Towards Experimental Handbooks in Catalysis

The “Seven Pillars” of oxidation catalysis proposed by Robert K. Grasselli represent an early example of phenomenological descriptors in the field of heterogeneous catalysis. Major advances in the theoretical description of catalytic reactions have been achieved in recent years and new catalysts are predicted today by using computational methods. To tackle the immense complexity of high-performance systems in reactions where selectivity is a major issue, analysis of scientific data by artificial intelligence and data science provides new opportunities for achieving improved understanding. Modern data analytics require data of highest quality and sufficient diversity. Existing data, however, frequently do not comply with these constraints. Therefore, new concepts of data generation and management are needed. Herein we present a basic approach in defining best practice procedures of measuring consistent data sets in heterogeneous catalysis using “handbooks”. Selective oxidation of short-chain alkanes over mixed metal oxide catalysts was selected as an example.DFG, 390540038, EXC 2008: Unifying Systems in Catalysis "UniSysCat

Unraveling property-performance relationships by surface tailoring of oxidation catalysts via ALD

Atomic layer deposition (ALD) of POx on V2O5 powder was applied as a tool to tailor active and selective sites of a bulk catalyst. ALD leads to homogeneous P deposition on the V2O5 surface with linear increase of P content with each ALD cycle. The catalyst performance was evaluated and correlated to structural motifs identified by detailed characterization methods. The catalytic conversion of butane to maleic anhydride (MAN) was chosen as proof-of-concept reaction. The selectivity towards MAN increases with ALD cycle number from 1–3 ALD cycles and remains constant at higher ALD cycles. Restructuring of the catalyst surface is induced by steam during reaction conditions at elevated temperatures. Excessive P is migrating away from the catalyst surface to form various VOPO4 polymorphs revealing partially but homogeneously covered V2O5 by P. The formed VOPO4 species barely contribute to the yield to MAN. Solid-state 31P-NMR was used to identify fingerprints relevant for selectivity and activity. This work shows that synthesizing model catalysts by atomic layer deposition combined with detailed analytics can reveal property-performance relationships.DFG, 390540038, EXC 2008: Unifying Systems in Catalysis "UniSysCat

Data-centric heterogeneous catalysis: identifying rules and materials genes of alkane selective oxidation

Artificial intelligence (AI) can accelerate materials design by identifying the key parameters correlated with the performance. However, widely used AI methods require big data, and only the smallest part of the available data in heterogeneous catalysis meets the quality requirement for data-efficient AI. Here, we use rigorous experimental procedures, designed to consistently take into account the kinetics of the catalyst active states formation, in order to measure 55 physicochemical parameters as well as the reactivity of 12 catalysts towards ethane, propane, and n-butane oxidation. These catalyst materials are based on vanadium or manganese redox-active elements (RAEs) and present diverse phase compositions, crystallinities, and catalytic behaviors. By applying the sure-independence-screening-and-sparsifying-operator (SISSO) approach to the consistent data set, we identify nonlinear property-function relationships depending on several key parameters, reflecting the intricate interplay of underlying processes governing selective oxidation. This approach indicates the most relevant characterization techniques and shows how the catalyst properties may be tuned in order to achieve the desired performance. For example, to achieve high olefin yields, the catalyst must have a high specific surface area, a low concentration of surface RAE, and the ability to change the surface RAE oxidation states under reaction conditions with respect to vacuum. These parameters are measured by N2 adsorption, x-ray photoelectron spectroscopy (XPS), and near-ambient-pressure in situ XPS. They reflect the relevance of local transport, site isolation, surface redox activity, and the materials dynamical restructuring under reaction conditions. Although the relationship describing the even more challenging oxygenate yields shares similarities with that for olefin yields, a parameter reflecting the importance of specific surface sites, derived from the analysis of the carbon 1s XPS spectra, is additionally identified as key for high selectivity to oxygenates

Biomarker-based prognosis for people with mild cognitive impairment (ABIDE) : a modelling study

Background: Biomarker-based risk predictions of dementia in people with mild cognitive impairment are highly relevant for care planning and to select patients for treatment when disease-modifying drugs become available. We aimed to establish robust prediction models of disease progression in people at risk of dementia. Methods: In this modelling study, we included people with mild cognitive impairment (MCI) from single-centre and multicentre cohorts in Europe and North America: the European Medical Information Framework for Alzheimer's Disease (EMIF-AD; n=883), Alzheimer's Disease Neuroimaging Initiative (ADNI; n=829), Amsterdam Dementia Cohort (ADC; n=666), and the Swedish BioFINDER study (n=233). Inclusion criteria were a baseline diagnosis of MCI, at least 6 months of follow-up, and availability of a baseline Mini-Mental State Examination (MMSE) and MRI or CSF biomarker assessment. The primary endpoint was clinical progression to any type of dementia. We evaluated performance of previously developed risk prediction models—a demographics model, a hippocampal volume model, and a CSF biomarkers model—by evaluating them across cohorts, incorporating different biomarker measurement methods, and determining prognostic performance with Harrell's C statistic. We then updated the models by re-estimating parameters with and without centre-specific effects and evaluated model calibration by comparing observed and expected survival. Finally, we constructed a model combining markers for amyloid deposition, tauopathy, and neurodegeneration (ATN), in accordance with the National Institute on Aging and Alzheimer's Association research framework. Findings: We included all 2611 individuals with MCI in the four cohorts, 1007 (39%) of whom progressed to dementia. The validated demographics model (Harrell's C 0·62, 95% CI 0·59–0·65), validated hippocampal volume model (0·67, 0·62–0·72), and updated CSF biomarkers model (0·72, 0·68–0·74) had adequate prognostic performance across cohorts and were well calibrated. The newly constructed ATN model had the highest performance (0·74, 0·71–0·76). Interpretation: We generated risk models that are robust across cohorts, which adds to their potential clinical applicability. The models could aid clinicians in the interpretation of CSF biomarker and hippocampal volume results in individuals with MCI, and help research and clinical settings to prepare for a future of precision medicine in Alzheimer's disease. Future research should focus on the clinical utility of the models, particularly if their use affects participants' understanding, emotional wellbeing, and behaviour. Funding: ZonMW-Memorabel

Prognostic Scores for Ursodeoxycholic Acid-Treated Patients Predict Graft Loss and Mortality in Recurrent Primary Biliary Cholangitis after Liver Transplantation

Background/aim Recurrent primary biliary cholangitis (rPBC) develops in approximately 30% of patients and negatively impacts graft and overall patient survival after liver transplantation (LT). There is a lack of data regarding the response rate to ursodeoxycholic acid (UDCA) in rPBC. We evaluated a large, international, multi-center cohort to assess the performance of scores for PBC to predict the risk of graft and overall survival after LT in patients with rPBC. Methods A total of 332 patients with rPBC after LT were evaluated from 28 centres across Europe, North and South America. The median age at the time of rPBC was 58.0 years [IQR 53.2 - 62.6], and 298 patients (90%) were females. The biochemical response was measured with serum levels of alkaline phosphatase (ALP) and bilirubin, and Paris-2, GLOBE and UK-PBC scores at 1 year after UDCA initiation. Results During a median follow-up of 8.7 years [IQR 4.3 - 12.9] after rPBC diagnosis, 52 patients (16%) had graft loss and 103 (31%) died. After 1 year of UDCA initiation the histological stage at rPBC (HR, 3.97, 95%CI 1.36-11.55, P=0.01), use of prednisone (HR 3.18, 95%CI 1.04-9.73, P=0.04), ALP xULN (HR 1.59, 95%CI 1.26-2.01,

Prognostic Scores for Ursodeoxycholic Acid-Treated Patients Predict Graft Loss and Mortality in Recurrent Primary Biliary Cholangitis after Liver Transplantation

Background/aim Recurrent primary biliary cholangitis (rPBC) develops in approximately 30% of patients and negatively impacts graft and overall patient survival after liver transplantation (LT). There is a lack of data regarding the response rate to ursodeoxycholic acid (UDCA) in rPBC. We evaluated a large, international, multi-center cohort to assess the performance of scores for PBC to predict the risk of graft and overall survival after LT in patients with rPBC. Methods A total of 332 patients with rPBC after LT were evaluated from 28 centres across Europe, North and South America. The median age at the time of rPBC was 58.0 years [IQR 53.2 - 62.6], and 298 patients (90%) were females. The biochemical response was measured with serum levels of alkaline phosphatase (ALP) and bilirubin, and Paris-2, GLOBE and UK-PBC scores at 1 year after UDCA initiation. Results During a median follow-up of 8.7 years [IQR 4.3 - 12.9] after rPBC diagnosis, 52 patients (16%) had graft loss and 103 (31%) died. After 1 year of UDCA initiation the histological stage at rPBC (HR, 3.97, 95%CI 1.36-11.55, P=0.01), use of prednisone (HR 3.18, 95%CI 1.04-9.73, P=0.04), ALP xULN (HR 1.59, 95%CI 1.26-2.01,