A first principles investigation of Bi2O3-modified TiO2 for visible light activated photocatalysis: the role of TiO2 crystal form and the Bi3+ stereochemical lone pair

Modification of TiO2 with metal oxide nanoclusters is a novel strategy for the design of new photocatalysts with visible light activity. This paper presents a first principles density functional theory (DFT) analysis of the effect of modifying TiO2 rutile (110) and anatase (101) and (001) surfaces with Bi2O3 nanoclusters on the band gap and the nature of the photoexcited state. We show that band gap modifications over unmodified TiO2 depend on the crystal form: modifying rutile (110) results in new Bi2O3 derived states that shift the valence band upwards. On anatase surfaces, there is little effect due to modification with Bi2O3 nanoclusters, but an enhanced UV activity would be expected. Analysis of electron and hole localisation in a model photoexcited state shows enhanced charge separation in Bi2O3-modified rutile (110) but not in Bi2O3-modified anatase. The effect of the Bi3+ lone-pair on the properties of Bi2O3-modified TiO2 contrasts with SnO-modified TiO2, consistent with the weaker lone pair in Bi2O3 compared with SnO

Metal oxide nanocluster-modified TiO2 as solar activated photocatalyst materials

In this review we describe our work on new TiO2 based photocatalysts. The key concept in our work is to form new composite structures by the modification of rutile and anatase TiO2 with nanoclusters of metal oxides and our density functional theory (DFT) level simulations are validated by experimental work synthesizing and characterizing surface-modified TiO2. We use DFT to show that nanoclusters of different metal oxides, TiO2, SnO/SnO2, PbO/PbO2, NiO and CuO can be adsorbed at rutile and anatase surfaces and can induce red shifts in the absorption edge to enable visible light absorption which is the first key requirement for a practical photocatalyst. We furthermore determine the origin of the red shift and discuss the factors influencing this shift and the fate of excited electrons and holes. For p-block metal oxides we show how the oxidation state of Sn and Pb can be used to tune both the magnitude of the red shift and also its mechanism. Finally, aiming to make our models more realistic, we present some new results on the stability of water at rutile and anatase surfaces and the effect of water on oxygen vacancy formation and on nanocluster modification. These nanocluster-modified TiO2 structures form the basis of a new class of photocatalysts which will be useful in oxidation reactions and with the suitable choice of nanocluster modifier can be applied to CO2 reduction

Forecasting GDP Growth using Disaggregated GDP Revisions

This paper investigates the informational content of regular revisions to real GDP growth and its components. We perform a real-time forecasting exercise for the advance estimate of real GDP growth using dynamic regression models that include revisions to GDP and its components. Echoing other work in the literature, we find little evidence that including aggregate GDP growth revisions improves forecast accuracy relative to an AR(1) baseline model; however, models that include revisions to components of GDP improve forecast accuracy. The first revision to consumption is particularly relevant in that every model that includes the revision outperforms the baseline model. Measured by root mean squared forecasting error (RMSFE), improvements are quite sizable, with many models increasing forecasting performance by 5% or more, and with top-performing models forecasting 0.24 percentage points closer to the advance estimate of growth. We use Bayesian model averaging to underscore that our results are driven by the informational content of revisions. The posterior probability of models with the first revision to consumption is significantly higher than our baseline model, despite strong priors that the latter should be the preferred forecasting model

Design of novel visible light active photocatalyst materials: Surface modified TiO2

Work on the design of new TiO2 based photocatalysts is described. The key concept is the formation of composite structures through the modification of anatase and rutile TiO2 with molecular-sized nanoclusters of metal oxides. Density functional theory (DFT) level simulations are compared with experimental work synthesizing and characterizing surface modified TiO2. DFT calculations are used to show that nanoclusters of metal oxides such as TiO2, SnO/SnO2, PbO/PbO2, ZnO and CuO are stable when adsorbed at rutile and anatase surfaces, and can lead to a significant red shift in the absorption edge which will induce visible light absorption; this is the first requirement for a useful photocatalyst. The origin of the red shift and the fate of excited electrons and holes are determined. For p-block metal oxides the oxidation state of Sn and Pb can be used to modify the magnitude of the red shift and its mechanism. Comparisons of recent experimental studies of surface modified TiO2 that validate our DFT simulations are described. These nanocluster-modified TiO2 structures form the basis of a new class of photocatalysts which will be useful in oxidation reactions and with a correct choice of nanocluster modified can be applied to other reactions

Forecasting GDP: Do Revisions Matter?

This paper investigates the informational content of regular revisions to real GDP growth and its components. We perform a real-time forecasting exercise for the advance estimate of real GDP growth using dynamic regression models that include GDP and GDP component revisions. Echoing other work in the literature, we find little evidence that including aggregate GDP growth revisions improves forecast accuracy relative to an AR(1) baseline model; however, when we include revisions to components of GDP (i.e. C, I, G, X, and M) we find improvements in forecast accuracy. Overall, nearly 68\% of all models that contain subsets of component revisions outperform our baseline model. The "best" component-augmented model forecasts roughly 0.2 percentage points better, and a large subset of models improve RMSFE by more than 5%. Finally, we use Bayesian model comparison to demonstrate that differences in forecast performance are unlikely to be the result of statistical noise. Our results imply that component revisions, in particular to consumption, contain important information for forecasting GDP growth

Forecasting GDP: Do Revisions Matter?

This paper investigates the informational content of regular revisions to real GDP growth and its components. We perform a real-time forecasting exercise for the advance estimate of real GDP growth using dynamic regression models that include GDP and GDP component revisions. Echoing other work in the literature, we find little evidence that including aggregate GDP growth revisions improves forecast accuracy relative to an AR(1) baseline model; however, when we include revisions to components of GDP (i.e. C, I, G, X, and M) we find improvements in forecast accuracy. Overall, nearly 68\% of all models that contain subsets of component revisions outperform our baseline model. The "best" component-augmented model forecasts roughly 0.2 percentage points better, and a large subset of models improve RMSFE by more than 5%. Finally, we use Bayesian model comparison to demonstrate that differences in forecast performance are unlikely to be the result of statistical noise. Our results imply that component revisions, in particular to consumption, contain important information for forecasting GDP growth

Liquid Dynamics Determine Transition Metal-N-Heterocyclic Carbene Complex Formation

The mechanism of metal-N-heterocyclic carbene (NHC) complex formation from imidazolium salts in the presence of weak bases was investigated through theoretical methods. Quantum chemical calculations revealed that the two bases considered here, sodium acetate and trimethylamine, both facilitate complex formation. In contrast to previous experiments, these calculations indicated a slightly lower barrier with the amine. Molecular dynamics simulations showed that the ionic nature of the [AuCl2]− and imidazolium ions, as well as the sodium acetate base keep these species associated in the reaction mixture through ion pairing. This pre-association of the components produces those clusters that are essential for the metal complex formation reaction. The neutral amine, however, remains mostly separated from the other reaction partners, making it a significantly less effective base

Mental Health of Parents and Life Satisfaction of Children: A Within-Family Analysis of Intergenerational Transmission of Well-Being

This paper addresses the extent to which there is an intergenerational transmission of mental health and subjective well-being within families. Specifically it asks whether parents’ own mental distress influences their child’s life satisfaction, and vice versa. Whilst the evidence on daily contagion of stress and strain between members of the same family is substantial, the evidence on the transmission between parental distress and children’s well-being over a longer period of time is sparse. We tested this idea by examining the within-family transmission of mental distress from parent to child’s life satisfaction, and vice versa, using rich longitudinal data on 1,175 British youths. Results show that parental distress at year t-1 is an important determinant of child’s life satisfaction in the current year. This is true for boys and girls, although boys do not appear to be affected by maternal distress levels. The results also indicated that the child’s own life satisfaction is related with their father’s distress levels in the following year, regardless of the gender of the child. Finally, we examined whether the underlying transmission correlation is due to shared social environment, empathic reactions, or transmission via parent-child interaction

Multiomics modeling of the immunome, transcriptome, microbiome, proteome and metabolome adaptations during human pregnancy.

MotivationMultiple biological clocks govern a healthy pregnancy. These biological mechanisms produce immunologic, metabolomic, proteomic, genomic and microbiomic adaptations during the course of pregnancy. Modeling the chronology of these adaptations during full-term pregnancy provides the frameworks for future studies examining deviations implicated in pregnancy-related pathologies including preterm birth and preeclampsia.ResultsWe performed a multiomics analysis of 51 samples from 17 pregnant women, delivering at term. The datasets included measurements from the immunome, transcriptome, microbiome, proteome and metabolome of samples obtained simultaneously from the same patients. Multivariate predictive modeling using the Elastic Net (EN) algorithm was used to measure the ability of each dataset to predict gestational age. Using stacked generalization, these datasets were combined into a single model. This model not only significantly increased predictive power by combining all datasets, but also revealed novel interactions between different biological modalities. Future work includes expansion of the cohort to preterm-enriched populations and in vivo analysis of immune-modulating interventions based on the mechanisms identified.Availability and implementationDatasets and scripts for reproduction of results are available through: https://nalab.stanford.edu/multiomics-pregnancy/.Supplementary informationSupplementary data are available at Bioinformatics online