of antitumor immune response, and iii) robust association analysis of murine and human microbiome profiles with model-predicted immune profiles. Using our model, we could distill the complexity of these murine and human studies on microbiome modulation in terms of just two model parameters: the activation and killing rate constants of immune cells. We further investigated associations between specific bacterial taxonomies and antitumor immunity and immunotherapy efficacy. This model can guide the design of studies to refine and validate mechanistic links between the microbiome and immune system.

TARO: tree-aggregated factor regression for microbiome data integration.

Motivation: Although the human microbiome plays a key role in health and disease, the biological mechanisms underlying the interaction between the microbiome and its host are incompletely understood. Integration with other molecular profiling data offers an opportunity to characterize the role of the microbiome and elucidate therapeutic targets. However, this remains challenging to the high dimensionality, compositionality, and rare features found in microbiome profiling data. These challenges necessitate the use of methods that can achieve structured sparsity in learning cross-platform association patterns. Results: We propose Tree-Aggregated factor RegressiOn (TARO) for the integration of microbiome and metabolomic data. We leverage information on the phylogenetic tree structure to flexibly aggregate rare features. We demonstrate through simulation studies that TARO accurately recovers a low-rank coefficient matrix and identifies relevant features. We applied TARO to microbiome and metabolomic profiles gathered from subjects being screened for colorectal cancer to understand how gut microrganisms shape intestinal metabolite abundances. Availability and implementation: The R package TARO implementing the proposed methods is available online at https://github.com/amishra-stats/taro-package .