Maximizing noble metal utilization in solid catalysts by control of nanoparticle location.

Maximizing the utilization of noble metals is crucial for applications such as catalysis. We found that the minimum loading of platinum for optimal performance in the hydroconversion of n-alkanes for industrially relevant bifunctional catalysts could be reduced by a factor of 10 or more through the rational arranging of functional sites at the nanoscale. Intentionally depositing traces of platinum nanoparticles on the alumina binder or the outer surface of zeolite crystals, instead of inside the zeolite crystals, enhanced isomer selectivity without compromising activity. Separation between platinum and zeolite acid sites preserved the metal and acid functions by limiting micropore blockage by metal clusters and enhancing access to metal sites. Reduced platinum nanoparticles were more active than platinum single atoms strongly bonded to the alumina binder.

Utilizing heart rate variability to predict ICU patient outcome in traumatic brain injury.

BACKGROUND: Prediction of patient outcome in medical intensive care units (ICU) may help for development and investigation of early interventional strategies. Several ICU scoring systems have been developed and are used to predict clinical outcome of ICU patients. These scores are calculated from clinical physiological and biochemical characteristics of patients. Heart rate variability (HRV) is a correlate of cardiac autonomic regulation and has been evident as a marker of poor clinical prognosis. HRV can be measured from the electrocardiogram non-invasively and monitored in real time. HRV has been identified as a promising 'electronic biomarker' of disease severity. Traumatic brain injury (TBI) is a subset of critically ill patients admitted to ICU, with significant morbidity and mortality, and often difficult to predict outcomes. Changes of HRV for brain injured patients have been reported in several studies. This study aimed to utilize the continuous HRV collection from admission across the first 24 h in the ICU in severe TBI patients to develop a patient outcome prediction system. RESULTS: A feature extraction strategy was applied to measure the HRV fluctuation during time. A prediction model was developed based on HRV measures with a genetic algorithm for feature selection. The result (AUC: 0.77) was compared with earlier reported scoring systems (highest AUC: 0.76), encouraging further development and practical application. CONCLUSIONS: The prediction models built with different feature sets indicated that HRV based parameters may help predict brain injury patient outcome better than the previously adopted illness severity scores.