Some observations describing the electrochemical surface annealing of austenitic stainless steel

Cold-worked austenitic stainless steels have been subject to a pulsed electrochemical treatment in fairly concentrated aqueous solutions of sodium nitrite. The electrochemical reactions that occur transform the strain-induced martensite phase, originally formed by the cold work, back to the austenite phase. However, unlike the conventional thermal annealing process, electrochemically induced surface annealing also hardens the surface of the alloy. Because the process causes transformation of the surface martensite, we term it "electrochemical surface annealing", despite the fact that it results in an increase in surface hardness.

Stress reduction and bond stability during thermal annealing of tetrahedral amorphous carbon

A comprehensive study of the stress release and structural changes caused by postdeposition thermal annealing of tetrahedral amorphous carbon (ta-C) on Si has been carried out. Complete stress relief occurs at 600-700°C and is accompanied by minimal structural modifications, as indicated by electron energy loss spectroscopy, Raman spectroscopy, and optical gap measurements. Further annealing in vacuum converts sp3 sites to sp2 with a drastic change occurring after 1100°C. The field emitting behavior is substantially retained up to the complete stress relief, confirming that ta-C is a robust emitting material. © 1999 American Institute of Physics.

Rigorous finite-time guarantees for optimization on continuous domains by simulated annealing

Simulated annealing is a popular method for approaching the solution of a global optimization problem. Existing results on its performance apply to discrete combinatorial optimization where the optimization variables can assume only a finite set of possible values. We introduce a new general formulation of simulated annealing which allows one to guarantee finite-time performance in the optimization of functions of continuous variables. The results hold universally for any optimization problem on a bounded domain and establish a connection between simulated annealing and up-to-date theory of convergence of Markov chain Monte Carlo methods on continuous domains. This work is inspired by the concept of finite-time learning with known accuracy and confidence developed in statistical learning theory.