Cs3Bi2Br9/g-C3N4 Direct Z-Scheme Heterojunction for Enhanced Photocatalytic Reduction of CO2 to CO.

Lead-free halide perovskite derivative Cs3Bi2Br9 has recently been found to possess optoelectronic properties suitable for photocatalytic CO2 reduction reactions to CO. However, further work needs to be performed to boost charge separation for improving the overall efficiency of the photocatalyst. This report demonstrates the synthesis of a hybrid inorganic/organic heterojunction between Cs3Bi2Br9 and g-C3N4 at different ratios, achieved by growing Cs3Bi2Br9 crystals on the surface of g-C3N4 using a straightforward antisolvent crystallization method. The synthesized powders showed enhanced gas-phase photocatalytic CO2 reduction in the absence of hole scavengers of 14.22 (±1.24) µmol CO g-1 h-1 with 40 wt % Cs3Bi2Br9 compared with 1.89 (±0.72) and 5.58 (±0.14) µmol CO g-1 h-1 for pure g-C3N4 and Cs3Bi2Br9, respectively. Photoelectrochemical measurements also showed enhanced photocurrent in the 40 wt % Cs3Bi2Br9 composite, demonstrating enhanced charge separation. In addition, stability tests demonstrated structural stability upon the formation of a heterojunction, even after 15 h of illumination. Band structure alignment and selective metal deposition studies indicated the formation of a direct Z-scheme heterojunction between the two semiconductors, which boosted charge separation. These findings support the potential of hybrid organic/inorganic g-C3N4/Cs3Bi2Br9 Z-scheme photocatalyst for enhanced CO2 photocatalytic activity and improved stability.

Lead-Free Halide Perovskite Cs2AgBiBr6/Bismuthene Composites for Improved CH4 Production in Photocatalytic CO2 Reduction.

CO2 photocatalytic conversion into value-added fuels through solar energy is a promising way of storing renewable energy while simultaneously reducing the concentration of CO2 in the atmosphere. Lead-based halide perovskites have recently shown great potential in various applications such as solar cells, optoelectronics, and photocatalysis. Even though they show high performance, the high toxicity of Pb2+ along with poor stability under ambient conditions restrains the application of these materials in photocatalysis. In this respect, we developed an in situ assembly strategy to fabricate the lead-free double perovskite Cs2AgBiBr6 on a 2D bismuthene nanosheet prepared by a ligand-assisted reprecipitation method for a liquid-phase CO2 photocatalytic reduction reaction. The composite improved the production and selectivity of the eight-electron CH4 pathway compared with the two-electron CO pathway, storing more of the light energy harvested by the photocatalyst. The Cs2AgBiBr6/bismuthene composite shows a photocatalytic activity of 1.49(±0.16) µmol g-1 h-1 CH4, 0.67(±0.14) µmol g-1 h-1 CO, and 0.75(±0.20) µmol g-1 h-1 H2, with a CH4 selectivity of 81(±1)% on an electron basis with 1 sun. The improved performance is attributed to the enhanced charge separation and suppressed electron-hole recombination due to good interfacial contact between the perovskite and bismuthene promoted by the synthesis method.

Solution-processable polymers of intrinsic microporosity for gas-phase carbon dioxide photoreduction.

Four solution-processable, linear conjugated polymers of intrinsic porosity are synthesised and tested for gas phase carbon dioxide photoreduction. The polymers' photoreduction efficiency is investigated as a function of their porosity, optical properties, energy levels and photoluminescence. All polymers successfully form carbon monoxide as the main product, without the addition of metal co-catalysts. The best performing single component polymer yields a rate of 66 µmol h-1 m-2, which we attribute to the polymer exhibiting macroporosity and the longest exciton lifetimes. The addition of copper iodide, as a source of a copper co-catalyst in the polymers shows an increase in rate, with the best performing polymer achieving a rate of 175 µmol h-1 m-2. The polymers are active for over 100 h under operating conditions. This work shows the potential of processable polymers of intrinsic porosity for use in the gas phase photoreduction of carbon dioxide towards solar fuels.

A Review on Halide Perovskite-Based Photocatalysts: Key Factors and Challenges.

A growing number of research articles have been published on the use of halide perovskite materials for photocatalytic reactions. These articles extend these materials' great success from solar cells to photocatalytic technologies such as hydrogen production, CO2 reduction, dye degradation, and organic synthesis. In the present review article, we first describe the background theory of photocatalysis, followed by a description on the properties of halide perovskites and their development for photocatalysis. We highlight key intrinsic factors influencing their photocatalytic performance, such as stability, electronic band structure, and sorption properties. We also discuss and shed light on key considerations and challenges for their development in photocatalysis, such as those related to reaction conditions, reactor design, presence of degradable organic species, and characterization, especially for CO2 photocatalytic reduction. This review on halide perovskite photocatalysts will provide a better understanding for their rational design and development and contribute to their scientific and technological adoption in the wide field of photocatalytic solar devices.

Automatic classification of free-text medical causes from death certificates for reactive mortality surveillance in France.

BACKGROUND: Mortality surveillance is of fundamental importance to public health surveillance. The real-time recording of death certificates, thanks to Electronic Death Registration System (EDRS), provides valuable data for reactive mortality surveillance based on medical causes of death in free-text format. Reactive mortality surveillance is based on the monitoring of mortality syndromic groups (MSGs). An MSG is a cluster of medical causes of death (pathologies, syndromes or symptoms) that meets the objectives of early detection and impact assessment of public health events. The aim of this study is to implement and measure the performance of a rule-based method and two supervised models for automatic free-text cause of death classification from death certificates in order to implement them for routine surveillance. METHOD: A rule-based method was implemented using four processing steps: standardization rules, splitting causes of death using delimiters, spelling corrections and dictionary projection. A supervised machine learning method using a linear Support Vector Machine (SVM) classifier was also implemented. Two models were produced using different features (SVM1 based solely on surface features and SVM2 combining surface features and MSGs classified by the rule-based method as feature vectors). The evaluation was conducted using an annotated subset of electronic death certificates received between 2012 and 2016. Classification performance was evaluated on seven MSGs (Influenza, Low respiratory diseases, Asphyxia/abnormal respiration, Acute respiratory disease, Sepsis, Chronic digestive diseases, and Chronic endocrine diseases). RESULTS: The rule-based method and the SVM2 model displayed a high performance with F-measures over 0.94 for all MSGs. Precision and recall were slightly higher for the rule-based method and the SVM2 model. An error-analysis shows that errors were not specific to an MSG. CONCLUSION: The high performance of the rule-based method and SVM2 model will allow us to set-up a reactive mortality surveillance system based on free-text death certificates. This surveillance will be an added-value for public health decision making.

A New Approach to Compare the Performance of Two Classification Methods of Causes of Death for Timely Surveillance in France.

Timely mortality surveillance in France is based on the monitoring of electronic death certificates to provide information to health authorities. This study aims to analyze the performance of a rule-based and a supervised machine learning method to classify medical causes of death into 60 mortality syndromic groups (MSGs). Performance was first measured on a test set. Then we compared the trends of the monthly numbers of deaths classified into MSGs from 2012 to 2016 using both methods. Among the 60 MSGs, 31 achieved recall and precision over 0.95 for either one or the other method on the test set. On the whole dataset, the correlation coefficient of the monthly numbers of deaths obtained by the two methods were close to 1 for 21 of the 31 MSGs. This approach is useful for analyzing a large number of categories or when annotated resources are limited.

Evaluation of the French reactive mortality surveillance system supporting decision making.

BACKGROUND: In France, a mortality syndromic surveillance system was set up with objectives of early detection and reactive evaluation of the impact of expected and unexpected events to support decision makers. This study aims to describe the characteristics of the system and its usefulness for decision makers. METHODS: Anonymized data from the administrative part of death certificates were daily collected from 3062 computerized city halls and were transmitted to Santé publique France in routine. Coverage of the system was measured as the proportion of deaths registered by the system among the complete number of deaths and analyzed by age, month and region. Deaths were described by gender, age and geographical level using proportion. The excess periods of deaths were described based on the comparison of the weekly observed and expected numbers of deaths between 2012 and 2016. RESULTS: The system recorded 77.5% of the national mortality covering the whole territory. About 81% of deaths were aged 65 years old and more. The surveillance system identified mortality variations mainly during winter and summer, for some concomitant with influenza epidemic or heatwave period, and thus provided information for decision makers. CONCLUSION: The ability of the system to detect and follow mortality outbreaks in routine in the whole territory has been demonstrated. It is a useful tool to provide early evaluation of the impact of threats on mortality and alert decision makers to adapt control measures. However, the absence of information on medical causes of death may limit the ability to target recommendations.