Research

High-entropy alloys (HEAs) are materials characterized by significant configurational disorder, offering promising properties for mechanical, chemical, and electrochemical applications. However, accurately determining their thermodynamics, especially the enthalpy of mixing, remains challenging because of the complexity of these systems and the limitations of empirical models. Here, we introduce an thermodynamic model that leverages properties of dilute solid solutions to accurately predict the enthalpies of mixing in high-entropy alloys. This approach, called Dilute Solution Interpolation (DSI) model, shows excellent agreement with experimental data for binary alloys and significantly outperforms semi-empirical models like the Miedema model. Furthermore, the model extends to multiprincipal component alloys, and we demonstrate its application to the AgAuCuPdPt, identifying possible phase separations. Our findings reveal that using this model offers a viable alternative from dilute solutions to nearly equimolar models for more accurate thermodynamic properties in HEAs. This advancement offers a more precise framework for designing HEAs, minimizing the reliance on empirical parameters. The study's findings provide fresh insights into thermodynamic modeling and could lead to the development of a wider range of high-performance materials. The model developed here is general and can be extended to other HEAs, including those with different crystalline phases or materials, such as oxides and other high-entropy compounds, providing a better prediction of HEAs crystal phases; especially on which compositions could lead to solid solutions. Our results also establish the DSI model's efficiency, characterized by a quadratic O(N²) scaling with the number of components (N). Consequently, the framework provides a scalable pathway for predicting the properties of concentrated multicomponent alloys.

Chemical Enhanced Oil Recovery (EOR) is a central route to maximizing hydrocarbon recovery from mature reservoirs. Although its field-scale success depends on several factors—including surfactant cost, adsorption and retention on the reservoir rock, solubility, and phase behavior—achieving a low oil–water interfacial tension (IFT) is a key enabling requirement. However, the design of surfactants meeting this requirement is impeded by the vast combinatorics of the chemical space and the prohibitive cost of traditional trial-and-error screening. In this study, we present a synergistic materials informatics framework that integrates Machine Learning (ML) and Molecular Dynamics (MD) simulations within an Active Learning (AL) workflow to accelerate surfactant discovery. Unlike static screening approaches, our methodology utilizes MD simulations as a high-fidelity oracle, iteratively labeling a subset of candidates to update a regressor, enabling the identification of high-performance surfactants from the PubChem database. Through this workflow, we identified Dodecylammonium Chloride (DDACl) as a top candidate; subsequent high-fidelity MD simulations confirmed a very low IFT at maximum surface coverage, outperforming widely used analogs such as Cetyltrimethylammonium Bromide (CTAB). We validated these computational findings through experimental tensiometry, confirming the efficacy of DDACl in reducing IFT in several oils with varying API gravities. The convergence of theoretical predictions with experimental results underscores the power of this data-driven framework to overcome the "small data" bottleneck in materials science, offering a scalable pathway for the data-driven discovery of surfactants with low oil–water IFT as candidates for EOR formulation development.

The environmental impact of pollutants is strongly shaped by the composition of the matrices in which they occur. Co-occurring contaminants can generate mixture-toxicity effects that are often difficult to predict, and the organic fraction of these matrices can influence pollutant transport, persistence, and bioavailability. Due to their distinctive properties, when nanomaterials reach the environment, they alter the fate and toxicity of cooccurring pollutants potentially increasing their adverse effects on living organisms through multiple mechanisms. Specifically, nanomaterials adsorb components of the medium, especially organic molecules, forming an "ecocorona". The influence of the ecocorona on nanomaterial-pollutant interactions and its consequent effects on mixture ecotoxicity remain insufficiently explored. In this study, we investigated the acute toxicity of mixtures of atrazine (ATZ), graphene oxide (GO), and tannic acid (TA) in zebrafish using an integrated experimental and computational approach. We assessed ATZ-induced developmental, neurotoxic, and histopathological effects in zebrafish embryos and found that GO and TA differentially modulate ATZ toxicity when applied individually or together. To understand the underlying mechanisms, we performed atomistic and coarse-grained simulations of ATZ-TA interactions as well as their adsorption onto the GO surface. We found that the ATZ forms stable complexes with TA, which adsorbs onto GO flakes, which explains the observed mixture effects.

The stability of chemically complex nanoparticles is governed by an immense configurational space arising from heterogeneous local atomic environments across surface and interior regions. Efficiently identifying low-energy configurations within this space remains a central challenge for first-principles-based materials discovery, particularly when the available reference data are limited. Here, we introduce a data-efficient and physically interpretable machine-learning framework based on a fragmented, layer-resolved descriptor that explicitly decomposes nanoparticles into surface, intermediate, and core environments using a topology-driven definition. This representation preserves a compact and fixed feature dimensionality while retaining spatial resolution, enabling controlled emphasis on different regions of the nanoparticle through physically motivated weighting schemes. Coupled with gradient-boosted decision-tree models and a ranking-based learning strategy, the proposed framework enables accurate identification of the most stable nanoparticle configurations using only a few hundred density functional theory reference calculations. Ranking performance metrics demonstrate near-saturation of correlation, high top-k recall, and rapidly vanishing regret at moderate training set sizes, highlighting the strong data efficiency of the approach. Beyond predictive performance, layer-weighting and SHAP-based interpretability analyses reveal how surface segregation, coordination topology, and local chemical disorder contribute differently to stability across spatial regions of the nanoparticle. The framework is system and code-agnostic, requiring only atomic coordinates, chemical species, and a scalar target energy, and is therefore directly transferable to other multicomponent nanostructures and to alternative first-principles or machine-learning energy methods.

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.

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 multi-class 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 F\textsubscript{1}-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 non-perovskite 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.

The discovery of new mineral phases capable of transporting water into the Earth's mantle via subduction zones is essential for understanding the deep Earth water cycle and its influence on mantle dynamics. Recent experimental and computational studies have proposed that certain hydrated oxyhydroxides may remain stable under deep mantle conditions, particularly within cold subducting slabs. Here, we report an advanced synchrotron multi-technique study on a natural complex iron oxyhydroxide mineral preserved within a Juína diamond, which may offer new evidence on water transport via cold subducted slabs and highlights the potential role of iron oxyhydroxides as carriers of volatiles into Earth's interior.

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.

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.

We present a proposed activity regarding the Rayleigh scattering phenomenon, explaining why the sky is blue. Milk-in-water suspensions were used, illuminated by smartphone light sources. Scattering quantification was performed using Arduino-based RGB sensors, enabling real-time detection of light transmitted through the suspension. We observed lower intensity in the blue component and higher intensity in the red component, reproducing the characteristic effect of a sunset. Furthermore, this activity can be conducted at various levels, ranging from a qualitative approach based on visual observation to an automated measurement process using the code provided in this study. Additionally, educators can challenge students to develop their own code. Therefore, this experiment aims to spark learner interest in an everyday topic, facilitating the understanding of complex optical phenomena within an interdisciplinary learning context. The implementation proved successful, as evidenced by the students' spontaneous presentation of the activity at outreach events and the development of the automation code for the experiment described in this work.

Bio-based adhesives are achieving attention due to their sustainability characteristics. However, the diversity of renewable feedstocks, combined with several approaches to top-down the biomass for creating an on-demand adhesive is still a substantial challenge. Here, we developed a bio-based adhesive suitable for versatile substrates, including underwater applications. The composition is based on a simple water-based colloidal mixture consisting of lignin and natural rubber latex (NRL). State of art analysis and theoretical approaches provided a comprehensive elucidation regarding the interactions at nano-molecular scale, revealing that the interactions between lignin and NRL were energetically favorable and driven by hydrogen bonds and van der Waals forces. Several substrates were pressure-adhered using the optimized adhesive formulation, demonstrating excellent adhesion properties, outperforming neat NRL. Furthermore, the macro prospective replicated the nanoscale-molecular interactions, indicating an optimal extraction route for lignin in the bio-based adhesive. This insight not only provides superior adhesion performance but also highlights a sustainable alternative to petroleum-based adhesives.

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.

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.

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.

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.

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.

Computational approaches, such as density functional theory (DFT) in conjunction with descriptor-based analysis and computational hydrogen electrode, have enabled exploring the intricate interactions between catalyst surfaces and oxygen species allowing for the rational design of materials with optimized electronic structure and reactivity for oxygen reduction reaction (ORR). The identification of active sites and the tuning of catalyst compositions at the atomic scale have been facilitated by computational simulations, accelerating the discovery of promising ORR catalysts. In this contribution, the insights provided by the computational analysis to understand the fundamental reasons behind inherent ORR overpotentials in the experimental reported catalysts are discussed. Various strategies to overcome the limitations in ORR catalysis using computational design are discussed. Several alternative earth-abundant and cost-effective materials suggested by computational guidance to replace platinum-based catalysts are reviewed. The accuracy of DFT and the role of solvent and electrolyte pH are outlined based on the understanding provided by the computational insight. Finally, an overview of recent achievements in employing materials informatics to accelerate catalyst material discovery for ORR is provided. These computational advancements hold great promise for the development of efficient and cost-effective ORR catalysts, bringing us closer to realizing the full potential of fuel cells as efficient electrochemical energy conversion technologies.

The oxygen reduction reaction (ORR) plays a pivotal role in clean energy generation and sustainable chemical production, particularly in the synthesis of hydrogen peroxide (H2O2). In this study, WO3/Vulcan-XC72 electrocatalysts have been synthesized and characterized for ORR applications. We assessed the ratio of WO3 to Vulcan-XC72 and investigated the impact of electrolytes pH (covering acidic and alkaline media) on the ORR process. The results revealed that WO3 with a monoclinic crystalline phase and nanoflower-like morphology was successfully synthesized, and confirmed an improvement in surface properties, with an increase in hydrophilicity and superficial oxygenated species. Electrochemical studies showed that WO3/C was the most selective for H2O2 electrogeneration, compared to pure Vulcan-XC72, in both acidic and alkaline media. These results indicate that the ORR on the WO3/C electrocatalyst surface has a pH-dependent mechanism. Using WO3/C GDEs, an accumulation of 862 mg L-1 of H2O2 was achieved after 120 min of electrolysis at 100 mA cm-2. The higher selectivity of WO3/C could be related to the presence of more oxygen functional acid species on the catalyst surface and increased hydrophilicity compared to pure Vulcan, as well as a synergistic effect of the WO3 nanoflowers in ORR, confirmed by theoretical calculations. The results reveal that WO3/Vulcan is a promising catalyst for H2O2 electrogeneration via the ORR.

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.

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.

Carbon dioxide (CO2) is a prominent greenhouse gas that contributes significantly to global warming. To combat this issue, one strategy is the conversion of CO2 into alcohols and hydrocarbons, which can be used as fuels and chemical feedstocks. Consequently, a substantial volume of scientific literature has been dedicated to investigating different materials and reaction conditions to facilitate the CO2 reduction reaction (CO2RR) into these so-called high-value products. However, the vastness of this literature makes it challenging to stay updated on recent discoveries and review the most promising materials and conditions that have been explored. To address this issue, we applied natural language processing tools to extract valuable data from 7292 published articles in the scientific literature. Our analysis revealed the emergence of new materials such as cesium–lead–bromide perovskites and bismuth oxyhalides that have been recently used in the CO2RR and identified Bi-based catalysts as the most selective for HCOO– production. Furthermore, we gleaned insights into the composition of other elements and materials commonly employed in the CO2RR, their relationship to product distribution, and the prevalent electrolytes used in the CO2 electrochemical reduction. Our findings can serve as a foundation for future investigations in the realm of catalysts for CO2RRs, offering insights into the most promising materials and conditions to pursue further research.

Ferulic acid (FA) is a highly promising phenolic compound known for its pharmacological activities, which include antioxidant, anti-inflammatory, and anticancer properties. However, limited bioavailability and low solubility hinder their therapeutic application. To overcome these challenges, nanocarriers can be designed by using computer simulation studies to enhance performance and stability. Classical molecular dynamics (CMD), widely employed to study interfaces, especially water–oil interfaces, plays a vital role in predicting molecular interactions and guiding the development of more efficient and effective drug delivery systems. Thus, this study aimed to develop FA nanoemulsions containing hazelnut oil (NE-FAHO) or medium-chain triglycerides (NE-FAMCT) with a previous study about the components that constitute the nanoemulsion and interfacial tension by CMD simulation. The FA-loaded nanoemulsions were prepared by the spontaneous emulsification method, and four different concentrations were used: 0.5 to 2.0 mg/mL. The nanoemulsions were characterized by measuring their droplet size, zeta potential, pH, FA content, encapsulation efficiency, and morphology. In addition, the stability and photostability of the FA nanoemulsions were evaluated. Analyzing the concentrations tested, NE-FAMCT at 1.5 mg/mL exhibited the best results in terms of characterization and stability, maintaining a content close to 95% for 60 days and providing photoprotection to FA. Furthermore, NE-FAMCT presented controlled release and significant antioxidant effect. Considering the significant FA anti-inflammatory and antioxidant potential, this study may contribute to the future development of topical pharmaceutical formulations aimed at treating skin-related diseases.

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.

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.

Improved catalysts are critical for more environmentally friendly, and long-term oxygen electrochemical reactions. Computational catalysis can provide atomic level information that is critical for optimizing the next generation of electrocatalysts. It has been demonstrated that by varying the exposed planes, the catalytic performance of metallic oxides can be tuned. Herein, we investigate the role of CeO2 surface orientations (100), (110), (111), (221), and (331) in enhancing catalytic activity toward various oxygen electrochemical reactions ranging from 4- and 2-electron oxygen reduction reactions (ORR) to 4-, 2- and 1-electron water oxidation reactions (WOR) using density functional theory (DFT) calculations in conjunction with the computational hydrogen electrode. Our results indicate that the CeO2(100) facet is the most promising for 4-electron ORR, with a theoretical limiting potential of 0.52 V. We also show that the presence of oxygen vacancies can enhance the 4-electron ORR activity of the CeO2(110) and CeO2(111) surfaces. Besides, CeO2(100) is selective for the 4-electron WOR while CeO2(110) and CeO2(111) are selective for the 2-electron and 1-electron WOR, respectively. Oxygen vacancies shift all the above three facets towards the 4-electron WOR. This work sheds light on the role of different ceria facets in various oxygen electrochemical reactions which is critical for developing better catalysts.

Naphthenic Acids (NA) are important oil extraction subproducts. These chemical species are one of the leading causes of marine pollution and duct corrosion. For this reason, understanding the behavior of NAs in different saline conditions is one of the challenges in the oil industry. In this work, we simulated several naphthenic acid species and their mixtures, employing density functional theory calculations with the MST-IEFPCM continuum solvation model, to obtain the octanol–water partition coefficients, together with microsecond classical molecular dynamics. The latter consisted of pure water, low-salinity, and high-salinity environment simulations, to assess the stability of NAs aggregates and their sizes. The quantum calculations have shown that the longer chain acids are more hydrophobic, and the classical simulations corroborated: that the longer the chain, the higher the order of the aggregate. In addition, we observed that larger aggregates are stable at higher salinities for all the studied NAs. This can be one factor in the observed low-salinity-enhanced oil recovery, which is a complex phenomenon. The simulations also show that stabilizing the aggregates induced by the salinity involves a direct interplay of Na+ cations with the carboxylic groups of the NAs inside the aggregates. In some cases, the ion/NA organization forms a membrane-like circular structural arrangement, especially for longer chain NAs.

Asphaltenes are a severe problem for the oil industry. The high content of aromatic and aliphatic hydrocarbons in asphaltenes poses a challenge for efficient methods of the solubilization and degradation of their components. The main goal of this study was to investigate an efficient and innovative method for asphaltene solubilization with surfactants to produce supramolecular aggregates with affinity by magnetic nanoparticles (Fe3O4) for magnetic separation and degradation. Asphaltene mixed with the cationic surfactant cetyltrimethylammonium bromide (CTAB) was both solubilized in chloroform and the solvent dried with N2 to produce a film that was resuspended in water and formed a stable colloid with asphaltene incorporated in CTAB micelles. The suspensions of CTAB/asphaltene supramolecular aggregates obtained at different surfactant/asphaltene ratios were characterized by dynamic and static light scattering (DLS and SLS) and by electrophoretic mobility for ζ potential determination. CTAB concentrations of 30 and 60 mM produced spherical supramolecular aggregates (SMAs) of size between 100 and 200 nm with polydispersity. The ζ potential of CTAB micelles loaded with asphaltenes increased from +9.17 +/– 4.6 to +56.7 +/– 5.8 eV. Electron paramagnetic resonance revealed that asphaltene forms stable free radicals in CTAB micelles. Classical molecular dynamics simulations were also used to study interactions of the functional groups of asphaltenes. The association with CTAB micelles provided the binding affinity of asphaltenes for nanoparticulate magnetite (Fe3O4) and precipitation of the most CTAB content. In this condition, Fe3O4 promoted the degradation of asphaltenes to low molecular mass products. Therefore, incorporation in CTAB micelles is a simple and innovative method contributing to asphaltene removal, degradation, and possible conversion to products with aggregated value.

In this work, the efficiency of partially hydrophobized hyperbranched polyglycerols (HPG11 and HPG12) as cetyltrimethylammonium bromide (CTAB) carriers was evaluated to prevent surfactant losses by adsorption on reservoir rocks surface during enhanced oil recovery (EOR) processes. Interactions between surfactant and polymers were studied by conductivity, zeta potential, and particle size measurements showing that complexes were formed between the components. The ability of PG, HPG11, HPG12 and those complexes to reduce the interfacial tension (IFT) was verified and one of the complexes was able to reduce the IFT to values under 1.0 mN/m, suggesting the occurrence of a synergy between the components. Molecular dynamics simulations indicated the preferential sites of interaction between surfactants and HPGs. HPG11:CTAB and HPG12:CTAB complexes' ability to permeate an unconsolidated porous medium and deliver the surfactant at the water–oil interface, increasing oil production, was evaluated through transport and oil displacement tests, and the results showed that the HPG12:CTAB complex led to almost 90% of oil recovery.

In existing or operating oil fields, enhanced oil recovery (EOR) methods are employed to increase production, which can contribute to meet fossil fuel demand. This research proposes a new EOR method by adding Cu(II) ions into the injected seawater, which shows a significant incremental oil production in carbonate reservoirs. An important remark in this study is the Cu(II) incorporation into the cores, with mineralization of atacamite on the carbonate surface, and copper complexation with the acid fraction of the oil. In addition, we observed oil removal up to the last monolayer on calcite surfaces. Based on these observations and first-principles calculations, we propose an EOR mechanism where the acidic oil desorbs from the rock surface by forming acid-copper complexes that are dragged by the injected fluids. In summary, we propose a simple and cost-effective EOR method with transition metals, which may significantly increase the yield of low-recovery carbonate reservoirs and reduce the environmental impact of exploring new reservoirs.

The 2-electron pathway of the oxygen reduction reaction is an unwanted process in the development of fuel cells. In contrast, it has gained the scientific community's attention due to its importance as a promising way of removing emergent pollutants and endocrine disruptors from water bodies and a more sustainable alternative for large-scale commercial hydrogen peroxide production. Cerium oxide has shown remarkable potential and selectivity experimentally for this mechanism, and its possible applications, exceeding the previous reference materials. In this work, we studied the 2-electron pathway for oxygen reduction on different ceria-cleaving directions (100), (110), (221), and (331) by first principles methods based on density functional theory. Our results show that the (100) surface is the most favorable for reduction, with the (331) crystallographic plane also showing potential for good catalytic activity. This fact could be essential for designing new nanostructures, with higher portions of those planes exposed, for higher catalytic activity.

The presence of water in hydrated cement has significant effects on the properties of concrete. However, direct correlation of water with the macroscopic properties of concrete remains a major challenge. In this study, we use molecular dynamics simulations to determine the water dynamical properties in four pore environments of the calcium silicate hydrate (C–S–H) colloid model; interlayer pores, gel pores, connected pores and in the vicinity of a finite disk–like globule particles. It was revealed that the translational and rotational dynamics are decoupled, consequently, the translational dynamics govern the calculated T2 relaxation time at the surface. Based on NMR T2–T2 relaxation investigation, we showed an exchange between 1 and 4 nm connected pores. At short times, the adsorption/desorption of water from a 1 nm pore is limited to the edges, a behavior linked to water sorption hysteresis. The properties of water in the proximity of a finite C–S–H globule and confined in infinite pore models are comparable.

Obtaining a precise theoretical description of the spectral properties of liquid water poses challenges for both molecular dynamics (MD) and electronic structure methods. The lower computational cost of the Koopmans-compliant functionals with respect to Green’s function methods allows the simulations of many MD trajectories, with a description close to the state-of-art quasi-particle self-consistent GW plus vertex corrections method (QSGW + fxc). Thus, we explore water spectral properties when different MD approaches are used, ranging from classical MD to first-principles MD, and including nuclear quantum effects. We have observed that different MD approaches lead to up to 1 eV change in the average band gap; thus, we focused on the band gap dependence with the geometrical properties of a system to explain such spread. We have evaluated the changes in the band gap due to variations in the intramolecular O–H bond distance and HOH angle, as well as the intermolecular hydrogen bond O···O distance and the OHO angles. We have observed that the dominant contribution comes from the O–H bond length; the O···O distance plays a secondary role, and the other geometrical properties do not significantly influence the gap. Furthermore, we analyze the electronic density of states (DOS), where the KIPZ functional shows good agreement with the DOS obtained with state-of-art approaches employing quasi-particle self-consistent GW plus vertex corrections. The O–H bond length also significantly influences the DOS. When nuclear quantum effects are considered, broadening of the peaks driven by the broader distribution of the O–H bond lengths is observed, leading to a closer agreement with the experimental photoemission spectra.

The two-electron pathway of the oxygen reduction reaction has been gaining attention from the scientific community due to its capability of forming radical hydroxyl. In addition, it is a promising way of removing emergent pollutants, like dyes, pharmaceuticals, hormones, pesticides, and endocrine disruptors from water bodies, a very serious problem that currently challenges scientists and grows at a global scale. Theoretical calculations have already guided, over the last decade, the development of better catalysts for the oxygen reduction reaction. However, this mechanism had usually been, until recently, taken as an unwanted process since the preferred route for energy generation and the main focus of these studies is the four-electron pathway. This review summarizes the recent progress on computational calculations from the hydrogen peroxide generation process point of view, specifically focused on carbon-based materials.

Under extreme conditions, such as harsh thermodynamical environment and spatial confinement, fluids can reveal unique properties. In this work, we investigate the effects on the interfacial and transport properties of fluids confined in nanopores that are due to the spatial confinement, surface hydrophilicity, and fluid composition. We perform fully atomistic molecular dynamics of water, brine, oil, and combinations thereof, confined within amorphous silica nanopores with the radius ranging from 1.0 to 2.4 nm. We have also studied the oil infiltration on nanopores previously filled with water or brine, mimicking the natural processes when the oil is geologically formed and infiltrates a porous media. We observe that an adsorbed water/brine layer remains on the surface of the nanopores after the oil infiltration, altering the interaction of the oil with the confining surface and leading to changes of their interfacial tensions and viscosities. The presence of the ions in the brine thickens the adsorbed water layer, preventing the oil from infiltrating the nanopores with 1.0 nm radius. Thus, we have observed a limit on the pore size for oil infiltration for brine-filled pores.

The physical chemistry mechanisms behind the oil–brine interface phenomena are not yet fully clarified. The knowledge of the relation between brine composition and concentration for a given oil may lead to the ionic tuning of the injected solution on geochemical and enhanced oil recovery processes. Thus, it is worth examining the parameters influencing the interfacial properties. In this context, we have combined machine learning (ML) techniques with classical molecular dynamics simulations (MD) to predict oil/brine interfacial tensions (IFT) effectively and compared this process to a linear regression (LR) method. To diversify our data set, we have introduced a new atomistic crude oil model (medium) with 36 different types of hydrocarbon molecules. The MD simulations were performed for mono- and multicomponent (toluene, heptane, Heptol, light, and medium) oil systems interfaced with sulfate and chloride brines with varying cations (Na+, K+, Ca2+, and Mg2+) and salinity concentration. Thus, a consistent IFT data set was built for the ML training and LR fitting at room temperature and pressure conditions, over the feature space considering oil density, oil composition, salinity, and ionic concentrations. On the basis of gradient boosted (GB) algorithms, we have observed that the dominant quantities affecting the IFT are related to the oil attributes and the salinity concentration, and no specific ion dominates the IFT changes. When the obtained LR model was validated against MD and experimental data from the literature, the error varied up to 2% and 9%, respectively, showing a robust and consistent transferability. The combination of MD simulations and ML techniques may provide a fast and cost-effective IFT determination over multiple and complex fluid–fluid and fluid–solid interfaces.

The goal of this review paper is two-fold: bringing an updated survey on the literature of the proposed mechanisms behind the low-salinity enhanced oil recovery (EOR) and propose ways to model them based on simulations coupling atomic to reservoir scales. The low salinity water injection (LSWI) presents some advantages over other EOR techniques since it is a cost-effective method, has no inherent environmental damage and does not affect the subsequent stages of crude oil treatment and refinement. The LSWI is particularly interesting for exploration and production on pre-salt carbonate reservoirs. We couple the LSWI mechanisms with molecular modeling methodologies, addressing their use to describe the EOR via LSWI. From the molecular modeling, one can obtain parameters for the large-scale reservoir simulators, thus, improving their accuracy. Therefore, the molecular modeling approaches are complementary tools to optimize the EOR via LSWI. Among all the involved mechanisms on the LSWI, the wettability alteration is pointed out by several authors as the fundamental one, to explain the EOR. However, there are controversies related to its cause: salting-in effect, multi-component ionic exchange, pH alteration, electric double layer expansion, fines migration, limited release of particles and osmotic pressure are among the main proposals. In this sense, several molecular modeling techniques have been explored to foster theories that explain the possible mechanisms and optimize the oil production combining the molecular dynamics simulations and Ab initio calculations with reservoir simulators.

This work, is based on the multiscale coupling between molecular simulations and reservoir simulators, to explore the brine composition for enhanced oil recovery via the low salinity water injection (LSWI) processes. To achieve this goal, molecular simulations were performed, providing physical-chemistry parameters to reservoir simulators and validate the proposed brine compositional model. The key data required within reservoir simulators are related to the chemical reactions, which are occurring due to the LSWI process, such as their free energies, kinetic constants, ionic strengths, chemical activities, and activation energies. To improve the accuracy of this input dataset, the main aqueous phase geochemical reactions were mapped, adsorption energies of hydrocarbons and brine ions on calcite surface were determined and ions-bearing calcium carbonate were evaluated. The calculations were based on the density functional theory (DFT) and classical molecular dynamics (MD) using Quantum-ESPRESSO and LAMMPS codes, respectively. The geochemical reactions that take place at mineral dissolution and ionic release, related to the LWSI process (MgSO4, CaSO4, BaSO4, Na2CO3, and CaCO3), were also determined. The obtained chemical equilibrium showed that the MgSO4 dissolution reaction was favored, while other minerals did not show a similar trend. Adsorption studies of organic the molecules naphthalene and anthracene over different surface sites were performed. The adsorption energies were similar for both molecules, where the most favorable configuration has the rings oriented parallel to the mineral surface. The potential of mean force obtained for brine ion adsorption suggested that there were no barriers for adsorbing Ca2+ and CO32- brine ions on calcite surface. In contrast, the other ions adsorption (Na+ and Cl-) have presented higher estimated activation energies. The energetic difference showed that the SO42- incorporation in calcite is more favorable than Mg2+. The Ba2+ showed unfavorable incorporation energy. The thermodynamic properties (free energies, entropies, and heat capacities) were calculated from the vibrational properties. Obtaining such input data by molecular simulations can significantly reduce uncertainties, by increasing the reservoir simulators predictive power, facilitating the optimization and understanding of the processes involved in the injection of low salinity fluids. From these results, the obtained equilibrium constants, free energies and adsorption energies can be used as input data in further reservoir simulators. In addition, it would allow the validation of the proposed model from the understanding of the physical processes underlying LSWI.

In this work, we present a multiscale approach based on first-principles calculations and classical molecular dynamics methods, to investigate the enhanced oil recovery via low-salinity water injection (EOR-LSWI). Salting-in effect, wettability, pH alteration, electrical double layer and the main geochemical reactions involved in the multicomponent ionic exchanges mechanism were analyzed in order to understand their contribution, also to provide an overall phenomenological perspective of the involved phenomena with a proposed feedback control system. The first-principles calculations were based on density functional theory, carry out in the Quantum-ESPRESSO package, to determine the adsorption energies of hydrocarbons (propionic and pentanoic acids and phenol) on calcite (CaCO3) {10.4} surface. In addition, we have obtained the free energy variations for the minerals dissolution processes. The solvent effect was taken into account for the geochemical reactions through a continuum dielectric. The interface between calcite and API brine was investigated through steered classical molecular dynamics, as implemented in the LAMMPs code to evaluate the brine ions adsorption/desorption on calcite surface and characterize the electrostatic environment in the vicinity of the calcite-brine-oil interfaces. Our results showed that the adsorption energies for the deprotonated molecules were lower than the ones for the neutral cases, highlighting the pH effect in the desorption processes. The pH also played a role in the calcite dissolution, since the free energy variation (ΔG) of the dissolution process mediated by H3O+ was lower than the ΔG for the neutral pH process. We found the lowest dissolution ΔG for the MgSO4 mineral (bulk), indicating that Mg2+ and SO42- ions would be abundant in the solution. In contrast, the other minerals exhibit a positive ΔG. Ions adsorption/desorption on calcite are isoergic and suggest an equilibrium between Ca2+ and CO32- ions. In contrast, the Na+ and Cl- ions adsorption were not found to be a spontaneous process. Moreover, the potential of mean force profile for Ca2+ and CO32- ions showed a layered structuring, which indicates that the ion hydration energy is related to the adsorption/desorption process. Such results may contribute to cause-effect understanding of correlations among the mechanisms in EOR-LSWI and help to propose an optimal brine composition to maximize the oil recovery.

This work provides an immersive visualization of oil & gas relevant systems and enhanced oil recovery (EOR) processes at the nanoscale by coupling molecular dynamics (MD) simulations with gamming virtual reality (VR) technologies. The main objective is to understand oil/brine/rock interfaces at molecular level and identify the underlying EOR mechanisms at the atomic scale. Within this immersive experience, the user can directly interact and enhance its perception of atomic environment for EOR applications. The experiences cover nano-EOR, nano-IOR and low-salt processes at nanoscale based on MD calculations of nanoparticles at oil-brine interfaces, oil-brine at silica nanopores and calcite-brine-oil interfaces, respectively. The MD simulations are performed with the Lammps package. The visualizations were done with an HTC Vive and Oculus Rift virtual reality headsets with the Nomad VR and Unitymol software. For the Nomad VR, the trajectories are previously saved from a Lammps molecular dynamics simulation, whereas, for the Unitymol, the simulation with Lammps is performed on-the-fly through the iMD (interactive MD) plugin. The user can visualize and navigate through the trajectories using the Nomad VR. Furthermore, the Unitymol also allows the user apply forces on selected molecules on real time during the VR experience. As means of comparison, the visualization was also performed with cell-phone based VR headsets with the Nomad VR application. Our demonstrations show that VR combined with molecular simulations can be an interesting and attractive way to improve the perception of the nanoscale for the general public. Additionally, it is an emergent tool to characterize, improve the understanding and provide molecular insights about nanosystems and the EOR methods, and also to be integrated with on-going digitalization processes within the oil & gas industry.

The complexity displayed by nanofluidic-based systems involves electronic and dynamic aspects occurring across different size and time scales. To properly model such kind of system, we introduced a top-down multilevel approach, combining molecular dynamics simulations (MD) with first-principles electronic transport calculations. The potential of this technique was demonstrated by investigating how the water and ionic flow through a (6,6) carbon nanotube (CNT) influences its electronic transport properties. We showed that the confinement on the CNT favors the partially hydrated Na, Cl, and Li ions to exchange charge with the nanotube. This leads to a change in the electronic transmittance, allowing for the distinguishing of cations from anions. Such an ionic trace may handle an indirect measurement of the ionic current that is recorded as a sensing output. With this case study, we are able to show the potential of this top-down multilevel approach, to be applied on the design of novel nanofluidic devices.

Calcite–fluid interface plays a central role in geochemical, synthetic, and biological crystal growth. The ionic nature of the calcite surface can modify the fluid–solid interaction and the fluid properties under spatial confinement and can also influence the adsorption of chemical species. We investigate the structure of the solvent and ions (Na, Cl, and Ca) at the calcite–aqueous solution interface under confinement and how such environment modifies the properties of water. To properly investigate the system, molecular dynamics simulations were employed to analyze the hydrogen bond network and to calculate NMR relaxation times. Here, we provide a new insight with additional atomistically detailed analysis by relating the topology of the hydrogen bond network with the dynamical properties in nanoconfinement interfaces. We have shown that the strong geometrical constraints and the presence of ions do influence the hydrogen bond network, resulting in more extended geodesic paths. Hydrogen bond branches connect low to high dynamics molecules across the pore and hence may explain the gluelike mechanical properties observed in the confinement environment. Moreover, we showed that the surface water observed at the calcite interface is characterized by slow transversal spin relaxation time (T2) and highly coordinated water molecules. The physical and electrostatic barrier emerged from the epitaxial ordering of water results in a particular ionic distribution, which can prevent the direct adsorption of a variety of chemical species. The implications of our results delineate important contributions to the current understanding of crystallization and biomineralization processes.

The heterogeneity and complexity of the cement structure and processes makes the interpretation of experimental data challenging. Atomistic simulations allow investigations at the atomic level of interactions, thus having the potential to provide complementary information to experiments. In this regard, the investigation of the transferability of the available force fields as well their ability to predict the properties of interest is an important prerequisite. In this work, we compare CLAYFF force field against first principles Density Functional Theory (DFT) calculations focusing on its ability to predict structural, vibrational and thermodynamic properties of cement phases differing in the degree of hydration. The systems studied include tobermorite 9 Å, 11 Å, 14 Å, gypsum, tricalcium aluminate and ettringite. Our results indicate that CLAYFF describes well the lattice parameters within acceptable errors. However for the vibrational properties, there is a significant alteration in the silicate, sulfate, water and OH frequencies in comparison to DFT and experimental results. DFT Bader charge analysis indicate that the charge on the interlayer calcium ions in tobermorite does not change with increase in hydration, implying that the nature inter-atomic bonding within the layers remain unchanged. For the thermodynamic quantities investigated (i.e. Helmholtz free energy, entropy and specific heat), CLAYFF results are in agreement with DFT calculations. Our findings indicate that water enhances the stability of the hydrated phases based on the lower values of the Helmholtz free energy. We demonstrate that CLAYFF can capture consistently the thermodynamic properties of cement phases.

We study water confined in calcite (104) slit pores from 6 to 1 nm by molecular dynamics. By determining NMR parameters combined with hydrogen bond network analysis, we provide an important contribution to the understanding of the dynamics of water confined. The water dynamics was found uncorrelated upon confinement within calcite, with the translational dynamics highly dependent on the local density variations and the rotational dynamics varying with local hydrogen bond connectivity. A water layered structuring is observed, and the layer by layer analysis reveals that translational dynamics are the main contribution to spin relaxation of near surface water molecules. The T2 relaxation time for water molecules directly hydrogen bonded to the surface is short and pore size independent; however, a bulk-like spin relaxation is observed at the center of pores larger than 3 nm. The hydrogen bond network of confined water has a more continuous path topology that results in the slightly longer rotational correlation time for water located up to 2 nm from the surface. Moreover, the number of tetrahedral geometric patterns which are associated with bulk water is reduced upon confinement. The confinement effects are enhanced mainly in the 1 nm pore due to overlap of surface effects.

Fluid flow through minerals pores occurs in underground aquifers, oil and shale gas reservoirs. In this work, we explore water and oil flow through silica nanopores. Our objective is to model the displacement of water and oil through a nanopore to mimic the fluid infiltration on geological nanoporous media and the displacement of oil with and without previous contact with water by water flooding to emulate an improved oil recovery process at nanoscale (NanoIOR). We have observed a barrier-less infiltration of water and oil on the empty (vacuum) simulated 4 nm diameter nanopores. For the water displacement with oil, we have obtained a critical pressure of 600 atm for the oil infiltration, and after the flow was steady, a water layer was still adsorbed to the surface, thus, hindering the direct contact of the oil with the surface. In addition, oil displacement with water was assessed, with and without an adsorbed water layer (AWL). Without the AWL, the pressure needed for oil infiltration was 5000 atm, whereas, with the AWL the infiltration was observed for pressures as low as 10 atm. Hence, the infiltration is greatly affected by the AWL, significantly lowering the critical pressure for oil displacement.

Graphane, hydrogenated graphene, can be patterned into electronic devices by selectively removing hydrogen atoms. The most simple of such devices is the so-called nanoroad, analogous to the graphene nanoribbon, where confinement—and the opening of a gap—is obtained without the need for breaking the carbon bonds. In this work we address the electronic transport properties of such systems considering different hydrogen impurities within the conduction channel. We show, using a combination of density functional theory and non-equilibrium Green’s functions, that hydrogen leads to significant changes in the transport properties and in some cases to current polarization.

Nitrogen-doped carbon nanotubes can provide reactive sites on the porphyrin-like defects. It is well known that many porphyrins have transition-metal atoms, and we have explored transition-metal atoms bonded to those porphyrin-like defects in N-doped carbon nanotubes. The electronic structure and transport are analyzed by means of a combination of density functional theory and recursive Green's function methods. The results determined the heme B–like defect (an iron atom bonded to four nitrogens) is the most stable and has a higher polarization current for a single defect. With randomly positioned heme B defects in nanotubes a few hundred nanometers long, the polarization reaches near 100%, meaning they are effective spin filters. A disorder-induced magnetoresistance effect is also observed in those long nanotubes, and values as high as 20 000% are calculated with nonmagnectic eletrodes.

Semiconductor optoelectronic devices based on GaN and on InGaN or AlGaN alloys and superlattices can operate in a wide range of wavelengths, from far infrared to near ultraviolet region. The efficiency of these devices could be enhanced by shrinking the size and increasing the density of the semiconductor components. Nanostructured materials are natural candidates to fulfill these requirements. Here we use the density functional theory to study the electronic and structural properties of nanotubes of GaN, AlN, and their alloys, as well as their heterojunctions. The AlGaN alloy nanotubes exhibit direct band gaps for the whole range of Al compositions, with band gaps varying from 3.45 to 4.85 eV, and a negative band gap bowing coefficient of −0.14 eV. The GaN/AlGaN alloy nanotube heterojunctions show a type-I band alignment, with the valence band offsets showing a non-linear dependence with the Al content in the nanotube alloy. The results show the possibility of engineering the band gaps and band offsets of these III-nitrides nanotubes by alloying on the cation sites.