366 research outputs found
Applications of artificial neural networks (ANNs) in several different materials research fields
PhDIn materials science, the traditional methodological framework is the
identification of the composition-processing-structure-property causal pathways
that link hierarchical structure to properties. However, all the properties of
materials can be derived ultimately from structure and bonding, and so the
properties of a material are interrelated to varying degrees.
The work presented in this thesis, employed artificial neural networks (ANNs) to
explore the correlations of different material properties with several examples in
different fields. Those including 1) to verify and quantify known correlations
between physical parameters and solid solubility of alloy systems, which were
first discovered by Hume-Rothery in the 1930s. 2) To explore unknown crossproperty
correlations without investigating complicated structure-property
relationships, which is exemplified by i) predicting structural stability of
perovskites from bond-valence based tolerance factors tBV, and predicting
formability of perovskites by using A-O and B-O bond distances; ii) correlating
polarizability with other properties, such as first ionization potential, melting
point, heat of vaporization and specific heat capacity. 3) In the process of
discovering unanticipated relationships between combination of properties of
materials, ANNs were also found to be useful for highlighting unusual data
points in handbooks, tables and databases that deserve to have their veracity
inspected. By applying this method, massive errors in handbooks were found,
and a systematic, intelligent and potentially automatic method to detect errors in
handbooks is thus developed.
Through presenting these four distinct examples from three aspects of ANN
capability, different ways that ANNs can contribute to progress in materials
science has been explored. These approaches are novel and deserve to be pursued
as part of the newer methodologies that are beginning to underpin material
research
Quantification and prediction of the concentration of different dilutions of Lambda Cyhalothrin through colorimetry and neural networks
The Lambda Cyhalothrin is an insecticide of broad spectrum used in agriculture, to reduce the loss in crops, due to the attack of some pests. This compound has the presence of the radicals chlorine, fluorine and cyano, which can cause serious effects on human health when are ingested. Because of this, exist the need of develop non - destructive methods, capable of determining the concentration of the pesticide in farming, for eradicate the presence of this substance on the fruit used as food. To achieve this, commercial Lambda Cyhalothrin and distilled water were used, to obtain the recommended dilutions for the treatment of various pests in agriculture. The samples were analyzed through colorimetry, obtaining the characteristic color spaces for the pesticide, with a correlation of 0.92 for the parameters "a" and "b", and 0.98 for the parameter "L". The Cab chroma and Hue angle were determined in 9.72 and 275° respectively for the pure compound. in the dilution, the value of Hue angle decreases until 220°. Through neural networks in Matlab, the relationship between the reflection spectrum of the dilutions with the concentration thereof was established. Estimating a prediction in the accuracy higher than 0.98 in the coefficient of determination
Discovery of Materials Through Applied Machine Learning
Advances in artificial intelligence technology, specifically machine learning, have cre- ated opportunities in the material sciences to accelerate material discovery and gain fundamental understanding of the interaction between certain the constituent ele- ments of a material and the properties expressed by that material. Application of machine learning to experimental materials discovery is slow due to the monetary and temporal cost of experimental data, but parallel techniques such as continuous com- positional gradients or high-throughput characterization setups are capable of gener- ating larger amounts of data than the typical experimental process, and therefore are suitable for combination with machine learning. A random forest machine learning algorithm has been applied to two different materials discovery challenges - discovery of new metallic glass forming ternary compositions and discovery of novel ammonia decomposition catalysts - and has led to accelerated discovery of high-performing materials
Interface Oral Health Science 2016: Innovative Research on Biosis–Abiosis Intelligent Interface
Dentistry; Oral and Maxillofacial Surgery; Regenerative Medicine/Tissue Engineerin
Faculty Publications and Creative Works 2003
Faculty Publications & Creative Works is an annual compendium of scholarly and creative activities of University of New Mexico faculty during the noted calendar year. It serves to illustrate the robust and active intellectual pursuits conducted by the faculty in support of teaching and research at UNM
Proceedings of the Scientific-Practical Conference "Research and Development - 2016"
talent management; sensor arrays; automatic speech recognition; dry separation technology; oil production; oil waste; laser technolog
Faculty Publications and Creative Works 2004
Faculty Publications & Creative Works is an annual compendium of scholarly and creative activities of University of New Mexico faculty during the noted calendar year. Published by the Office of the Vice President for Research and Economic Development, it serves to illustrate the robust and active intellectual pursuits conducted by the faculty in support of teaching and research at UNM
Bionano-Interfaces through Peptide Design
The clinical success of restoring bone and tooth function through implants critically depends on the maintenance of an infection-free, integrated interface between the host tissue and the biomaterial surface. The surgical site infections, which are the infections within one year of surgery, occur in approximately 160,000-300,000 cases in the US annually. Antibiotics are the conventional treatment for the prevention of infections. They are becoming ineffective due to bacterial antibiotic-resistance from their wide-spread use. There is an urgent need both to combat bacterial drug resistance through new antimicrobial agents and to limit the spread of drug resistance by limiting their delivery to the implant site. This work aims to reduce surgical site infections from implants by designing of chimeric antimicrobial peptides to integrate a novel and effective delivery method. In recent years, antimicrobial peptides (AMPs) have attracted interest as natural sources for new antimicrobial agents. By being part of the immune system in all life forms, they are examples of antibacterial agents with successfully maintained efficacy across evolutionary time. Both natural and synthetic AMPs show significant promise for solving the antibiotic resistance problems. In this work, AMP1 and AMP2 was shown to be active against three different strains of pathogens in Chapter 4. In the literature, these peptides have been shown to be effective against multi-drug resistant bacteria. However, their effective delivery to the implantation site limits their clinical use. In recent years, different groups adapted covalent chemistry-based or non-specific physical adsorption methods for antimicrobial peptide coatings on implant surfaces. Many of these procedures use harsh chemical conditions requiring multiple reaction steps. Furthermore, none of these methods allow the orientation control of these molecules on the surfaces, which is an essential consideration for biomolecules. In the last few decades, solid binding peptides attracted high interest due to their material specificity and self-assembly properties. These peptides offer robust surface adsorption and assembly in diverse applications. In this work, a design method for chimeric antimicrobial peptides that can self-assemble and self-orient onto biomaterial surfaces was demonstrated. Three specific aims used to address this two-fold strategy of self-assembly and self-orientation are: 1) Develop classification and design methods using rough set theory and genetic algorithm search to customize antibacterial peptides; 2) Develop chimeric peptides by designing spacer sequences to improve the activity of antimicrobial peptides on titanium surfaces; 3) Verify the approach as an enabling technology by expanding the chimeric design approach to other biomaterials. In Aim 1, a peptide classification tool was developed because the selection of an antimicrobial peptide for an application was difficult among the thousands of peptide sequences available. A rule-based rough-set theory classification algorithm was developed to group antimicrobial peptides by chemical properties. This work is the first time that rough set theory has been applied to peptide activity analysis. The classification method on benchmark data sets resulted in low false discovery rates. The novel rough set theory method was combined with a novel genetic algorithm search, resulting in a method for customizing active antibacterial peptides using sequence-based relationships. Inspired by the fact that spacer sequences play critical roles between functional protein domains, in Aim 2, chimeric peptides were designed to combine solid binding functionality with antimicrobial functionality. To improve how these functions worked together in the same peptide sequence, new spacer sequences were engineered. The rough set theory method from Aim 1 was used to find structure-based relationships to discover new spacer sequences which improved the antimicrobial activity of the chimeric peptides. In Aim 3, the proposed approach is demonstrated as an enabling technology. In this work, calcium phosphate was tested and verified the modularity of the chimeric antimicrobial self-assembling peptide approach. Other chimeric peptides were designed for common biomaterials zirconia and urethane polymer. Finally, an antimicrobial peptide was engineered for a dental adhesive system toward applying spacer design concepts to optimize the antimicrobial activity
Proceedings of the Scientific-Practical Conference "Research and Development - 2016"
talent management; sensor arrays; automatic speech recognition; dry separation technology; oil production; oil waste; laser technolog
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