Synthetic structural materials with exceptional mechanical performance suffer from either large weight and adverse environmental impact (for example, steels and alloys) or complex manufacturing processes and thus high cost (for example, polymer-based and biomimetic composites). Natural wood is a low-cost and abundant material and has been used for millennia as a structural material for building and furniture construction. However, the mechanical performance of natural wood (its strength and toughness) is unsatisfactory for many advanced engineering structures and applications. Pre-treatment with steam, heat, ammonia or cold rolling followed by densification has led to the enhanced mechanical performance of natural wood. However, the existing methods result in incomplete densification and lack dimensional stability, particularly in response to humid environments, and wood treated in these ways can expand and weaken. Here we report a simple and effective strategy to transform bulk natural wood directly into a high-performance structural material with a more than tenfold increase in strength, toughness and ballistic resistance and with greater dimensional stability. Our two-step process involves the partial removal of lignin and hemicellulose from the natural wood via a boiling process in an aqueous mixture of NaOH and NaSO followed by hot-pressing, leading to the total collapse of cell walls and the complete densification of the natural wood with highly aligned cellulose nanofibres. This strategy is shown to be universally effective for various species of wood. Our processed wood has a specific strength higher than that of most structural metals and alloys, making it a low-cost, high-performance, lightweight alternative.
Basic concepts in probability are employed to develop analytic formulae for both the expectation (bias) and variance for image motions obtained during subset-based pattern matching. Specifically, the expectation and variance in image motions in the presence of uncorrelated Gaussian intensity noise for each pixel location are obtained by optimising a least squares intensity matching metric. Results for both 1D and 2D image analyses clearly quantify both the bias and the covariance matrix for image motion estimates as a function of: (a) interpolation method, (b) sub-pixel motion, (c) intensity noise, (d) contrast, (e) level of uniaxial normal strain and (f) subset size. For 1D translations, excellent agreement is demonstrated between simulations, theoretical predictions and experimental measurements. The level of agreement confirms that the analytical formulae can be used to provide a priori estimates for the 'quality' of local, subset-based measurements achievable with a given pattern. For 1D strain with linear interpolation, theoretical predictions are provided for the expectation and co-variance matrix for the local displacement and strain parameters. For 2D translations with bi-linear interpolation, theoretical predictions are provided for both the expectation and the co-variance matrix for both displacement components. Theoretical results in both cases show that the expectations for the local parameters are biased and a function of: (a) the interpolation difference between the translated and reference images, (b) magnitude of white noise, (c) decimal part of the motion and (d) intensity pattern gradients. For 1D strain, the biases and the covariance matrix for both parameters are directly affected by the strain parameter p 1 as the deformed image is stretched by (1 + p 1 ). For 2D rigid body motion case, the covariance matrix for measured motions is shown to have coupling between the motions, demonstrating that the directions of maximum and minimum variability do not generally coincide with the x and y directions.KEY WORDS: digital image correlation/matching, error assessment, expectation and variance for image motion, intensity interpolation, intensity pattern noise, probabilistic formulation 1 The muted effect of quantisation on the measurement bias for an 8-bit signal is highlighted in the Discussion section.
In 1993, a new beryllium bearing bulk metallic glass with the nominal composition of Zr 41.25 Ti 13.75 Cu 12.5 Ni 10 Be 22.5 was discovered at Caltech. This metallic glass can be cast as cylindrical rods as large as 16 mm in diameter, which permitted specimens to be fabricated with geometries suitable for dynamic testing. For the first time, the dynamic compressive yield behavior of a metallic glass was characterized at strain rates of 10 2 to 10 4 ͞s by using the split Hopkinson pressure bar. A high-speed infrared thermal detector was also used to determine if adiabatic heating occurred during dynamic deformation of the metallic glass. From these tests it appears that the yield stress of the metallic glass is insensitive to strain rate and no adiabatic heating occurs before yielding.
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