Comitê Gestor
Ilum-CNPEM
James M. de Almeida
Publicações
2026
Oliveira, Maria C. S.; Silva, Caroline E. P.; Ferreira, Elisa S.; Almeida, James M.; Bernardes, Juliana S.
Interfacial stabilization mechanism in one-step W/O/W multiple emulsions: The role of cellulose nanofibrils and oleic acid Journal Article
Em: Food Hydrocolloids, vol. 178, 2026, ISSN: 0268-005X.
@article{Oliveira2026b,
title = {Interfacial stabilization mechanism in one-step W/O/W multiple emulsions: The role of cellulose nanofibrils and oleic acid},
author = {Maria C. S. Oliveira and Caroline E. P. Silva and Elisa S. Ferreira and James M. Almeida and Juliana S. Bernardes},
doi = {10.1016/j.foodhyd.2026.112665},
issn = {0268-005X},
year = {2026},
date = {2026-09-01},
journal = {Food Hydrocolloids},
volume = {178},
publisher = {Elsevier BV},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Oliveira, Maria C. S.; Silva, Caroline E. P.; Ferreira, Elisa S.; Almeida, James M.; Bernardes, Juliana S.
Interfacial stabilization mechanism in one-step W/O/W multiple emulsions: The role of cellulose nanofibrils and oleic acid Journal Article
Em: Food Hydrocolloids, vol. 178, 2026, ISSN: 0268-005X.
@article{Oliveira2026c,
title = {Interfacial stabilization mechanism in one-step W/O/W multiple emulsions: The role of cellulose nanofibrils and oleic acid},
author = {Maria C. S. Oliveira and Caroline E. P. Silva and Elisa S. Ferreira and James M. Almeida and Juliana S. Bernardes},
doi = {10.1016/j.foodhyd.2026.112665},
issn = {0268-005X},
year = {2026},
date = {2026-09-01},
journal = {Food Hydrocolloids},
volume = {178},
publisher = {Elsevier BV},
abstract = {The multiphase structure and large interfacial area of multiple emulsions have driven increasing interest in their use in the pharmaceutical, cosmetics, and food industries. However, the presence of two thermodynamically unstable interfaces remains a challenge that hinders their broader application. Typically, the production of multiple emulsions requires large amounts of synthetic emulsifiers and a two-step preparation process. Here, we developed novel almond oil W1/O/W2 multiple emulsions in one step by combining cellulose nanofibrils (CNFs) and oleic acid as bio-based emulsifiers. Cationic or anionic cellulose nanofibrils (CNFs), with average widths of about 2 nm, acted as Pickering stabilizers at the O/W2 interface, producing oil droplets with diameters in the tens of micrometers. The internal W1/O emulsion, composed of water droplets a few micrometers in size, was stabilized by the naturally occurring oleic acid in the oil phase. Molecular dynamics simulations showed that increasing oleic acid content reduces the interfacial tension from 28 to 19 mN m−1. Emulsions prepared with mineral oil as an alternative oil phase did not result in multiple emulsion formation, confirming the essential role of oleic acid in stabilizing the W1/O interface. Additionally, the stability of multiple emulsions can be tailored by the type of CNF, as electrostatic repulsion between the negatively charged CNFs and oleic acid enhances the migration of oleic acid to the W1/O interface, promoting stability for over 60 days. These results demonstrate the capability of functionalized CNFs and oleic acid in the design of multiple emulsions using a practical method, with possible use in food and cosmetic formulations.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The multiphase structure and large interfacial area of multiple emulsions have driven increasing interest in their use in the pharmaceutical, cosmetics, and food industries. However, the presence of two thermodynamically unstable interfaces remains a challenge that hinders their broader application. Typically, the production of multiple emulsions requires large amounts of synthetic emulsifiers and a two-step preparation process. Here, we developed novel almond oil W1/O/W2 multiple emulsions in one step by combining cellulose nanofibrils (CNFs) and oleic acid as bio-based emulsifiers. Cationic or anionic cellulose nanofibrils (CNFs), with average widths of about 2 nm, acted as Pickering stabilizers at the O/W2 interface, producing oil droplets with diameters in the tens of micrometers. The internal W1/O emulsion, composed of water droplets a few micrometers in size, was stabilized by the naturally occurring oleic acid in the oil phase. Molecular dynamics simulations showed that increasing oleic acid content reduces the interfacial tension from 28 to 19 mN m−1. Emulsions prepared with mineral oil as an alternative oil phase did not result in multiple emulsion formation, confirming the essential role of oleic acid in stabilizing the W1/O interface. Additionally, the stability of multiple emulsions can be tailored by the type of CNF, as electrostatic repulsion between the negatively charged CNFs and oleic acid enhances the migration of oleic acid to the W1/O interface, promoting stability for over 60 days. These results demonstrate the capability of functionalized CNFs and oleic acid in the design of multiple emulsions using a practical method, with possible use in food and cosmetic formulations.
Santos, Arthur Silva Sousa; Almeida, James Moraes; Trindade, Fabiane Jesus; Felez, Marissol Rodrigues; Marques, Larissa Silva; Florio, Daniel Zanetti
Machine learning assisted prediction and synthesis of new high-entropy fluorite oxide Journal Article
Em: Open Ceramics, vol. 26, 2026, ISSN: 2666-5395.
@article{daSilvaSousaSantos2026b,
title = {Machine learning assisted prediction and synthesis of new high-entropy fluorite oxide},
author = {Arthur Silva Sousa Santos and James Moraes Almeida and Fabiane Jesus Trindade and Marissol Rodrigues Felez and Larissa Silva Marques and Daniel Zanetti Florio},
doi = {10.1016/j.oceram.2026.100982},
issn = {2666-5395},
year = {2026},
date = {2026-06-01},
journal = {Open Ceramics},
volume = {26},
publisher = {Elsevier BV},
abstract = {High-entropy oxides are a novel class of materials with promising applications in energy conversion and storage; however, their rational design remains challenging due to the immense compositional space. Here, we propose a machine-learning-based methodology to design stable, single-phase HEOs. We trained predictive models to identify candidate fluorite-structured compositions. The ensemble achieved reasonable performance in a six-class classification task, as evaluated using nested stratified cross-validation and external validation (weighted-average F1-scores of 77% and 70%, respectively). We further applied SHAP analysis to assess the physical relevance of the predictors. Experimentally, we synthesized Ce0.2La0.2Nd0.2Mg0.2Al0.2O
. X-ray diffraction confirmed this prediction, and transmission electron microscopy (TEM) combined with selected-area electron diffraction (SAED) validated the phase assignment. Overall, these results demonstrate that machine learning is a powerful approach to navigate the complex HEO compositional landscape and accelerate the discovery of materials with targeted properties.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
High-entropy oxides are a novel class of materials with promising applications in energy conversion and storage; however, their rational design remains challenging due to the immense compositional space. Here, we propose a machine-learning-based methodology to design stable, single-phase HEOs. We trained predictive models to identify candidate fluorite-structured compositions. The ensemble achieved reasonable performance in a six-class classification task, as evaluated using nested stratified cross-validation and external validation (weighted-average F1-scores of 77% and 70%, respectively). We further applied SHAP analysis to assess the physical relevance of the predictors. Experimentally, we synthesized Ce0.2La0.2Nd0.2Mg0.2Al0.2O
. X-ray diffraction confirmed this prediction, and transmission electron microscopy (TEM) combined with selected-area electron diffraction (SAED) validated the phase assignment. Overall, these results demonstrate that machine learning is a powerful approach to navigate the complex HEO compositional landscape and accelerate the discovery of materials with targeted properties.
. X-ray diffraction confirmed this prediction, and transmission electron microscopy (TEM) combined with selected-area electron diffraction (SAED) validated the phase assignment. Overall, these results demonstrate that machine learning is a powerful approach to navigate the complex HEO compositional landscape and accelerate the discovery of materials with targeted properties.
Santos, Arthur Silva Sousa; Almeida, James Moraes; Trindade, Fabiane Jesus; Felez, Marissol Rodrigues; Marques, Larissa Silva; Florio, Daniel Zanetti
Machine learning assisted prediction and synthesis of new high-entropy fluorite oxide Journal Article
Em: Open Ceramics, vol. 26, 2026, ISSN: 2666-5395.
@article{daSilvaSousaSantos2026c,
title = {Machine learning assisted prediction and synthesis of new high-entropy fluorite oxide},
author = {Arthur Silva Sousa Santos and James Moraes Almeida and Fabiane Jesus Trindade and Marissol Rodrigues Felez and Larissa Silva Marques and Daniel Zanetti Florio},
doi = {10.1016/j.oceram.2026.100982},
issn = {2666-5395},
year = {2026},
date = {2026-06-01},
journal = {Open Ceramics},
volume = {26},
publisher = {Elsevier BV},
abstract = {High-entropy oxides are a novel class of materials with promising applications in energy conversion and storage; however, their rational design remains challenging due to the immense compositional space. Here, we propose a machine-learning-based methodology to design stable, single-phase HEOs. We trained predictive models to identify candidate fluorite-structured compositions. The ensemble achieved reasonable performance in a six-class classification task, as evaluated using nested stratified cross-validation and external validation (weighted-average F1-scores of 77% and 70%, respectively). We further applied SHAP analysis to assess the physical relevance of the predictors. Experimentally, we synthesized Ce0.2La0.2Nd0.2Mg0.2Al0.2O
. X-ray diffraction confirmed this prediction, and transmission electron microscopy (TEM) combined with selected-area electron diffraction (SAED) validated the phase assignment. Overall, these results demonstrate that machine learning is a powerful approach to navigate the complex HEO compositional landscape and accelerate the discovery of materials with targeted properties.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
High-entropy oxides are a novel class of materials with promising applications in energy conversion and storage; however, their rational design remains challenging due to the immense compositional space. Here, we propose a machine-learning-based methodology to design stable, single-phase HEOs. We trained predictive models to identify candidate fluorite-structured compositions. The ensemble achieved reasonable performance in a six-class classification task, as evaluated using nested stratified cross-validation and external validation (weighted-average F1-scores of 77% and 70%, respectively). We further applied SHAP analysis to assess the physical relevance of the predictors. Experimentally, we synthesized Ce0.2La0.2Nd0.2Mg0.2Al0.2O
. X-ray diffraction confirmed this prediction, and transmission electron microscopy (TEM) combined with selected-area electron diffraction (SAED) validated the phase assignment. Overall, these results demonstrate that machine learning is a powerful approach to navigate the complex HEO compositional landscape and accelerate the discovery of materials with targeted properties.
. X-ray diffraction confirmed this prediction, and transmission electron microscopy (TEM) combined with selected-area electron diffraction (SAED) validated the phase assignment. Overall, these results demonstrate that machine learning is a powerful approach to navigate the complex HEO compositional landscape and accelerate the discovery of materials with targeted properties.
Santos, Arthur Silva Sousa; Stojanovska, Elena; Alves, Antonio Augusto; Paula, Amauri Jardim; Florio, Daniel Zanetti; Almeida, James Moraes
Rational Design of Single-Phase High-Entropy Oxides via Large Language Model Data Mining and Explainable Machine Learning Journal Article
Em: J. Chem. Inf. Model., 2026, ISSN: 1549-960X.
@article{daSilvaSousaSantos2026,
title = {Rational Design of Single-Phase High-Entropy Oxides via Large Language Model Data Mining and Explainable Machine Learning},
author = {Arthur Silva Sousa Santos and Elena Stojanovska and Antonio Augusto Alves and Amauri Jardim Paula and Daniel Zanetti Florio and James Moraes Almeida},
doi = {10.1021/acs.jcim.6c00752},
issn = {1549-960X},
year = {2026},
date = {2026-04-25},
urldate = {2026-04-25},
journal = {J. Chem. Inf. Model.},
publisher = {American Chemical Society (ACS)},
abstract = {The rational design of high-entropy oxides (HEOs) is currently hindered by the scarcity of structured property data in the scientific literature. In this work, we present an end-to-end materials informatics framework that couples large language model (LLM) data mining with interpretable machine learning to predict single-phase stability in HEOs. We deployed agents based on gpt-oss-120b to extract compositions, phases, and synthesis methods from unstructured scientific abstracts. Combined with regular-expression routines, the LLM-based agent achieved an accuracy of 96% in database generation despite the complexity of the task, including on-the-fly inference of relative cation proportions. Subsequently, a range of machine-learning models was trained in an exploratory multiclass classification setting to distinguish canonical HEO crystal structures using several variants of the primary databases obtained by combining different feature subsets. For this task, an XGBoost classifier achieved an F1-score of 86% in a seven-class classification problem, and the best-performing database variant combined primary and statistical features. This optimal database representation was then used to train a neural-network binary classifier to distinguish perovskite from nonperovskite compositions, achieving 97.9% classification accuracy on the test set, whereas the Goldschmidt tolerance factor reached only 67.3% on the same data. These results indicate that the proposed methodology can support the design of HEO compositions with target properties and substantially outperforms traditional descriptor-based approaches. Furthermore, SHAP (SHapley Additive exPlanations) analysis revealed that high-entropy perovskite phase stability is governed by a critical interplay between geometric factors, such as the sum of cation radii, and electronic descriptors, including Sanderson electronegativity and atomization enthalpy. Overall, these findings demonstrate that LLM-driven data mining can overcome data bottlenecks and enable the discovery of physical design rules for complex, multicomponent ceramics.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The rational design of high-entropy oxides (HEOs) is currently hindered by the scarcity of structured property data in the scientific literature. In this work, we present an end-to-end materials informatics framework that couples large language model (LLM) data mining with interpretable machine learning to predict single-phase stability in HEOs. We deployed agents based on gpt-oss-120b to extract compositions, phases, and synthesis methods from unstructured scientific abstracts. Combined with regular-expression routines, the LLM-based agent achieved an accuracy of 96% in database generation despite the complexity of the task, including on-the-fly inference of relative cation proportions. Subsequently, a range of machine-learning models was trained in an exploratory multiclass classification setting to distinguish canonical HEO crystal structures using several variants of the primary databases obtained by combining different feature subsets. For this task, an XGBoost classifier achieved an F1-score of 86% in a seven-class classification problem, and the best-performing database variant combined primary and statistical features. This optimal database representation was then used to train a neural-network binary classifier to distinguish perovskite from nonperovskite compositions, achieving 97.9% classification accuracy on the test set, whereas the Goldschmidt tolerance factor reached only 67.3% on the same data. These results indicate that the proposed methodology can support the design of HEO compositions with target properties and substantially outperforms traditional descriptor-based approaches. Furthermore, SHAP (SHapley Additive exPlanations) analysis revealed that high-entropy perovskite phase stability is governed by a critical interplay between geometric factors, such as the sum of cation radii, and electronic descriptors, including Sanderson electronegativity and atomization enthalpy. Overall, these findings demonstrate that LLM-driven data mining can overcome data bottlenecks and enable the discovery of physical design rules for complex, multicomponent ceramics.
Hawthorne, Felipe; Seixas, Leandro; Almeida, James M.; Woellner, Cristiano F.; Tromer, Raphael M.
Interpretable Machine Learning of Nanoparticle Stability through Topological Layer Embeddings Miscellaneous
2026.
@misc{hawthorne2026interpretablemachinelearningnanoparticle,
title = {Interpretable Machine Learning of Nanoparticle Stability through Topological Layer Embeddings},
author = {Felipe Hawthorne and Leandro Seixas and James M. Almeida and Cristiano F. Woellner and Raphael M. Tromer},
url = {https://arxiv.org/abs/2602.17528},
year = {2026},
date = {2026-02-19},
urldate = {2026-02-19},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
2025
Paula, Amauri J.; Petry, Romana; Almeida, James M.; Caetano, André A.; Sales, José; Ferreira, Odair P.; Martinez, Diego S. T.; Kobs, Henry J.; Faria, Andreia F.
Large-Scale Meta-Analysis of Nanomaterials Toxicity Based on Natural Language Processing of Scientific Articles Journal Article
Em: ACS Appl. Nano Mater., 2025, ISSN: 2574-0970.
@article{Paula2025,
title = {Large-Scale Meta-Analysis of Nanomaterials Toxicity Based on Natural Language Processing of Scientific Articles},
author = {Amauri J. Paula and Romana Petry and James M. Almeida and André A. Caetano and José Sales and Odair P. Ferreira and Diego S. T. Martinez and Henry J. Kobs and Andreia F. Faria},
doi = {10.1021/acsanm.5c05119},
issn = {2574-0970},
year = {2025},
date = {2025-12-23},
urldate = {2025-12-23},
journal = {ACS Appl. Nano Mater.},
publisher = {American Chemical Society (ACS)},
abstract = {Natural language processing (NLP) pipelines can mine the nanotoxicology literature at a scale and resolution that cannot be achieved by manual curation. Here, we established a NLP pipeline that coupled topic modeling and end point-specific extraction LLM prompts to convert ∼106 sentences extracted from abstracts of scientific articles into a structured knowledge base containing 13 nanotoxicology features. The pipeline is capable of analyzing and extracting nanomaterial descriptors such as size, ζ potential, and surface area, along with biological end points such as minimum inhibitory concentration (MIC), minimum bactericidal concentration (MBC) and lethal concentration 50% (LC50). Statistical convergence across multiple quantitative end points - MIC, MBC, microbial log reduction, and biofilm killing efficiency - shows that Ag-based nanomaterials are the most potent antimicrobial agents, showing lower MIC and MBC than ZnO, TiO2 and Au analogs. This trend was also observed for individual pathogens such as Escherichia coli and Staphylococcus aureus. Most nanomaterials are within 1 to 100 nm, with nanoparticles featured in >80% of the studies. Although nanomaterials <50 nm often produce the lowest MIC and LC50, toxicity within a single size class spans orders of magnitude, underscoring the influence of surface chemistry, coatings, and colloidal behavior. In addition, adverse reproductive effects in Caenorhabditis elegans and Daphnia magna, and developmental abnormalities in Danio rerio, are predominantly related to Ag and TiO2. In general, our automated data extraction and the curation strategy transforms disparate literature into a machine-readable knowledge base that paves the way for data-driven predictions of nanomaterial hazards.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Natural language processing (NLP) pipelines can mine the nanotoxicology literature at a scale and resolution that cannot be achieved by manual curation. Here, we established a NLP pipeline that coupled topic modeling and end point-specific extraction LLM prompts to convert ∼106 sentences extracted from abstracts of scientific articles into a structured knowledge base containing 13 nanotoxicology features. The pipeline is capable of analyzing and extracting nanomaterial descriptors such as size, ζ potential, and surface area, along with biological end points such as minimum inhibitory concentration (MIC), minimum bactericidal concentration (MBC) and lethal concentration 50% (LC50). Statistical convergence across multiple quantitative end points - MIC, MBC, microbial log reduction, and biofilm killing efficiency - shows that Ag-based nanomaterials are the most potent antimicrobial agents, showing lower MIC and MBC than ZnO, TiO2 and Au analogs. This trend was also observed for individual pathogens such as Escherichia coli and Staphylococcus aureus. Most nanomaterials are within 1 to 100 nm, with nanoparticles featured in >80% of the studies. Although nanomaterials <50 nm often produce the lowest MIC and LC50, toxicity within a single size class spans orders of magnitude, underscoring the influence of surface chemistry, coatings, and colloidal behavior. In addition, adverse reproductive effects in Caenorhabditis elegans and Daphnia magna, and developmental abnormalities in Danio rerio, are predominantly related to Ag and TiO2. In general, our automated data extraction and the curation strategy transforms disparate literature into a machine-readable knowledge base that paves the way for data-driven predictions of nanomaterial hazards.
Paula, Amauri J.; Petry, Romana; Almeida, James M.; Caetano, André A.; Sales, José; Ferreira, Odair P.; Martinez, Diego S. T.; Kobs, Henry J.; Faria, Andreia F.
Large-Scale Meta-Analysis of Nanomaterials Toxicity Based on Natural Language Processing of Scientific Articles Journal Article
Em: ACS Appl. Nano Mater., 2025, ISSN: 2574-0970.
@article{Paula2025b,
title = {Large-Scale Meta-Analysis of Nanomaterials Toxicity Based on Natural Language Processing of Scientific Articles},
author = {Amauri J. Paula and Romana Petry and James M. Almeida and André A. Caetano and José Sales and Odair P. Ferreira and Diego S. T. Martinez and Henry J. Kobs and Andreia F. Faria},
doi = {10.1021/acsanm.5c05119},
issn = {2574-0970},
year = {2025},
date = {2025-12-23},
urldate = {2025-12-23},
journal = {ACS Appl. Nano Mater.},
publisher = {American Chemical Society (ACS)},
abstract = {Natural language processing (NLP) pipelines can mine the nanotoxicology literature at a scale and resolution that cannot be achieved by manual curation. Here, we established a NLP pipeline that coupled topic modeling and end point-specific extraction LLM prompts to convert ∼106 sentences extracted from abstracts of scientific articles into a structured knowledge base containing 13 nanotoxicology features. The pipeline is capable of analyzing and extracting nanomaterial descriptors such as size, ζ potential, and surface area, along with biological end points such as minimum inhibitory concentration (MIC), minimum bactericidal concentration (MBC) and lethal concentration 50% (LC50). Statistical convergence across multiple quantitative end points - MIC, MBC, microbial log reduction, and biofilm killing efficiency - shows that Ag-based nanomaterials are the most potent antimicrobial agents, showing lower MIC and MBC than ZnO, TiO2 and Au analogs. This trend was also observed for individual pathogens such as Escherichia coli and Staphylococcus aureus. Most nanomaterials are within 1 to 100 nm, with nanoparticles featured in >80% of the studies. Although nanomaterials <50 nm often produce the lowest MIC and LC50, toxicity within a single size class spans orders of magnitude, underscoring the influence of surface chemistry, coatings, and colloidal behavior. In addition, adverse reproductive effects in Caenorhabditis elegans and Daphnia magna, and developmental abnormalities in Danio rerio, are predominantly related to Ag and TiO2. In general, our automated data extraction and the curation strategy transforms disparate literature into a machine-readable knowledge base that paves the way for data-driven predictions of nanomaterial hazards.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Natural language processing (NLP) pipelines can mine the nanotoxicology literature at a scale and resolution that cannot be achieved by manual curation. Here, we established a NLP pipeline that coupled topic modeling and end point-specific extraction LLM prompts to convert ∼106 sentences extracted from abstracts of scientific articles into a structured knowledge base containing 13 nanotoxicology features. The pipeline is capable of analyzing and extracting nanomaterial descriptors such as size, ζ potential, and surface area, along with biological end points such as minimum inhibitory concentration (MIC), minimum bactericidal concentration (MBC) and lethal concentration 50% (LC50). Statistical convergence across multiple quantitative end points - MIC, MBC, microbial log reduction, and biofilm killing efficiency - shows that Ag-based nanomaterials are the most potent antimicrobial agents, showing lower MIC and MBC than ZnO, TiO2 and Au analogs. This trend was also observed for individual pathogens such as Escherichia coli and Staphylococcus aureus. Most nanomaterials are within 1 to 100 nm, with nanoparticles featured in >80% of the studies. Although nanomaterials <50 nm often produce the lowest MIC and LC50, toxicity within a single size class spans orders of magnitude, underscoring the influence of surface chemistry, coatings, and colloidal behavior. In addition, adverse reproductive effects in Caenorhabditis elegans and Daphnia magna, and developmental abnormalities in Danio rerio, are predominantly related to Ag and TiO2. In general, our automated data extraction and the curation strategy transforms disparate literature into a machine-readable knowledge base that paves the way for data-driven predictions of nanomaterial hazards.
Ipaves, Bruno; Justo, João F.; Almeida, James M.; Assali, Lucy V. C.; Autreto, Pedro Alves Silva
Metal-Free Doping Strategies in Two-Dimensional Carbon Nitride C_4N_2 for Enhanced Hydrogen Evolution Catalysis Journal Article
Em: ACS Omega, 2025, ISSN: 2470-1343.
@article{Ipaves2025,
title = {Metal-Free Doping Strategies in Two-Dimensional Carbon Nitride C_4N_2 for Enhanced Hydrogen Evolution Catalysis},
author = {Bruno Ipaves and João F. Justo and James M. Almeida and Lucy V. C. Assali and Pedro Alves Silva Autreto},
doi = {10.1021/acsomega.5c05773},
issn = {2470-1343},
year = {2025},
date = {2025-09-16},
urldate = {2025-09-16},
journal = {ACS Omega},
publisher = {American Chemical Society (ACS)},
abstract = {This study investigates the structural, electronic, and catalytic properties of pristine and doped C4N2 nanosheets as potential catalysts for the hydrogen evolution reaction (HER). The pristine C36N18 nanosheets exhibit limited HER activity, primarily due to high positive Gibbs free energies (>2.2 eV). We explored doping it with B, Si, or P atoms at the nitrogen site to enhance catalytic performance. Among these systems, B-doped C36N17 nanosheets exhibit the most promising catalytic activity, with a Gibbs free energy close to zero (≈−0.2 eV), indicating efficient hydrogen adsorption. Band structure, projected density of states (PDOS), charge density, and Bader charge analyses reveal significant changes in the electronic environment due to doping. Although stacking configurations (AA′A″ and ABC) have a minimal effect on catalytic performance, doping, particularly with B, substantially alters the electronic structure, thus optimizing hydrogen adsorption and facilitating an efficient hydrogen evolution reaction.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This study investigates the structural, electronic, and catalytic properties of pristine and doped C4N2 nanosheets as potential catalysts for the hydrogen evolution reaction (HER). The pristine C36N18 nanosheets exhibit limited HER activity, primarily due to high positive Gibbs free energies (>2.2 eV). We explored doping it with B, Si, or P atoms at the nitrogen site to enhance catalytic performance. Among these systems, B-doped C36N17 nanosheets exhibit the most promising catalytic activity, with a Gibbs free energy close to zero (≈−0.2 eV), indicating efficient hydrogen adsorption. Band structure, projected density of states (PDOS), charge density, and Bader charge analyses reveal significant changes in the electronic environment due to doping. Although stacking configurations (AA′A″ and ABC) have a minimal effect on catalytic performance, doping, particularly with B, substantially alters the electronic structure, thus optimizing hydrogen adsorption and facilitating an efficient hydrogen evolution reaction.
Almeida, James Moraes; Zornio, Bruno Fedosse; Baptista, Alvaro David Torrez; Miranda, Caetano Rodrigues
Charge Effects on the Adsorption of Octanoic Acid and Octanoate at Carbonates Journal Article
Em: ACS Omega, 2025, ISSN: 2470-1343.
@article{deAlmeida2025,
title = {Charge Effects on the Adsorption of Octanoic Acid and Octanoate at Carbonates},
author = {James Moraes Almeida and Bruno Fedosse Zornio and Alvaro David Torrez Baptista and Caetano Rodrigues Miranda},
doi = {10.1021/acsomega.5c06363},
issn = {2470-1343},
year = {2025},
date = {2025-07-24},
urldate = {2025-07-24},
journal = {ACS Omega},
publisher = {American Chemical Society (ACS)},
abstract = {In this work, we investigate the adsorption behavior of protonated and deprotonated acids on carbonate surfaces, employing density functional theory (DFT) simulations and the self-consistent potential correction (SCPC) for the charged deprotonated acid. By comparing the coadsorption models with the SCPC method, we have observed significant differences in the adsorption energies, indicating that coadsorption underestimates the stability of the acid–carbonate interactions, even leading to changes from favorable to unfavorable adsorption on magnesites. Our study highlights the distinct chemical interactions of protonated and deprotonated acids with carbonate surfaces, revealing a more covalent bonding nature for protonated acids and a predominantly ionic character for deprotonated acids. Hence, we highlight the importance of employing charge correction methods, such as the SCPC, for a more accurate representation of the adsorption of charged molecules on mineral surfaces, which could be extended to other systems.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In this work, we investigate the adsorption behavior of protonated and deprotonated acids on carbonate surfaces, employing density functional theory (DFT) simulations and the self-consistent potential correction (SCPC) for the charged deprotonated acid. By comparing the coadsorption models with the SCPC method, we have observed significant differences in the adsorption energies, indicating that coadsorption underestimates the stability of the acid–carbonate interactions, even leading to changes from favorable to unfavorable adsorption on magnesites. Our study highlights the distinct chemical interactions of protonated and deprotonated acids with carbonate surfaces, revealing a more covalent bonding nature for protonated acids and a predominantly ionic character for deprotonated acids. Hence, we highlight the importance of employing charge correction methods, such as the SCPC, for a more accurate representation of the adsorption of charged molecules on mineral surfaces, which could be extended to other systems.
Silva, Monyque Karoline Paula; Nicoleti, Vitória Yumi Uetuki; Rodrigues, Barbara Paixão Perez; Araujo, Ademir Sergio Ferreira; Ellwanger, Joel Henrique; Almeida, James M.; Lemos, Leandro Nascimento
Exploring deep learning in phage discovery and characterization Journal Article
Em: Virology, 2025, ISSN: 0042-6822.
@article{dePaulaSilva2025,
title = {Exploring deep learning in phage discovery and characterization},
author = {Monyque Karoline Paula Silva and Vitória Yumi Uetuki Nicoleti and Barbara Paixão Perez Rodrigues and Ademir Sergio Ferreira Araujo and Joel Henrique Ellwanger and James M. Almeida and Leandro Nascimento Lemos},
doi = {10.1016/j.virol.2025.110559},
issn = {0042-6822},
year = {2025},
date = {2025-04-29},
journal = {Virology},
publisher = {Elsevier BV},
abstract = {Bacteriophages, or bacterial viruses, play diverse ecological roles by shaping bacterial populations and also hold significant biotechnological and medical potential, including the treatment of infections caused by multidrug-resistant bacteria. The discovery of novel bacteriophages using large-scale metagenomic data has been accelerated by the accessibility of deep learning (Artificial Intelligence), the increased computing power of graphical processing units (GPUs), and new bioinformatics tools. This review addresses the recent revolution in bacteriophage research, ranging from the adoption of neural network algorithms applied to metagenomic data to the use of pre-trained language models, such as BERT, which have improved the reconstruction of viral metagenome-assembled genomes (vMAGs). This article also discusses the main aspects of bacteriophage biology using deep learning, highlighting the advances and limitations of this approach. Finally, prospects of deep-learning-based metagenomic algorithms and recommendations for future investigations are described.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Bacteriophages, or bacterial viruses, play diverse ecological roles by shaping bacterial populations and also hold significant biotechnological and medical potential, including the treatment of infections caused by multidrug-resistant bacteria. The discovery of novel bacteriophages using large-scale metagenomic data has been accelerated by the accessibility of deep learning (Artificial Intelligence), the increased computing power of graphical processing units (GPUs), and new bioinformatics tools. This review addresses the recent revolution in bacteriophage research, ranging from the adoption of neural network algorithms applied to metagenomic data to the use of pre-trained language models, such as BERT, which have improved the reconstruction of viral metagenome-assembled genomes (vMAGs). This article also discusses the main aspects of bacteriophage biology using deep learning, highlighting the advances and limitations of this approach. Finally, prospects of deep-learning-based metagenomic algorithms and recommendations for future investigations are described.
Ipaves, Bruno; Justo, João F.; Almeida, James M.; Assali, Lucy V. C.; Autreto, Pedro A. S.
Enhancing catalyst activity of two-dimensional C4N2 through doping for the hydrogen evolution reaction Working paper
2025.
@workingpaper{ipaves2025enhancingcatalystactivitytwodimensional,
title = {Enhancing catalyst activity of two-dimensional C4N2 through doping for the hydrogen evolution reaction},
author = {Bruno Ipaves and João F. Justo and James M. Almeida and Lucy V. C. Assali and Pedro A. S. Autreto},
url = {https://arxiv.org/abs/2502.10863},
year = {2025},
date = {2025-02-15},
urldate = {2025-01-01},
abstract = {This study investigates the structural, electronic, and catalytic properties of pristine and doped C4N2 nanosheets as potential electrocatalysts for the hydrogen evolution reaction. The pristine C36N18 nanosheets exhibit limited HER activity, primarily due to high positive Gibbs free energies (> 2.2 eV). To enhance catalytic performance, doping with B, Si, or P at the nitrogen site was explored. Among these systems, B-doped C36N17 nanosheets exhibit the most promising catalytic activity, with a Gibbs free energy close to zero (≈ -0.2 eV), indicating efficient hydrogen adsorption. Band structure, projected density of states, charge density, and Bader charge analyses reveal significant changes in the electronic environment due to doping. While stacking configurations (AA′A′′ and ABC) have minimal effect on catalytic performance, doping - particularly with B -substantially alters the electronic structure, optimizing hydrogen adsorption and facilitating efficient HER. These findings suggest that B-doped C36N17 nanosheets could serve as efficient cocatalysts when combined with metallic materials, offering a promising approach to enhance catalytic efficiency in electrocatalytic and photocatalytic applications.},
keywords = {},
pubstate = {published},
tppubtype = {workingpaper}
}
This study investigates the structural, electronic, and catalytic properties of pristine and doped C4N2 nanosheets as potential electrocatalysts for the hydrogen evolution reaction. The pristine C36N18 nanosheets exhibit limited HER activity, primarily due to high positive Gibbs free energies (> 2.2 eV). To enhance catalytic performance, doping with B, Si, or P at the nitrogen site was explored. Among these systems, B-doped C36N17 nanosheets exhibit the most promising catalytic activity, with a Gibbs free energy close to zero (≈ -0.2 eV), indicating efficient hydrogen adsorption. Band structure, projected density of states, charge density, and Bader charge analyses reveal significant changes in the electronic environment due to doping. While stacking configurations (AA′A′′ and ABC) have minimal effect on catalytic performance, doping - particularly with B -substantially alters the electronic structure, optimizing hydrogen adsorption and facilitating efficient HER. These findings suggest that B-doped C36N17 nanosheets could serve as efficient cocatalysts when combined with metallic materials, offering a promising approach to enhance catalytic efficiency in electrocatalytic and photocatalytic applications.
2024
Petry, Romana; Almeida, James M.; Côa, Francine; Lima, F. Crasto; Martinez, Diego Stéfani T; Fazzio, Adalberto
Interaction of graphene oxide with tannic acid: computational modeling and toxicity mitigation in C. elegans Journal Article
Em: Beilstein J. Nanotechnol., vol. 15, pp. 1297–1311, 2024, ISSN: 2190-4286.
@article{Petry2024,
title = {Interaction of graphene oxide with tannic acid: computational modeling and toxicity mitigation in C. elegans},
author = {Romana Petry and James M. Almeida and Francine Côa and F. Crasto Lima and Diego Stéfani T Martinez and Adalberto Fazzio},
doi = {10.3762/bjnano.15.105},
issn = {2190-4286},
year = {2024},
date = {2024-10-30},
urldate = {2024-10-30},
journal = {Beilstein J. Nanotechnol.},
volume = {15},
pages = {1297–1311},
publisher = {Beilstein Institut},
abstract = {Graphene oxide (GO) undergoes multiple transformations when introduced to biological and environmental media. GO surface favors the adsorption of biomolecules through different types of interaction mechanisms, modulating the biological effects of the material. In this study, we investigated the interaction of GO with tannic acid (TA) and its consequences for GO toxicity. We focused on understanding how TA interacts with GO, its impact on the material surface chemistry, colloidal stability, as well as, toxicity and biodistribution using the Caenorhabditis elegans model. Employing computational modeling, including reactive classical molecular dynamics and ab initio calculations, we reveal that TA preferentially binds to the most reactive sites on GO surfaces via the oxygen-containing groups or the carbon matrix; van der Waals interaction forces dominate the binding energy. TA exhibits a dose-dependent mitigating effect on the toxicity of GO, which can be attributed not only to the surface interactions between the molecule and the material but also to the inherent biological properties of TA in C. elegans. Our findings contribute to a deeper understanding of GO’s environmental behavior and toxicity and highlight the potential of tannic acid for the synthesis and surface functionalization of graphene-based nanomaterials, offering insights into safer nanotechnology development.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Graphene oxide (GO) undergoes multiple transformations when introduced to biological and environmental media. GO surface favors the adsorption of biomolecules through different types of interaction mechanisms, modulating the biological effects of the material. In this study, we investigated the interaction of GO with tannic acid (TA) and its consequences for GO toxicity. We focused on understanding how TA interacts with GO, its impact on the material surface chemistry, colloidal stability, as well as, toxicity and biodistribution using the Caenorhabditis elegans model. Employing computational modeling, including reactive classical molecular dynamics and ab initio calculations, we reveal that TA preferentially binds to the most reactive sites on GO surfaces via the oxygen-containing groups or the carbon matrix; van der Waals interaction forces dominate the binding energy. TA exhibits a dose-dependent mitigating effect on the toxicity of GO, which can be attributed not only to the surface interactions between the molecule and the material but also to the inherent biological properties of TA in C. elegans. Our findings contribute to a deeper understanding of GO’s environmental behavior and toxicity and highlight the potential of tannic acid for the synthesis and surface functionalization of graphene-based nanomaterials, offering insights into safer nanotechnology development.
Cala, Paula; Dariani, Guilherme; Veiga, Eduardo; Macedo, Pedro; Paula, Amauri J.; Almeida, James M.
Predictive Modeling of Surface Tension in Chemical Compounds: Uncovering Crucial Features with Machine Learning Journal Article
Em: J. Braz. Chem. Soc., 2024, ISSN: 1678-4790.
@article{Cala2024,
title = {Predictive Modeling of Surface Tension in Chemical Compounds: Uncovering Crucial Features with Machine Learning},
author = {Paula Cala and Guilherme Dariani and Eduardo Veiga and Pedro Macedo and Amauri J. Paula and James M. Almeida},
doi = {10.21577/0103-5053.20240110},
issn = {1678-4790},
year = {2024},
date = {2024-07-01},
journal = {J. Braz. Chem. Soc.},
publisher = {Sociedade Brasileira de Quimica (SBQ)},
abstract = {<jats:p>Surface tension (SFT) can shape the behavior of liquids in industrial chemical processes, influencing variables such as flow rate and separation efficiency. This property is commonly measured with experimental approaches such as Du Noüy ring and Wilhelmy plate methods. Here, we present machine learning (ML) methodologies that can predict the SFT of hydrocarbons. A comparative analysis encompassing k-nearest neighbors, random forest, and XGBoost (extreme gradient boosting) methods was done. Results from our study reveal that XGBoost is the most accurate in predicting hydrocarbon SFT, with a mean squared error (MSE) of 4.65 mN2 m-2 and a coefficient of determination (R2 ) score of 0.89. The feature importance was evaluated with the permutation feature importance method and Shapley analysis. Enthalpy of vaporization, density, molecular weight and hydrogen content are key factors in accurately predicting SFT. The successful integration of these methodologies holds the potential to impact efficiency in different industry processes.</jats:p>},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
<jats:p>Surface tension (SFT) can shape the behavior of liquids in industrial chemical processes, influencing variables such as flow rate and separation efficiency. This property is commonly measured with experimental approaches such as Du Noüy ring and Wilhelmy plate methods. Here, we present machine learning (ML) methodologies that can predict the SFT of hydrocarbons. A comparative analysis encompassing k-nearest neighbors, random forest, and XGBoost (extreme gradient boosting) methods was done. Results from our study reveal that XGBoost is the most accurate in predicting hydrocarbon SFT, with a mean squared error (MSE) of 4.65 mN2 m-2 and a coefficient of determination (R2 ) score of 0.89. The feature importance was evaluated with the permutation feature importance method and Shapley analysis. Enthalpy of vaporization, density, molecular weight and hydrogen content are key factors in accurately predicting SFT. The successful integration of these methodologies holds the potential to impact efficiency in different industry processes.</jats:p>
Lucchetti, Lanna E. B.; Autreto, Pedro A. S.; Santos, Mauro C.; Almeida, James M.
Cerium doped graphene-based materials towards oxygen reduction reaction catalysis Journal Article
Em: Materials Today Communications, vol. 38, pp. 108461, 2024, ISSN: 2352-4928.
@article{LUCCHETTI2024108461,
title = {Cerium doped graphene-based materials towards oxygen reduction reaction catalysis},
author = {Lanna E. B. Lucchetti and Pedro A. S. Autreto and Mauro C. Santos and James M. Almeida},
url = {https://www.sciencedirect.com/science/article/pii/S2352492824004410},
doi = {https://doi.org/10.1016/j.mtcomm.2024.108461},
issn = {2352-4928},
year = {2024},
date = {2024-02-26},
urldate = {2024-02-26},
journal = {Materials Today Communications},
volume = {38},
pages = {108461},
abstract = {With the global transition towards cleaner energy and sustainable processes, the demand for efficient catalysts, especially for the oxygen reduction reaction, has gained attention from the scientific community. This research work investigates cerium-doped graphene-based materials as catalysts for this process with density functional theory calculations. The electrochemical performance of Ce-doped graphene was assessed within the computation hydrogen electrode framework. Our findings reveal that Ce doping, especially when synergized with an oxygen atom, shows improved catalytic activity and selectivity. For instance, Ce doping in combination with an oxygen atom, located near a border, can be selective for the 2-electron pathway. Overall, the combination of Ce doping with structural defects and oxygenated functions lowers the reaction free energies for the oxygen reduction compared to pure graphene, and consequently, might improve the catalytic activity. This research sheds light from a computational perspective on Ce-doped carbon materials as a sustainable alternative to traditional costly metal-based catalysts, offering promising prospects for green energy technologies and electrochemical applications.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
With the global transition towards cleaner energy and sustainable processes, the demand for efficient catalysts, especially for the oxygen reduction reaction, has gained attention from the scientific community. This research work investigates cerium-doped graphene-based materials as catalysts for this process with density functional theory calculations. The electrochemical performance of Ce-doped graphene was assessed within the computation hydrogen electrode framework. Our findings reveal that Ce doping, especially when synergized with an oxygen atom, shows improved catalytic activity and selectivity. For instance, Ce doping in combination with an oxygen atom, located near a border, can be selective for the 2-electron pathway. Overall, the combination of Ce doping with structural defects and oxygenated functions lowers the reaction free energies for the oxygen reduction compared to pure graphene, and consequently, might improve the catalytic activity. This research sheds light from a computational perspective on Ce-doped carbon materials as a sustainable alternative to traditional costly metal-based catalysts, offering promising prospects for green energy technologies and electrochemical applications.
Trench, Aline B.; Fernandes, Caio Machado; Moura, João Paulo C.; Lucchetti, Lanna E. B.; Lima, Thays S.; Antonin, Vanessa S.; Almeida, James M.; Autreto, Pedro A. S.; Robles, Irma; Motheo, Artur J.; Lanza, Marcos R. V.; Santos, Mauro C.
Hydrogen peroxide electrogeneration from O2 electroreduction: A review focusing on carbon electrocatalysts and environmental applications Journal Article
Em: Chemosphere, pp. 141456, 2024, ISSN: 0045-6535.
@article{TRENCH2024141456,
title = {Hydrogen peroxide electrogeneration from O2 electroreduction: A review focusing on carbon electrocatalysts and environmental applications},
author = {Aline B. Trench and Caio Machado Fernandes and João Paulo C. Moura and Lanna E. B. Lucchetti and Thays S. Lima and Vanessa S. Antonin and James M. Almeida and Pedro A. S. Autreto and Irma Robles and Artur J. Motheo and Marcos R. V. Lanza and Mauro C. Santos},
url = {https://www.sciencedirect.com/science/article/pii/S0045653524003497},
doi = {https://doi.org/10.1016/j.chemosphere.2024.141456},
issn = {0045-6535},
year = {2024},
date = {2024-02-15},
urldate = {2024-01-01},
journal = {Chemosphere},
pages = {141456},
abstract = {Hydrogen peroxide (H2O2) stands as one of the foremost utilized oxidizing agents in modern times. The established method for its production involves the intricate and costly anthraquinone process. However, a promising alternative pathway is the electrochemical hydrogen peroxide production, accomplished through the oxygen reduction reaction via a 2-electron pathway. This method not only simplifies the production process but also upholds environmental sustainability, especially when compared to the conventional anthraquinone method. In this review paper, recent works from the literature focusing on the 2-electron oxygen reduction reaction promoted by carbon electrocatalysts are summarized. The practical applications of these materials in the treatment of effluents contaminated with different pollutants (drugs, dyes, pesticides, and herbicides) are presented. Water treatment aiming to address these issues can be achieved through advanced oxidation electrochemical processes such as electro-Fenton, solar-electro-Fenton, and photo-electro-Fenton. These processes are discussed in detail in this work and the possible radicals that degrade the pollutants in each case are highlighted. The review broadens its scope to encompass contemporary computational simulations focused on the 2-electron oxygen reduction reaction, employing different models to describe carbon-based electrocatalysts. Finally, perspectives and future challenges in the area of carbon-based electrocatalysts for H2O2 electrogeneration are discussed. This review paper presents a forward-oriented viewpoint of present innovations and pragmatic implementations, delineating forthcoming challenges and prospects of this ever-evolving field.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Hydrogen peroxide (H2O2) stands as one of the foremost utilized oxidizing agents in modern times. The established method for its production involves the intricate and costly anthraquinone process. However, a promising alternative pathway is the electrochemical hydrogen peroxide production, accomplished through the oxygen reduction reaction via a 2-electron pathway. This method not only simplifies the production process but also upholds environmental sustainability, especially when compared to the conventional anthraquinone method. In this review paper, recent works from the literature focusing on the 2-electron oxygen reduction reaction promoted by carbon electrocatalysts are summarized. The practical applications of these materials in the treatment of effluents contaminated with different pollutants (drugs, dyes, pesticides, and herbicides) are presented. Water treatment aiming to address these issues can be achieved through advanced oxidation electrochemical processes such as electro-Fenton, solar-electro-Fenton, and photo-electro-Fenton. These processes are discussed in detail in this work and the possible radicals that degrade the pollutants in each case are highlighted. The review broadens its scope to encompass contemporary computational simulations focused on the 2-electron oxygen reduction reaction, employing different models to describe carbon-based electrocatalysts. Finally, perspectives and future challenges in the area of carbon-based electrocatalysts for H2O2 electrogeneration are discussed. This review paper presents a forward-oriented viewpoint of present innovations and pragmatic implementations, delineating forthcoming challenges and prospects of this ever-evolving field.
2023
Almeida, James M.; Ferreira, Conny Cerai; Bandeira, Lucas; Cunha, Renato D.; Coutinho-Neto, Maurício Domingues; Homem-de-Mello, Paula; Orestes, Ednilsom; Nascimento, Regina Sandra Veiga
Em: The Journal of Physical Chemistry B, vol. 0, não 0, pp. null, 2023, (PMID: 37871185).
@article{doi:10.1021/acs.jpcb.3c01707,
title = {Synergistic Interaction of Hyperbranched Polyglycerols and Cetyltrimethylammonium Bromide for Oil/Water Interfacial Tension Reduction: A Molecular Dynamics Study},
author = {James M. Almeida and Conny Cerai Ferreira and Lucas Bandeira and Renato D. Cunha and Maurício Domingues Coutinho-Neto and Paula Homem-de-Mello and Ednilsom Orestes and Regina Sandra Veiga Nascimento},
url = {https://doi.org/10.1021/acs.jpcb.3c01707},
doi = {10.1021/acs.jpcb.3c01707},
year = {2023},
date = {2023-10-23},
journal = {The Journal of Physical Chemistry B},
volume = {0},
number = {0},
pages = {null},
abstract = {Applying surfactants to reduce the interfacial tension (IFT) on water/oil interfaces is a proven technique. The search for new surfactants and delivery strategies is an ongoing research area with applications in many fields such as drug delivery through nanoemulsions and enhanced oil recovery. Experimentally, the combination of hyperbranched polyglycerol (HPG) with cetyltrimethylammonium bromide (CTAB) substantially reduced the observed IFT of oil/water interface, 0.9 mN/m, while HPG alone was 5.80 mN/m and CTAB alone IFT was 8.08 mN/m. Previous simulations in an aqueous solution showed that HPG is a surfactant carrier. Complementarily, in this work, we performed classical molecular dynamics simulations on combinations of CTAB and HPG with one aliphatic chain to investigate further the interaction of this pair in oil interfaces and propose the mechanism of IFT decrease. Basically, from our results, one can observe that the IFT reduction comes from a combination of effects that have not been observed for other dual systems: (i) Due to the CTAB-HPG strong interaction, a weakening of their specific and isolated interactions with the water and oil phases occurs. (ii) Aggregates enlarge the interfacial area, turning it into a less ordered interface. (iii) The spread of individual molecules charge profiles leads to the much lower interfacial tension observed with the CTAB+HPG systems.},
note = {PMID: 37871185},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Applying surfactants to reduce the interfacial tension (IFT) on water/oil interfaces is a proven technique. The search for new surfactants and delivery strategies is an ongoing research area with applications in many fields such as drug delivery through nanoemulsions and enhanced oil recovery. Experimentally, the combination of hyperbranched polyglycerol (HPG) with cetyltrimethylammonium bromide (CTAB) substantially reduced the observed IFT of oil/water interface, 0.9 mN/m, while HPG alone was 5.80 mN/m and CTAB alone IFT was 8.08 mN/m. Previous simulations in an aqueous solution showed that HPG is a surfactant carrier. Complementarily, in this work, we performed classical molecular dynamics simulations on combinations of CTAB and HPG with one aliphatic chain to investigate further the interaction of this pair in oil interfaces and propose the mechanism of IFT decrease. Basically, from our results, one can observe that the IFT reduction comes from a combination of effects that have not been observed for other dual systems: (i) Due to the CTAB-HPG strong interaction, a weakening of their specific and isolated interactions with the water and oil phases occurs. (ii) Aggregates enlarge the interfacial area, turning it into a less ordered interface. (iii) The spread of individual molecules charge profiles leads to the much lower interfacial tension observed with the CTAB+HPG systems.
Antonin, Vanessa S.; Lucchetti, Lanna E. B.; Souza, Felipe M.; Pinheiro, Victor S.; Moura, João P. C.; Trench, Aline B.; Almeida, James M.; Autreto, Pedro A. S.; Lanza, Marcos R. V.; Santos, Mauro C.
Sodium niobate microcubes decorated with ceria nanorods for hydrogen peroxide electrogeneration: An experimental and theoretical study Journal Article
Em: Journal of Alloys and Compounds, vol. 965, 2023, ISSN: 0925-8388.
@article{Antonin2023,
title = {Sodium niobate microcubes decorated with ceria nanorods for hydrogen peroxide electrogeneration: An experimental and theoretical study},
author = {Vanessa S. Antonin and Lanna E. B. Lucchetti and Felipe M. Souza and Victor S. Pinheiro and João P. C. Moura and Aline B. Trench and James M. Almeida and Pedro A. S. Autreto and Marcos R. V. Lanza and Mauro C. Santos},
url = {https://www.sciencedirect.com/science/article/abs/pii/S092583882302666X},
doi = {10.1016/j.jallcom.2023.171363},
issn = {0925-8388},
year = {2023},
date = {2023-07-21},
journal = {Journal of Alloys and Compounds},
volume = {965},
publisher = {Elsevier BV},
abstract = {The present work investigates the catalytic activity of NaNbO3 microcubes decorated with CeO2 nanorods on carbon (1 %, 3 %, 5 %, and 10 % w/w) for H2O2 electrogeneration. The crystalline phases and the morphology of the materials were identified with scanning electron microscopy, transmission electron microscopy, X‐ray diffraction and X-ray Photoelectronic spectroscopy. Contact angle measurements were performed to characterize the hydrophilicity of each material. The H2O2 electrogeneration was assessed by oxygen reduction reaction using the rotating ring-disk electrode technique. Electrochemical characterization results shown an enhancement on the H2O2 electrogeneration by NaNbO3 @CeO2/C-based materials compared to what was obtained with pure Vulcan XC72. The 1 % NaNbO3 @CeO2/C electrocatalyst presented the lower starting potential for the ORR and a 2.3 electron transfer, favoring the 2-electron mechanism and providing a higher H2O2 electrogeneration rate. Also, the enhancement of oxygen-containing functional groups showed the potential to comprehensively tune properties and optimize active sites and, consequently, increases the H2O2 electrogeneration. Density functional theory calculations indicated that NaNbO3 and CeO2 surfaces have a similar low theoretical overpotential for this reaction and that CeO2 improves the catalyst facilitating the electron transfer. These results indicate that NaNbO3 @CeO2/C-based electrocatalysts are promising materials for in situ H2O2 electrogeneration.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The present work investigates the catalytic activity of NaNbO3 microcubes decorated with CeO2 nanorods on carbon (1 %, 3 %, 5 %, and 10 % w/w) for H2O2 electrogeneration. The crystalline phases and the morphology of the materials were identified with scanning electron microscopy, transmission electron microscopy, X‐ray diffraction and X-ray Photoelectronic spectroscopy. Contact angle measurements were performed to characterize the hydrophilicity of each material. The H2O2 electrogeneration was assessed by oxygen reduction reaction using the rotating ring-disk electrode technique. Electrochemical characterization results shown an enhancement on the H2O2 electrogeneration by NaNbO3 @CeO2/C-based materials compared to what was obtained with pure Vulcan XC72. The 1 % NaNbO3 @CeO2/C electrocatalyst presented the lower starting potential for the ORR and a 2.3 electron transfer, favoring the 2-electron mechanism and providing a higher H2O2 electrogeneration rate. Also, the enhancement of oxygen-containing functional groups showed the potential to comprehensively tune properties and optimize active sites and, consequently, increases the H2O2 electrogeneration. Density functional theory calculations indicated that NaNbO3 and CeO2 surfaces have a similar low theoretical overpotential for this reaction and that CeO2 improves the catalyst facilitating the electron transfer. These results indicate that NaNbO3 @CeO2/C-based electrocatalysts are promising materials for in situ H2O2 electrogeneration.
Lucchetti, Lanna E. B.; Autreto, Pedro A. S.; Almeida, James M.; Santos, Mauro C.; Siahrostami, Samira
Unravelling catalytic activity trends in ceria surfaces toward the oxygen reduction and water oxidation reactions Journal Article
Em: React. Chem. Eng., vol. 8, não 6, pp. 1285–1293, 2023, ISSN: 2058-9883.
@article{Lucchetti2023,
title = {Unravelling catalytic activity trends in ceria surfaces toward the oxygen reduction and water oxidation reactions},
author = {Lanna E. B. Lucchetti and Pedro A. S. Autreto and James M. Almeida and Mauro C. Santos and Samira Siahrostami},
doi = {10.1039/d3re00027c},
issn = {2058-9883},
year = {2023},
date = {2023-05-30},
journal = {React. Chem. Eng.},
volume = {8},
number = {6},
pages = {1285–1293},
publisher = {Royal Society of Chemistry (RSC)},
abstract = {<jats:p>Different facets of ceria exhibit activities for the entire spectrum of oxygen electrochemistry.</jats:p>},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
<jats:p>Different facets of ceria exhibit activities for the entire spectrum of oxygen electrochemistry.</jats:p>
