Coalgebra Learning via Duality

Automata learning is a popular technique for inferring minimal automata through membership and equivalence queries. In this paper, we generalise learning to the theory of coalgebras. The approach relies on the use of logical formulas as tests, based on a dual adjunction between states and logical theories. This allows us to learn, e.g., labelled transition systems, using Hennessy-Milner logic. Our main contribution is an abstract learning algorithm, together with a proof of correctness and termination

Angluin learning via logic

In this paper we will provide a fresh take on Dana Angluin's algorithm for learning using ideas from coalgebraic modal logic. Our work opens up possibilities for applications of tools & techniques from modal logic to automata learning and vice versa. As main technical result we obtain a generalisation of Angluin's original algorithm from DFAs to coalgebras for an arbitrary finitary set functor T in the following sense: given a (possibly infinite) pointed T-coalgebra that we assume to be regular (i.e. having an equivalent finite representation) we can learn its finite representation by asking (i) "logical queries" (corresponding to membership queries) and (ii) making conjectures to which the teacher has to reply with a counterexample. This covers (a known variant) of the original L* algorithm and the learning of Mealy/Moore machines. Other examples are bisimulation quotients of (probabilistic) transition systems

Effect of oxygen functionalities on the hydrous hydrazine decomposition over carbonaceous materials

Metal-free heterogeneous catalysis is promising in the context of H2 generation. Therefore, establishing structure-activity relationships is a crucial issue to improve the development of more efficient catalysts. Herein, to evaluate the reactivity of the oxygen functionalities in carbonaceous materials, functionalized commercial pyrolytically stripped carbon nanofibers (CNFs) were used as catalysts in the liquid-phase hydrous hydrazine decomposition process and compared its activity to the one of pristine CNF material. Different oxygenated groups were inserted by treating CNFs with hydrogen peroxide for 1h (O1-H2O2) and HNO3 for 1h (O1-HNO3) and 6h (O6-HNO3). An increase in activity was observed as a function of the oxidizing agent strength (HNO3 > H2O2) and the functionalization time (6h > 1h). A thorough characterization of the catalysts demonstrated that the activity could be directly correlated with the oxygen content (O6-HNO3 > O1-HNO3 > O1-H2O2 > CNFs) and pointed out the active sites for the reaction at carbon-oxygen double bond groups (C=O and COOH). Systematic DFT calculations supported rationalizing the experimental kinetic trends with respect to each oxygen group (C=O, C-O-C, C-OH and COOH)

Selective decomposition of hydrazine over metal free carbonaceous materials

Herein we report a combined experimental and computational investigation unravelling the hydrazine hydrate decomposition reaction on metal-free catalysts. The study focuses on commercial graphite and two different carbon nanofibers, pyrolytically stripped (CNF-PS) and high heat-treated (CNF-HHT), respectively, treated at 700 and 3000 °C to increase their intrinsic defects. Raman spectroscopy demonstrated a correlation between the initial catalytic activity and the intrinsic defectiveness of carbonaceous materials. CNF-PS with higher defectivity (ID/IG = 1.54) was found to be the best performing metal-free catalyst, showing a hydrazine conversion of 94% after 6 hours of reaction and a selectivity to H2 of 89%. In addition, to unveil the role of NaOH, CNF-PS was also tested in the absence of alkaline solution, showing a decrease in the reaction rate and selectivity to H2. Density functional theory (DFT) demonstrated that the single vacancies (SV) present on the graphitic layer are the only active sites promoting hydrazine decomposition, whereas other defects such as double vacancy (DV) and Stone–Wales (SW) defects are unable to adsorb hydrazine fragments. Two symmetrical and one asymmetrical dehydrogenation pathways were found, in addition to an incomplete decomposition pathway forming N2 and NH3. On the most stable hydrogen production pathway, the effect of the alkaline medium was elucidated through calculations concerning the diffusion and recombination of atomic hydrogen. Indeed, the presence of NaOH helps the extraction of H species without additional energetic barriers, as opposed to the calculations performed in a polarizable continuum medium. Considering the initial hydrazine dissociative adsorption, the first step of the dehydrogenation pathway is more favourable than the scission of the N–N bond, which leads to NH3 as the product. This first reaction step is crucial to define the reaction mechanisms and the computational results are in agreement with the experimental ones. Moreover, comparing two different hydrogen production pathways (with and without diffusion and recombination), we confirmed that the presence of sodium hydroxide in the experimental reaction environment can modify the energy gap between the two pathways, leading to an increased reaction rate and selectivity to H2