Oil has long been the dominant feedstock for producing fuels and chemicals, but coal, natural gas and biomass are increasingly explored alternatives. Their conversion first generates syngas, a mixture of CO and H, which is then processed further using Fischer-Tropsch (FT) chemistry. However, although commercial FT technology for fuel production is established, using it to access valuable chemicals remains challenging. A case in point is linear α-olefins (LAOs), which are important chemical intermediates obtained by ethylene oligomerization at present. The commercial high-temperature FT process and the FT-to-olefin process under development at present both convert syngas directly to LAOs, but also generate much CO waste that leads to a low carbon utilization efficiency. The efficiency is further compromised by substantially fewer of the converted carbon atoms ending up as valuable C-C LAOs than are found in the C-C olefins that dominate the product mixtures. Here we show that the use of the original phase-pure χ-iron carbide can minimize these syngas conversion problems: tailored and optimized for the process of FT to LAOs, this catalyst exhibits an activity at 290 °C that is 1-2 orders higher than dedicated FT-to-olefin catalysts can achieve above 320 °C (refs. ), is stable for 200 h, and produces desired C-C LAOs and unwanted CO with carbon-based selectivities of 51% and 9% under industrially relevant conditions. This higher catalytic performance, persisting over a wide temperature range (250-320 °C), demonstrates the potential of the system for developing a practically relevant technology.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11541216 | PMC |
http://dx.doi.org/10.1038/s41586-024-08078-5 | DOI Listing |
Glob Ment Health (Camb)
January 2025
Department of Health Policy and Management, School of Public Health, Peking University, Beijing, China.
This study aimed to investigate the effects of physical multimorbidity on the trajectory of cognitive decline over 17 years and whether vary across wealth status. The study was conducted in 9035 respondents aged 50+ at baseline from nine waves (2002-2019) of the English Longitudinal Study of Aging. A latent class analysis was used to identify patterns of physical multimorbidity, and mixed multilevel models were performed to determine the association between physical multimorbidity and trajectories of cognitive decline.
View Article and Find Full Text PDFFront Psychol
December 2024
Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, United States.
Introduction: While the fact that visual stimuli synthesized by Artificial Neural Networks (ANN) may evoke emotional reactions is documented, the precise mechanisms that connect the strength and type of such reactions with the ways of how ANNs are used to synthesize visual stimuli are yet to be discovered. Understanding these mechanisms allows for designing methods that synthesize images attenuating or enhancing selected emotional states, which may provide unobtrusive and widely-applicable treatment of mental dysfunctions and disorders.
Methods: The Convolutional Neural Network (CNN), a type of ANN used in computer vision tasks which models the ways humans solve visual tasks, was applied to synthesize ("dream" or "hallucinate") images with no semantic content to maximize activations of neurons in precisely-selected layers in the CNN.
Front Cardiovasc Med
December 2024
Cardiovascular Department, Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, China.
Background: Poor nutritional status may affect outcomes after coronary revascularization, but the association between nutritional status and outcomes in patients undergoing coronary revascularization has not been fully evaluated. This study was based on the MIMIC-IV database to analyze the impact of baseline nutritional status on poor outcomes in patients with coronary revascularization.
Methods: Patients with coronary revascularization were screened from the MIMIC-IV database.
Patterns (N Y)
December 2024
Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, USA.
Guidelines in statistical modeling for genomics hold that simpler models have advantages over more complex ones. Potential advantages include cost, interpretability, and improved generalization across datasets or biological contexts. We directly tested the assumption that small gene signatures generalize better by examining the generalization of mutation status prediction models across datasets (from cell lines to human tumors and vice versa) and biological contexts (holding out entire cancer types from pan-cancer data).
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!