Esplorado

Esplorado

catalyzing research

Explore scientific publications via visual representaiton of key content indicators

A novel approach

Esplorado proposes a novel literature research method, in which the scientific publications are sorted by key-performance-indicators (KPIs), in contrast to the common title and abstract key-word based research engines.Esplorado scrapes scientific journals content and classifies the key-content-indicators.
Via the web portal, the users can interactively visualize the properties directly from the overview, and only then select the journals to read. Moreover, the digitalization of the KPI properties allows the use of novel analytic tools to relate material and process properties.Case study: Catalysis implies the improvement of the chemical reactions efficiency. A catalyst material lowers the energy barriers of the reaction intermediates, promoting the reaction rate and the selectivity. Catalytic materials are key components for the transition to a greener and sustainable future.
The performance of a catalytic material can be described by few numeric parameters such as activity, selectivity and stability.The scope of the project further explore the analysis and comparison of all the data related to catalytic processes, such as catalytic material syntheses, their support on electrodes or reactor bed and the reactor designs.

Benefits: the customer benefits of higher time efficiency and quality of information during literature research.

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FOUNDERS

Matteo Miola & Francesco Pasetto

Matteo Miola is a passionate scientist with the dream of developing a novel literature research method.
Currently, he is R&D manager at Enphos, developing H2 electrolyzers and solar fuel production processe. During his postdoctoral research at the Rijksuniversiteit Groningen (RUG), he developed electrocatalysts for the H2O-to-H2O2 (RELEASE, NL) and CO2-to-formate (RECODE, EU) conversion. He obtained his PhD in nanoscience at the Carbon dioxide activation centre (CADIAC) in Aarhus University (DK) and his Master’s degree (cum laude) in Material Science and Engineering at Università degli Studi di Padova (IT).

Francesco Pasetto is a dynamic innovator with a vision to bridge the gap between technology and impactful solutions.
Currently, he is a Machine Learning Engineer at Arca Assicurazioni/Unipol, where he specializes in developing cutting-edge AI models for predictive analytics and automation. His journey began with a Master’s degree in Data Science from Università degli Studi di Padova (IT), where he honed his expertise in data-driven technologies. Francesco has a deep passion for leveraging data science and machine learning to solve complex problems, and he continuously seeks opportunities to drive technological advancements that make a tangible difference.