681 research outputs found
Ti-6Al-4V β Phase Selective Dissolution: In Vitro Mechanism and Prediction
Retrieval studies document Ti-6Al-4V β phase dissolution within total hip replacement systems. A gap persists in our mechanistic understanding and existing standards fail to reproduce this damage. This thesis aims to (1) elucidate the Ti-6Al-4V selective dissolution mechanism as functions of solution chemistry, electrode potential and temperature; (2) investigate the effects of adverse electrochemical conditions on additively manufactured (AM) titanium alloys and (3) apply machine learning to predict the Ti-6Al-4V dissolution state. We hypothesized that (1) cathodic activation and inflammatory species (H2O2) would degrade the Ti-6Al-4V oxide, promoting dissolution; (2) AM Ti-6Al-4V selective dissolution would occur and (3) near field electrochemical impedance spectra (nEIS) would distinguish between dissolved and polished Ti-6Al-4V, allowing for deep neural network prediction. First, we show a combinatorial effect of cathodic activation and inflammatory species, degrading the oxide film’s polarization resistance (Rp) by a factor of 105 Ωcm2 (p = 0.000) and inducing selective dissolution. Next, we establish a potential range (-0.3 V to –1 V) where inflammatory species, cathodic activation and increasing solution temperatures (24 oC to 55 oC) synergistically affect the oxide film. Then, we evaluate the effect of solution temperature on the dissolution rate, documenting a logarithmic dependence. In our second aim, we show decreased AM Ti-6Al-4V Rp when compared with AM Ti-29Nb-21Zr in H2O2. AM Ti-6Al-4V oxide degradation preceded pit nucleation in the β phase. Finally, in our third aim, we identified gaps in the application of artificial intelligence to metallic biomaterial corrosion. With an input of nEIS spectra, a deep neural network predicted the surface dissolution state with 96% accuracy. In total, these results support the inclusion of inflammatory species and cathodic activation in pre-clinical titanium devices and biomaterial testing
Finding and Recommending Scholarly Articles
The rate at which scholarly literature is being produced has been increasing
at approximately 3.5 percent per year for decades. This means that during a
typical 40 year career the amount of new literature produced each year
increases by a factor of four. The methods scholars use to discover relevant
literature must change. Just like everybody else involved in information
discovery, scholars are confronted with information overload. Two decades ago,
this discovery process essentially consisted of paging through abstract books,
talking to colleagues and librarians, and browsing journals. A time-consuming
process, which could even be longer if material had to be shipped from
elsewhere. Now much of this discovery process is mediated by online scholarly
information systems. All these systems are relatively new, and all are still
changing. They all share a common goal: to provide their users with access to
the literature relevant to their specific needs. To achieve this each system
responds to actions by the user by displaying articles which the system judges
relevant to the user's current needs. Recently search systems which use
particularly sophisticated methodologies to recommend a few specific papers to
the user have been called "recommender systems". These methods are in line with
the current use of the term "recommender system" in computer science. We do not
adopt this definition, rather we view systems like these as components in a
larger whole, which is presented by the scholarly information systems
themselves. In what follows we view the recommender system as an aspect of the
entire information system; one which combines the massive memory capacities of
the machine with the cognitive abilities of the human user to achieve a
human-machine synergy.Comment: 14 pages, part of the forthcoming MIT book "Bibliometrics and Beyond:
Metrics-Based Evaluation of Scholarly Research" edited by Blaise Cronin and
Cassidy R. Sugimot
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