Daniel José da Silva

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Faculdade de Ciências da Universidade de Lisboa

Portugal

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Recent Grants

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Solar green hydrogen fuel Flexi-PEC ́s: Flexible photoelectrochemical cells for water splitting

Open Date: 2023-01-01

Close Date:

Grant: Close

Solar green hydrogen fuel Flexi-PEC ´s: Flexible photoelectrochemical cells for water splitting

Open Date: 2023-01-01

Close Date:

Grant: Close

Demagnetizing-based magnetocaloric refrigeration

Open Date: 2021-02-01

Close Date:

Grant: Close

Demagnetizing-based magnetocaloric refrigeration

Open Date: 2021-02-01

Close Date:

Grant: Close

Material’s Research with Radioactive Isotopes and Nuclear Techniques at ISOLDE-CERN

Open Date: 2021-01-01

Close Date:

Articles (12)

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2025

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2025

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2024

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2023

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2022

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2022

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2022

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2022

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2021

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2021

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2020

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2020

Collaborators (3)

João Horta Belo

PORTUGAL

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João Ventura

PORTUGAL

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Joana Oliveira

Professora Associada

Universidade do Porto Faculdade de Engenharia

PORTUGAL

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Daniel José da Silva

Research Interests

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Recent Grants

Articles (12)

Solid-state magnetic refrigerator based on the demagnetizing effect

Optimization of a novel magnetic refrigerator based on the demagnetizing effect using a particle swarm-like algorithm

Magnetic refrigeration enhanced by magnetically-activated thermal switch: An experimental proof-of-concept

Rod mangle rotation patterns for adjustable magnetic field generation

Hybridizing Triboelectric and Thermomagnetic Effects: A Novel Low‐Grade Thermal Energy Harvesting Technology

Modeling the Transient Response of Thermal Circuits

Predicting the magnetic measurements of first- and second-order phase transition magnetocaloric materials with artificial neural networks

Numerical simulation and optimization of a solid state thermal diode based on shape-memory alloys

Thermal switching requirements for solid state magnetic refrigeration

Caloric devices: A review on numerical modeling and optimization strategies

Broad Multi-Parameter Dimensioning of Magnetocaloric Systems Using Statistical Learning Classifiers

Predicting the performance of magnetocaloric systems using machine learning regressors

Collaborators (3)

João Horta Belo

João Ventura

Joana Oliveira

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