TOLEDO ROMÁN, IGNACIO GALVARINO
Preferred name
TOLEDO ROMÁN, IGNACIO GALVARINO
Main Affiliation
Email
itoledor@udd.cl
ORCID
0000-0002-7657-4241
Scopus Author ID
58879857000
4 results
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Item type:Publication, Advances in the Sustainable Development of Biobased Materials Using Plant and Animal Waste as Raw Materials: A Review(2024); ; There is substantial concern about critical environmental problems related to waste in production sectors such as textile, construction, and packaging. The materials ascribed to the sector’s unsustainability are primarily fabrics, plastic, and hazardous solvents, making developing new biobased materials imperative. As such, various strategies have been investigated to convert and recycle waste and give them commercial value via the manufacture of biobased materials. This review discusses the various types of raw materials as sources to develop new biobased materials that could promote the transition toward sustainability. According to the literature, the functional qualities of biobased materials are comparable to those of synthetic materials. Raw material sources such as biomass, derived from plant and animal-based waste, are attractive due to their low cost, abundance, and biodegradability. The manufacture of biomaterials, as well as their characterization and performance, are also discussed. Further, this review will offer a comprehensive view of the potential applicability and current commercial applications of the developed biobased materials in relevant areas such as packaging, construction, textile, and wastewater remediation. This could be a potential field of research to address the environmental challenges posed by the continuous growth of the global population.Scopus© Citations 23 2 - Some of the metrics are blocked by yourconsent settings
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Item type:Publication, Learning Analytics to Explore Dropout in Online Entrepreneurship Education(2020); 2 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Text-based link prediction in social networksThis thesis aims to contribute to the literature on text-based link prediction methods in scientific collaboration networks with two original works. First, in Chapter I we present a comprehensive survey on text-based link prediction methods in social networks. And second, in Chapter II we introduce a simple but effective approach to improve the performance of text-based link prediction models1