Dependence of phase transitions on halide ratio in inorganic CsPb BrxI1 x 3 perovskite thin films obtained from high throughput experimentation

In this communication, we present the phase diagram of CsPb BrxI1 amp; 8722;x 3 0 amp; 8804; x amp; 8804; 1, 300 585 K obtained by high throughput in situ GIWAXS measurements of a combinatorial thin film library. We find that all compositions convert to the cubic perovskite phase at high temperature and that the presence of bromide in the films stabilizes the metastable perovskite phases upon cool down. In accordance with recent predictions from DFT calculations, the transition temperatures monotonically decrease with increasing bromide conten

Combinatorial inkjet printing for compositional tuning of metal halide perovskite thin films

To accelerate the materials discovery and development process for a sustainable technology advancement it is imperative to explore and develop combined high throughput material synthesis and analysis workflows. In this work, we investigate a method of combinatorial inkjet printing to tune the composition of the inorganic cesium lead mixed halide perovskite solid solution, CsPb BrxI1 amp; 8722;x 3. The compositional variation is achieved by simultaneous printing of different precursor inks with multiple printheads and controlled by varying the number of droplets printed by each printhead throughout the sample. The droplet placement is optimised through an algorithm that allows maximum mixing of the combined inks. The local compositional homogeneity of thin film samples was investigated as a function of the printing resolution by micrometer resolution X ray fluorescence and synchrotron based grazing incidence wide angle X ray scattering. We show that a combinatorial library of ten compositions between CsPbI3 and CsPbBr2I, printed using the developed algorithm, is locally homogeneous for the optimised printing parameters. An implementation of the algorithm in the high level programming language Python is provided for easy use in other system

Diagnostic metabolite biomarkers of chronic typhoid carriage.

BACKGROUND: Salmonella Typhi and Salmonella Paratyphi A are the agents of enteric (typhoid) fever; both can establish chronic carriage in the gallbladder. Chronic Salmonella carriers are typically asymptomatic, intermittently shedding bacteria in the feces, and contributing to disease transmission. Detecting chronic carriers is of public health relevance in areas where enteric fever is endemic, but there are no routinely used methods for prospectively identifying those carrying Salmonella in their gallbladder. METHODOLOGY/PRINCIPAL FINDINGS: Here we aimed to identify biomarkers of Salmonella carriage using metabolite profiling. We performed metabolite profiling on plasma from Nepali patients undergoing cholecystectomy with confirmed S. Typhi or S. Paratyphi A gallbladder carriage (and non-carriage controls) using two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GCxGC-TOFMS) and supervised pattern recognition modeling. We were able to significantly discriminate Salmonella carriage samples from non-carriage control samples. We were also able to detect differential signatures between S. Typhi and S. Paratyphi A carriers. We additionally compared carriage metabolite profiles with profiles generated during acute infection; these data revealed substantial heterogeneity between metabolites associated with acute enteric fever and chronic carriage. Lastly, we found that Salmonella carriers could be significantly distinguished from non-carriage controls using only five metabolites, indicating the potential of these metabolites as diagnostic markers for detecting chronic Salmonella carriers. CONCLUSIONS/SIGNIFICANCE: Our novel approach has highlighted the potential of using metabolomics to search for diagnostic markers of chronic Salmonella carriage. We suggest further epidemiological investigations of these potential biomarkers in alternative endemic enteric fever settings

Reproducible diagnostic metabolites in plasma from typhoid fever patients in Asia and Africa.

Salmonella Typhi is the causative agent of typhoid. Typhoid is diagnosed by blood culture, a method that lacks sensitivity, portability and speed. We have previously shown that specific metabolomic profiles can be detected in the blood of typhoid patients from Nepal (Näsström et al., 2014). Here, we performed mass spectrometry on plasma from Bangladeshi and Senegalese patients with culture confirmed typhoid fever, clinically suspected typhoid, and other febrile diseases including malaria. After applying supervised pattern recognition modelling, we could significantly distinguish metabolite profiles in plasma from the culture confirmed typhoid patients. After comparing the direction of change and degree of multivariate significance, we identified 24 metabolites that were consistently up- or down regulated in a further Bangladeshi/Senegalese validation cohort, and the Nepali cohort from our previous work. We have identified and validated a metabolite panel that can distinguish typhoid from other febrile diseases, providing a new approach for typhoid diagnostics

An open-access database and analysis tool for perovskite solar cells based on the FAIR data principles

Large datasets are now ubiquitous as technology enables higher-throughput experiments, but rarely can a research field truly benefit from the research data generated due to inconsistent formatting, undocumented storage or improper dissemination. Here we extract all the meaningful device data from peer-reviewed papers on metal-halide perovskite solar cells published so far and make them available in a database. We collect data from over 42,400 photovoltaic devices with up to 100 parameters per device. We then develop open-source and accessible procedures to analyse the data, providing examples of insights that can be gleaned from the analysis of a large dataset. The database, graphics and analysis tools are made available to the community and will continue to evolve as an open-source initiative. This approach of extensively capturing the progress of an entire field, including sorting, interactive exploration and graphical representation of the data, will be applicable to many fields in materials science, engineering and biosciences

An open-access database and analysis tool for perovskite solar cells based on the FAIR data principles

Large datasets are now ubiquitous as technology enables higher-throughput experiments, but rarely can a research field truly benefit from the research data generated due to inconsistent formatting, undocumented storage or improper dissemination. Here we extract all the meaningful device data from peer-reviewed papers on metal-halide perovskite solar cells published so far and make them available in a database. We collect data from over 42, 400 photovoltaic devices with up to 100 parameters per device. We then develop open-source and accessible procedures to analyse the data, providing examples of insights that can be gleaned from the analysis of a large dataset. The database, graphics and analysis tools are made available to the community and will continue to evolve as an open-source initiative. This approach of extensively capturing the progress of an entire field, including sorting, interactive exploration and graphical representation of the data, will be applicable to many fields in materials science, engineering and biosciences. © 2021, The Author(s)