Mechanical properties of graphene flake-reinforced Y₂O₃ ceramics

W pracy badano wpływ płatków grafenowych na właściwości mechaniczne kompozytu Y2O3 – grafen w funkcji sposobu przygotowania zawiesin tlenku grafenu GO oraz jego zawartości w kompozycie. Do otrzymania próbek użyto handlowy nanometryczny proszek Y2O3 o czystości 99,99% i GO otrzymany w ITME. Kompozyty otrzymano na bazie wodnej mieszaniny obu składników, którą spiekano po wysuszeniu w piecu Astro pod jednoosiowym ciśnieniem i metodą SPS. Wykonano kompozyty o zawartości wagowej GO 1 i 3%. Spektroskopia Ramana potwierdziła obecność zredukowanego tlenku grafenu w otrzymanych kompozytach. Poza pojedynczymi przypadkami sposób przygotowania zawiesin GO nie miał wpływu na wartości mierzonych właściwości mechanicznych. Stwierdzono, że w funkcji zawartości GO dla próbek spiekanych w piecu Astro twardość oraz moduł Younga nieznacznie maleją, wytrzymałość na zginanie rośnie maksymalnie o ok. 30% dla 3% GO. Odporność na pękanie mierzona na belkach z karbem nieznacznie maleje w funkcji zawartości GO, ale za to rośnie odporność na pękanie mierzona metodą Vickersa (o ok. 50%). Odporność na pękanie próbek spiekanych metodą SPS rośnie maksymalnie ok. 80% (dla obu metod pomiaru). Zaobserwowany na zdjęciach pęknięć Vickersa mechanizm wzmacniania przez płatki GO, polegał na skręcaniu płaszczyzny pękania i blokowaniu jego propagacji.The influence of graphene flakes on the mechanical properties of Y2O3 – graphene composite as a function of the preparation method of the suspensions of graphene oxide GO and its content was studied. To obtain samples, a commercial nano-sized Y2O3 powder with a purity of 99.99% and GO fabricated at ITME were used. The composites were based on an aqueous mixture of both components. They were sintered after drying under uniaxial pressure in an Astro furnace and an SPS machine. The GO weight content in the case of these composites was 1 and 3%. Raman spectroscopy confirmed the presence of reduced graphene oxide in the resultant composites. Besides isolated cases,the preparation of the GO suspensions did not affect the measured mechanical properties. It was found that for the samples sintered in the Astro furnace both hardness and Young's modulus as function of the GO content were slightly reduced, whereas the bending strength increased to approx. 30% for 3% GO. In addition, the fracture toughness measured at the notched beams decreased slightly as a function of the GO content but grew (about 50%) for the fracture toughness measured by the Vickers method. The fracture toughness of the samples sintered in the SPS machine increased up to about 80% for both measurement methods. The mechanism of reinforcing the material with graphene flakes observed in the pictures of the Vickers cracks was based on crack deflection and crack blocking

The Cryptographic Power of Random Selection

The principle of random selection and the principle of adding biased noise are new paradigms used in several recent papers for constructing lightweight RFID authentication protocols. The cryptographic power of adding biased noise can be characterized by the hardness of the intensively studied Learning Parity with Noise (LPN) Problem. In analogy to this, we identify a corresponding learning problem called RandomSelect for random selection and study its complexity. Given L secret linear functions f_1,...,f_L : {0,1}^n -> {0,1}^a, RandomSelect(L,n,a) denotes the problem of learning f_1,...,f_L from values (u,f_l(u)), where the secret indices l \in {1,...,L} and the inputs u \in {0,1}^n are randomly chosen by an oracle. We take an algebraic attack approach to design a nontrivial learning algorithm for this problem, where the running time is dominated by the time needed to solve full-rank systems of linear equations over O(n^L) unknowns. In addition to the mathematical findings relating correctness and average running time of the suggested algorithm, we also provide an experimental assessment of our results