Elias Chatzitheodoridis
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Elias Chatzitheodoridis has a total of 251 co-authors. Here are the top co-authors by number of shared publications:
G. Popovic
Inst. for Micro Technique & Precision Eng., Vienna Univ. of Technol., Austria
6 shared publicationsW. Brenner
6 shared publications- Ákos Kereszturi
Research Centre for Astronomy and Earth Sciences
5 shared publications - Bernard Foing
Leiden Observatory, Leiden University, P.O. Box 9513, 2300 RA Leiden, Netherlands, The Netherlands
4 shared publications D. Petrovic
4 shared publications- Hector-Andreas Stavrakakis
National Technical University of Athens
4 shared publications
Latest Publications
- Open Access
- 2 July 2026
- Published by Copernicus GmbH
Connecting Meteorite Spectra to Lunar Surface Composition Using Hyperspectral Imaging and Machine Learning
We present an innovative, cost-effective framework integrating laboratory Hyperspectral Imaging (HSI) of the Bechar010 Lunar meteorite with ground-based lunar HSI and supervised Machine Learning(ML) to generate high-fidelity mineralogical maps. A 3mm thin section of Bechar010 was imaged under a microscope with a 30mm focal length lens at 150mm working distance, using 6x binning to increase the signal-to-noise ratio, producing a data cube (X × Y × λ = 791×1024×224, 0.24mm × 0.2mm resolution) across 400-1000}nm (224 bands, 2.7nm spectral sampling, 5.5nm full width at half maximum spectral resolution) using a Specim FX10 camera. Ground-based lunar HSI was captured with a Celestron 8SE telescope (3km/pixel), yielded a data cube (371×1024×224). Solar calibration was performed using a Spectralon reference ({99}\% reflectance {
- 2 July 2026
- Published by Copernicus GmbH
Open AccessConnecting Meteorite Spectra to Lunar Surface Composition Using Hyperspectral Imaging and Machine Learning
We present an innovative, cost-effective framework integrating laboratory Hyperspectral Imaging (HSI) of the Bechar010 Lunar meteorite with ground-based lunar HSI and supervised Machine Learning(ML) to generate high-fidelity mineralogical maps. A 3mm thin section of Bechar010 was imaged under a microscope with a 30mm focal length lens at 150mm working distance, using 6x binning to increase the signal-to-noise ratio, producing a data cube (X × Y × λ = 791×1024×224, 0.24mm × 0.2mm resolution) across 400-1000}nm (224 bands, 2.7nm spectral sampling, 5.5nm full width at half maximum spectral resolution) using a Specim FX10 camera. Ground-based lunar HSI was captured with a Celestron 8SE telescope (3km/pixel), yielded a data cube (371×1024×224). Solar calibration was performed using a Spectralon reference ({99}\% reflectance {
- Open Access
Connecting Meteorite Spectra to Lunar Surface Composition Using Hyperspectral Imaging and Machine Learning
We present an innovative, cost-effective framework integrating laboratory Hyperspectral Imaging (HSI) of the Bechar 010 Lunar meteorite with ground-based lunar HSI and supervised Machine Learning (ML) to generate high-fidelity mineralogical maps. A 3 mm thin section of Bechar 010 was imaged under a microscope with a 30 mm focal length lens at 150 mm working distance, using 6x binning to increase the signal-to-noise ratio, producing a data cube (X × Y × = , mm × mm resolution) across 400 nm to 1000 nm (224 bands, nm spectral sampling, nm full width at half maximum spectral resolution) using a Specim FX10 camera. Ground-based lunar HSI was captured with a Celestron 8SE telescope (3 km/pixel), yielded a data cube (). Solar calibration was performed using a Spectralon reference (99% reflectance < 2% error) ensured accurate reflectance spectra. A Support Vector Machine (SVM) with a radial basis function kernel, trained on expert-labeled spectra, achieved 93.7% classification accuracy (5-fold cross-validation) for olivine (92% precision, 90% recall) and pyroxene (88% precision, 86% recall) in Bechar 010. LIME analysis identified key wavelengths (e.g., 485 nm, 22.4% for M3; 715 nm, 20.6% for M6) across 10 pre-selected regions (M1 to M10), indicating olivine-rich (Highland-like) and pyroxene-rich (Mare-like) compositions. SAM analysis revealed angles from rad to rad, linking M3 and M9 to Highlands and M6 and M10 to Mares. K-means clustering of Lunar data identified 10 mineralogical clusters (88% accuracy), validated against Chandrayaan-1 Moon mineralogy Mapper () data (140 m/pixel, 10 nm spectral resolution). A novel push-broom HSI approach with a telescope achieves 0.8 arcsec resolution for lunar spectroscopy, inspiring full-sky multi-object spectral mapping.Open AccessConnecting Meteorite Spectra to Lunar Surface Composition Using Hyperspectral Imaging and Machine Learning
We present an innovative, cost-effective framework integrating laboratory Hyperspectral Imaging (HSI) of the Bechar 010 Lunar meteorite with ground-based lunar HSI and supervised Machine Learning (ML) to generate high-fidelity mineralogical maps. A 3 mm thin section of Bechar 010 was imaged under a microscope with a 30 mm focal length lens at 150 mm working distance, using 6x binning to increase the signal-to-noise ratio, producing a data cube (X × Y × = , mm × mm resolution) across 400 nm to 1000 nm (224 bands, nm spectral sampling, nm full width at half maximum spectral resolution) using a Specim FX10 camera. Ground-based lunar HSI was captured with a Celestron 8SE telescope (3 km/pixel), yielded a data cube (). Solar calibration was performed using a Spectralon reference (99% reflectance < 2% error) ensured accurate reflectance spectra. A Support Vector Machine (SVM) with a radial basis function kernel, trained on expert-labeled spectra, achieved 93.7% classification accuracy (5-fold cross-validation) for olivine (92% precision, 90% recall) and pyroxene (88% precision, 86% recall) in Bechar 010. LIME analysis identified key wavelengths (e.g., 485 nm, 22.4% for M3; 715 nm, 20.6% for M6) across 10 pre-selected regions (M1 to M10), indicating olivine-rich (Highland-like) and pyroxene-rich (Mare-like) compositions. SAM analysis revealed angles from rad to rad, linking M3 and M9 to Highlands and M6 and M10 to Mares. K-means clustering of Lunar data identified 10 mineralogical clusters (88% accuracy), validated against Chandrayaan-1 Moon mineralogy Mapper () data (140 m/pixel, 10 nm spectral resolution). A novel push-broom HSI approach with a telescope achieves 0.8 arcsec resolution for lunar spectroscopy, inspiring full-sky multi-object spectral mapping. - Open Access
- 17 March 2026
- Published by MDPI AG in Preprints.org
Connecting Meteorite Spectra to Lunar Surface Composition Using Hyperspectral Imaging and Machine Learning
We present an innovative, cost-effective framework integrating laboratory Hyperspectral Imaging (HSI) of the BECHAR 010 lunar meteorite with ground-based lunar HSI and supervised Machine Learning (ML) to generate high-fidelity mineralogical maps. A \SI{3}{\milli\metre} thin section of BECHAR 010 was imaged under a microscope with a \SI{30}{\milli\metre} focal length lens at \SI{150}{\milli\metre} working distance, using 6x binning to increase the signal-to-noise ratio, producing a data cube (X $\times$ Y $\times$ $\lambda$ = $791 \times 1024 \times 224$, \SI{0.24}{\milli\metre} $\times$ \SI{0.2}{\milli\metre} resolution) across \SIrange{400}{1000}{\nano\metre} (224 bands, \SI{2.7}{\nano\metre} spectral sampling, \SI{5.5}{\nano\metre} FWHM spectral resolution) using a Specim FX10 camera. Ground-based lunar HSI was captured with a Celestron 8SE telescope (\SI{3}{\kilo\metre}/pixel), yielded a data cube ($371 \times 1024 \times 224$). Solar calibration was performed using a Spectralon reference (\SI{99}{\percent} reflectance \SI{< 2}{\percent} error) ensured accurate reflectance spectra. A Support Vector Machine (SVM) with a radial basis function kernel, trained on expert-labeled spectra, achieved \SI{93.7}{\percent} classification accuracy (5-fold cross-validation) for olivine (\SI{92}{\percent} precision, \SI{90}{\percent} recall) and pyroxene (\SI{88}{\percent} precision, \SI{86}{\percent} recall) in BECHAR 010. Local Interpretable Model-agnostic Explanations (LIME) identified key wavelengths (e.g., \SI{485}{\nano\metre}, \SI{22.4}{\percent} for M3; \SI{715}{\nano\metre}, \SI{20.6}{\percent} for M6) across 10 pre selected regions (M1 to M10), indicating olivine-rich (Highland-like) and pyroxene-rich (Mare-like) compositions. Spectral Angle Mapper (SAM) analysis revealed angles from \SI{0.26}{\radian} to \SI{0.66}{\radian}, linking M3 and M9 to Highlands and M6 and M10 to Mares. K-means clustering of lunar data identified 10 mineralogical clusters (\SI{88}{\percent} accuracy), validated against Chandrayaan-1 Moon mineralogy Mapper ($\rm M^3$) data (\SI{140}{\metre}/pixel, \SI{10}{\nano\metre} spectral resolution). A novel push-broom HSI approach with telescope, achieves 0.8 arcsec resolution for lunar spectroscopy, inspiring full-sky multi-object spectral mapping.- 17 March 2026
- Published by MDPI AG in Preprints.org
Open AccessConnecting Meteorite Spectra to Lunar Surface Composition Using Hyperspectral Imaging and Machine Learning
We present an innovative, cost-effective framework integrating laboratory Hyperspectral Imaging (HSI) of the BECHAR 010 lunar meteorite with ground-based lunar HSI and supervised Machine Learning (ML) to generate high-fidelity mineralogical maps. A \SI{3}{\milli\metre} thin section of BECHAR 010 was imaged under a microscope with a \SI{30}{\milli\metre} focal length lens at \SI{150}{\milli\metre} working distance, using 6x binning to increase the signal-to-noise ratio, producing a data cube (X $\times$ Y $\times$ $\lambda$ = $791 \times 1024 \times 224$, \SI{0.24}{\milli\metre} $\times$ \SI{0.2}{\milli\metre} resolution) across \SIrange{400}{1000}{\nano\metre} (224 bands, \SI{2.7}{\nano\metre} spectral sampling, \SI{5.5}{\nano\metre} FWHM spectral resolution) using a Specim FX10 camera. Ground-based lunar HSI was captured with a Celestron 8SE telescope (\SI{3}{\kilo\metre}/pixel), yielded a data cube ($371 \times 1024 \times 224$). Solar calibration was performed using a Spectralon reference (\SI{99}{\percent} reflectance \SI{< 2}{\percent} error) ensured accurate reflectance spectra. A Support Vector Machine (SVM) with a radial basis function kernel, trained on expert-labeled spectra, achieved \SI{93.7}{\percent} classification accuracy (5-fold cross-validation) for olivine (\SI{92}{\percent} precision, \SI{90}{\percent} recall) and pyroxene (\SI{88}{\percent} precision, \SI{86}{\percent} recall) in BECHAR 010. Local Interpretable Model-agnostic Explanations (LIME) identified key wavelengths (e.g., \SI{485}{\nano\metre}, \SI{22.4}{\percent} for M3; \SI{715}{\nano\metre}, \SI{20.6}{\percent} for M6) across 10 pre selected regions (M1 to M10), indicating olivine-rich (Highland-like) and pyroxene-rich (Mare-like) compositions. Spectral Angle Mapper (SAM) analysis revealed angles from \SI{0.26}{\radian} to \SI{0.66}{\radian}, linking M3 and M9 to Highlands and M6 and M10 to Mares. K-means clustering of lunar data identified 10 mineralogical clusters (\SI{88}{\percent} accuracy), validated against Chandrayaan-1 Moon mineralogy Mapper ($\rm M^3$) data (\SI{140}{\metre}/pixel, \SI{10}{\nano\metre} spectral resolution). A novel push-broom HSI approach with telescope, achieves 0.8 arcsec resolution for lunar spectroscopy, inspiring full-sky multi-object spectral mapping. - Top Cited PapersOpen Access
Microbial Bioleaching of Critical Metals from Spent Lithium-Ion Batteries: A Biohydrometallurgical Approach
Biohydrometallurgical processing of spent lithium-ion batteries offers a low-impact route for critical metal recovery compared with conventional hydrometallurgy. In this work, the iron-oxidizing bacterium Acidithiobacillus ferrooxidans was evaluated for the bioleaching of cobalt (Co), nickel (Ni), lithium (Li) and copper (Cu) from pyrolyzed industrial black mass derived primarily from LiCoO2-based batteries, containing both LiCoO2 and LiNiO2 layered oxide phases. Batch experiments were conducted in 9K medium at 30 °C, varying pulp density (1%–2%, w/v), inoculum volume (10–20 mL in 200 mL medium) and initial pH (with and without adjustment). At 1% pulp density and 10% v/v inoculum, metal recoveries after 6–7 days reached about 64%–70% Co, 57%–72% Ni, 52%–60% Li and 81%–100% Cu, with most dissolution occurring in the first 6 days. Higher inoculum loads without initial pH adjustment increased Li recovery up to 79%, but did not further improve Co and Cu, indicating a trade-off between microbial activity, metal toxicity and ferric iron availability. The temporal evolution of pH and metal dissolution is consistent with indirect redoxolysis by biogenic Fe3+ and sulfuric acid generated during ferrous iron and elemental sulfur oxidation. Overall, the results confirm the feasibility of A. ferrooxidans-assisted bioleaching as a green option for Co, Ni, Li and Cu recovery from spent LiCoO2 batteries and provide operating windows for subsequent process optimization and scale-up.
Top Cited PapersOpen AccessMicrobial Bioleaching of Critical Metals from Spent Lithium-Ion Batteries: A Biohydrometallurgical Approach
Biohydrometallurgical processing of spent lithium-ion batteries offers a low-impact route for critical metal recovery compared with conventional hydrometallurgy. In this work, the iron-oxidizing bacterium Acidithiobacillus ferrooxidans was evaluated for the bioleaching of cobalt (Co), nickel (Ni), lithium (Li) and copper (Cu) from pyrolyzed industrial black mass derived primarily from LiCoO2-based batteries, containing both LiCoO2 and LiNiO2 layered oxide phases. Batch experiments were conducted in 9K medium at 30 °C, varying pulp density (1%–2%, w/v), inoculum volume (10–20 mL in 200 mL medium) and initial pH (with and without adjustment). At 1% pulp density and 10% v/v inoculum, metal recoveries after 6–7 days reached about 64%–70% Co, 57%–72% Ni, 52%–60% Li and 81%–100% Cu, with most dissolution occurring in the first 6 days. Higher inoculum loads without initial pH adjustment increased Li recovery up to 79%, but did not further improve Co and Cu, indicating a trade-off between microbial activity, metal toxicity and ferric iron availability. The temporal evolution of pH and metal dissolution is consistent with indirect redoxolysis by biogenic Fe3+ and sulfuric acid generated during ferrous iron and elemental sulfur oxidation. Overall, the results confirm the feasibility of A. ferrooxidans-assisted bioleaching as a green option for Co, Ni, Li and Cu recovery from spent LiCoO2 batteries and provide operating windows for subsequent process optimization and scale-up.
- Open Access
- 27 January 2026
- Published by MDPI AG in Preprints.org
Microbial Bioleaching of Critical Metals from Spent Lithium-Ion Batteries: A Biohydrometallurgical Approach
Biohydrometallurgical processing of spent lithium-ion batteries offers a low-impact route for critical metal recovery compared with conventional hydrometallurgy. In this work, the iron-oxidizing bacterium Acidithiobacillus ferrooxidans was evaluated for the bioleaching of cobalt (Co), nickel (Ni), lithium (Li) and copper (Cu) from the black mass of spent LiCoO₂ batteries. Batch experiments were conducted in 9K medium at 30 °C, varying pulp density (1-2 %, w/v), inoculum volume (10-20 mL in 200 mL medium) and initial pH (with and without adjustment). At 1 % pulp density and 10 % v/v inoculum, metal recoveries after 6-7 days reached about 64-70 % Co, 57-72 % Ni, 52-60 % Li and 81-100 % Cu, with most dissolution occurring in the first 6 days. Higher inoculum loads without initial pH adjustment increased Li recovery up to 79 %, but did not further improve Co and Cu, indicating a trade-off between microbial activity, metal toxicity and ferric iron availability. The temporal evolution of pH and metal dissolution is consistent with indirect redoxolysis by biogenic Fe³⁺ and sulfuric acid generated during ferrous iron and elemental sulfur oxidation. Overall, the results confirm the feasibility of A. ferrooxidans-assisted bioleaching as a green option for Co, Ni, Li and Cu recovery from spent LiCoO₂ batteries and provide operating windows for subsequent process optimization and scale-up.
- 27 January 2026
- Published by MDPI AG in Preprints.org
Open AccessMicrobial Bioleaching of Critical Metals from Spent Lithium-Ion Batteries: A Biohydrometallurgical Approach
Biohydrometallurgical processing of spent lithium-ion batteries offers a low-impact route for critical metal recovery compared with conventional hydrometallurgy. In this work, the iron-oxidizing bacterium Acidithiobacillus ferrooxidans was evaluated for the bioleaching of cobalt (Co), nickel (Ni), lithium (Li) and copper (Cu) from the black mass of spent LiCoO₂ batteries. Batch experiments were conducted in 9K medium at 30 °C, varying pulp density (1-2 %, w/v), inoculum volume (10-20 mL in 200 mL medium) and initial pH (with and without adjustment). At 1 % pulp density and 10 % v/v inoculum, metal recoveries after 6-7 days reached about 64-70 % Co, 57-72 % Ni, 52-60 % Li and 81-100 % Cu, with most dissolution occurring in the first 6 days. Higher inoculum loads without initial pH adjustment increased Li recovery up to 79 %, but did not further improve Co and Cu, indicating a trade-off between microbial activity, metal toxicity and ferric iron availability. The temporal evolution of pH and metal dissolution is consistent with indirect redoxolysis by biogenic Fe³⁺ and sulfuric acid generated during ferrous iron and elemental sulfur oxidation. Overall, the results confirm the feasibility of A. ferrooxidans-assisted bioleaching as a green option for Co, Ni, Li and Cu recovery from spent LiCoO₂ batteries and provide operating windows for subsequent process optimization and scale-up.
- 18 June 2024
- Published by Elsevier in Advances In Space Research
- Vol. 74 (7) , 3407-3436
Sensing technologies for the challenging Lunar environment
- 18 June 2024
- Published by Elsevier in Advances In Space Research
- Vol. 74 (7) , 3407-3436
Sensing technologies for the challenging Lunar environment
- 1 January 2024
- Published by Springer Nature
- p. 199-252
Mars as a Science Base: Towards a Small Permanent Outpost
- 1 January 2024
- Published by Springer Nature
- p. 199-252
Mars as a Science Base: Towards a Small Permanent Outpost
- 1 January 2024
- Published by Springer Nature
- p. 117-197
Perspectives for Crewed Missions to Mars: Exploration from Orbit and/or Short Stay
- 1 January 2024
- Published by Springer Nature
- p. 117-197
Perspectives for Crewed Missions to Mars: Exploration from Orbit and/or Short Stay
- 1 January 2024
- Published by Springer Nature
- p. 253-339
Towards Permanent Settlements on Mars (from 1000 to 100,000 People)
- 1 January 2024
- Published by Springer Nature
- p. 253-339
Towards Permanent Settlements on Mars (from 1000 to 100,000 People)
- 10 July 2023
- Published by SAGE Publications in Astrobiology
- Vol. 23 (7) , 821-823
NoRCEL's Engagement in Africa: The AstroScience Exploration Network (ASEN)
The AstroScience Exploration Network (ASEN) is the latest innovative initiative from the Network of Researchers on the Chemical Emergence of Life (NoRCEL). Materializing on the vibrancy of the African continent, recognizing its people as a key asset, and building on specific strategic advantages, ASEN will funnel the appetite for scientific knowledge through an educational hub that paves the way for the Global South to come to the fore in new global endeavors and will eventually help build a variety of career paths in a diversifying economy.
- 10 July 2023
- Published by SAGE Publications in Astrobiology
- Vol. 23 (7) , 821-823
NoRCEL's Engagement in Africa: The AstroScience Exploration Network (ASEN)
The AstroScience Exploration Network (ASEN) is the latest innovative initiative from the Network of Researchers on the Chemical Emergence of Life (NoRCEL). Materializing on the vibrancy of the African continent, recognizing its people as a key asset, and building on specific strategic advantages, ASEN will funnel the appetite for scientific knowledge through an educational hub that paves the way for the Global South to come to the fore in new global endeavors and will eventually help build a variety of career paths in a diversifying economy.