Surface chemistry of 2,3-dibromosubstituted norbornadiene/quadricyclane as molecular solar thermal energy storage system on Ni(111).

Dwindling fossil fuels force humanity to search for new energy production routes. Besides energy generation, its storage is a crucial aspect. One promising approach is to store energy from the sun chemically in strained organic molecules, so-called molecular solar thermal (MOST) systems, which can release the stored energy catalytically. A prototypical MOST system is norbornadiene/quadricyclane (NBD/QC) whose energy release and surface chemistry need to be understood. Besides important key parameters such as molecular weight, endergonic reaction profiles, and sufficient quantum yields, the position of the absorption onset of NBD is crucial to cover preferably a large range of sunlight's spectrum. For this purpose, one typically derivatizes NBD with electron-donating and/or electron-accepting substituents. To keep the model system simple enough to be investigated with photoemission techniques, we introduced bromine atoms at the 2,3-position of both compounds. We study the adsorption behavior, energy release, and surface chemistry on Ni(111) using high-resolution X-ray photoelectron spectroscopy (HR-XPS), UV photoelectron spectroscopy, and density functional theory calculations. Both Br2-NBD and Br2-QC partially dissociate on the surface at ∼120 K, with Br2-QC being more stable. Several stable adsorption geometries for intact and dissociated species were calculated, and the most stable structures are determined for both molecules. By temperature-programmed HR-XPS, we were able to observe the conversion of Br2-QC to Br2-NBD in situ at 170 K. The decomposition of Br2-NBD starts at 190 K when C-Br bond cleavage occurs and benzene and methylidene are formed. For Br2-QC, the cleavage already occurs at 130 K when cycloreversion to Br2-NBD sets in.

A learning process of the matching identification problem.

Recently, Mehrez and Steinberg (1995) described and studied the matching identification problem (MIP). The MIP is a form of knowledge acquisition problem from the field of artificial intelligence. For instance, an expert system infers knowledge from a set of examples. But how do you most quickly acquire the examples that knowledge is inferred from? The MIP is a special case of this problem. Although an optimal algorithm was not found by Mehrez and Steinberg, they described two general types of heuristics. We describe in this paper an optimal algorithm for the case of K=2, and an improved heuristic for general K, which identifies a chosen subset with 6% fewer inquiries on average when N=15, K=3. The heuristic improves relative to the Type I heuristic as N increases, K held constant. The improved heuristic is concerned with the symbols yet unclassified as being in the chosen subset or not in the chosen subset. By inquiring subsets with all unclassified symbols, we most quickly "span" the set of unclassified numbers. Closed form equations are developed for the expected number of inquiries required and the variance of the number of inquiries required for the optimal algorithm. Computational studies are provided for Mehrez and Steinberg's Type I heuristics, the K=2 optimal algorithm, and the spanning heuristic.