scholarly journals Catalytic resonance theory: Negative dynamic surfaces for programmable catalysts

2022 ◽  
Author(s):  
Sallye R. Gathmann ◽  
M. Alexander Ardagh ◽  
Paul J. Dauenhauer
2021 ◽  
Author(s):  
Sallye Gathmann ◽  
M. Alexander Ardagh ◽  
Paul Dauenhauer

2019 ◽  
Author(s):  
M. Alexander Ardagh ◽  
Manish Shetty ◽  
Anatoliy Kuznetsov ◽  
Qi Zhang ◽  
Phillip Christopher ◽  
...  

Catalytic enhancement of chemical reactions via heterogeneous materials occurs through stabilization of transition states at designed active sites, but dramatically greater rate acceleration on that same active site is achieved when the surface intermediates oscillate in binding energy. The applied oscillation amplitude and frequency can accelerate reactions orders of magnitude above the catalytic rates of static systems, provided the active site dynamics are tuned to the natural frequencies of the surface chemistry. In this work, differences in the characteristics of parallel reactions are exploited via selective application of active site dynamics (0 < ΔU < 1.0 eV amplitude, 10<sup>-6</sup> < f < 10<sup>4</sup> Hz frequency) to control the extent of competing reactions occurring on the shared catalytic surface. Simulation of multiple parallel reaction systems with broad range of variation in chemical parameters revealed that parallel chemistries are highly tunable in selectivity between either pure product, even when specific products are not selectively produced under static conditions. Two mechanisms leading to dynamic selectivity control were identified: (i) surface thermodynamic control of one product species under strong binding conditions, or (ii) catalytic resonance of the kinetics of one reaction over the other. These dynamic parallel pathway control strategies applied to a host of chemical conditions indicate significant potential for improving the catalytic performance of many important industrial chemical reactions beyond their existing static performance.


Langmuir ◽  
2021 ◽  
Author(s):  
Gaoyan Mu ◽  
C. K. Pandiyarajan ◽  
Xiuyuan Lu ◽  
Matt Weaver ◽  
Jan Genzer ◽  
...  

Author(s):  
Jean-François Bony ◽  
Laurent Michel ◽  
Thierry Ramond

Nature ◽  
1934 ◽  
Vol 133 (3374) ◽  
pp. 983-983
Author(s):  
E. B. WEDMORE

2014 ◽  
Vol 543-547 ◽  
pp. 1934-1938
Author(s):  
Ming Xiao

For a clustering algorithm in two-dimension spatial data, the Adaptive Resonance Theory exists not only the shortcomings of pattern drift and vector module of information missing, but also difficultly adapts to spatial data clustering which is irregular distribution. A Tree-ART2 network model was proposed based on the above situation. It retains the memory of old model which maintains the constraint of spatial distance by learning and adjusting LTM pattern and amplitude information of vector. Meanwhile, introducing tree structure to the model can reduce the subjective requirement of vigilance parameter and decrease the occurrence of pattern mixing. It is showed that TART2 network has higher plasticity and adaptability through compared experiments.


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