Matter Sciences

52 items

DOAJ (DOAJ: Directory of Open Access Journals) Oct 1, 2026
The work developed and characterized two deferoxamine-loaded topical formulations, a polymeric film and a hydrogel, based on Opuntia ficus-indica mucilage, aimed at wound-dressing applications.
The work developed and characterized two deferoxamine-loaded topical formulations, a polymeric film and a hydrogel, based on Opuntia ficus-indica mucilage, aimed at wound-dressing applications.
Deferoxamine-loaded topical film and hydrogel were developed from Opuntia ficus-indica mucilage for wound-dressing use
The work developed and characterized two deferoxamine-loaded topical formulations, a polymeric film and a hydrogel, based on Opuntia ficus-indica mucilage, aimed at wound-dressing applications.
The work developed and characterized two deferoxamine-loaded topical formulations, a polymeric film and a hydrogel, based on Opuntia ficus-indica mucilage, aimed at wound-dressing applications.
DOAJ (DOAJ: Directory of Open Access Journals) Oct 1, 2026
Using PC1D numerical modelling, the work examines how layer thickness, doping concentration, and operating temperature affect InGaN solar cell performance, adding a simulation-based contribution to the existing line of InGaN photovoltaic optimization studies.
Using PC1D numerical modelling, the work examines how layer thickness, doping concentration, and operating temperature affect InGaN solar cell performance, adding a simulation-based contribution to the existing line of InGaN photovoltaic optimization studies.
PC1D modelling indicates that layer thickness, doping concentration, and operating temperature jointly shape InGaN solar cell efficiency
Using PC1D numerical modelling, the work examines how layer thickness, doping concentration, and operating temperature affect InGaN solar cell performance, adding a simulation-based contribution to the existing line of InGaN photovoltaic optimization studies.
Using PC1D numerical modelling, the work examines how layer thickness, doping concentration, and operating temperature affect InGaN solar cell performance, adding a simulation-based contribution to the existing line of InGaN photovoltaic optimization studies.
Nature News Sep 30, 2026
Mineral physicist Alisha Clark used the Z machine at Sandia National Laboratories to send shockwaves through water-infused glass samples, recreating pressures near Earth's core during its formation; earlier experiments showed the wet glass became easier to compress as pressure rose, leading her to propose that molten rock could have locked water inside Earth during its magma-ocean phase rather than losing it all or receiving it later from comets and asteroids.
Mineral physicist Alisha Clark used the Z machine at Sandia National Laboratories to send shockwaves through water-infused glass samples, recreating pressures near Earth's core during its formation; earlier experiments showed the wet glass became easier to compress as pressure rose, leading her to propose that molten rock could have locked water inside Earth during its magma-ocean phase rather than losing it all or receiving it later from comets and asteroids.
Clark fired the Z machine at water-infused glass and found melt may grow easier to compress under deep pressure, offering a clue to how early Earth kept its water
Mineral physicist Alisha Clark used the Z machine at Sandia National Laboratories to send shockwaves through water-infused glass samples, recreating pressures near Earth's core during its formation; earlier experiments showed the wet glass became easier to compress as pressure rose, leading her to propose that molten rock could have locked water inside Earth during its magma-ocean phase rather than losing it all or receiving it later from comets and asteroids.
Mineral physicist Alisha Clark used the Z machine at Sandia National Laboratories to send shockwaves through water-infused glass samples, recreating pressures near Earth's core during its formation; earlier experiments showed the wet glass became easier to compress as pressure rose, leading her to propose that molten rock could have locked water inside Earth during its magma-ocean phase rather than losing it all or receiving it later from comets and asteroids.
CSIAM Transactions on Applied Mathematics Sep 29, 2026
The authors propose a Derivative-informed Graph Convolutional Autoencoder (DiGCA) phase classifier that feeds both the Lifshitz-Petrich model solutions and their derivatives (the nonlocal term G(φ)) into a graph convolutional autoencoder for dimensionality reduction, then classifies with a fully connected neural network, generating phase diagrams over the parameter domain [−0.01,0.05]×[0,1] with over 98% classification accuracy, roughly two orders of magnitude faster than MCMS-RBM, and remaining stable under up to 10% additive white noise.
The authors propose a Derivative-informed Graph Convolutional Autoencoder (DiGCA) phase classifier that feeds both the Lifshitz-Petrich model solutions and their derivatives (the nonlocal term G(φ)) into a graph convolutional autoencoder for dimensionality reduction, then classifies with a fully connected neural network, generating phase diagrams over the parameter domain [−0.01,0.05]×[0,1] with over 98% classification accuracy, roughly two orders of magnitude faster than MCMS-RBM, and remaining stable under up to 10% additive white noise.
Chen, Ji and Xu propose the DiGCA phase classifier, generating Lifshitz-Petrich phase diagrams about two orders of magnitude faster with over 98% classification accuracy
The authors propose a Derivative-informed Graph Convolutional Autoencoder (DiGCA) phase classifier that feeds both the Lifshitz-Petrich model solutions and their derivatives (the nonlocal term G(φ)) into a graph convolutional autoencoder for dimensionality reduction, then classifies with a fully connected neural network, generating phase diagrams over the parameter domain [−0.01,0.05]×[0,1] with over 98% classification accuracy, roughly two orders of magnitude faster than MCMS-RBM, and remaining stable under up to 10% additive white noise.
The authors propose a Derivative-informed Graph Convolutional Autoencoder (DiGCA) phase classifier that feeds both the Lifshitz-Petrich model solutions and their derivatives (the nonlocal term G(φ)) into a graph convolutional autoencoder for dimensionality reduction, then classifies with a fully connected neural network, generating phase diagrams over the parameter domain [−0.01,0.05]×[0,1] with over 98% classification accuracy, roughly two orders of magnitude faster than MCMS-RBM, and remaining stable under up to 10% additive white noise.
Frontiers in Medicine Sep 28, 2026
Using data reconstructed from Wang et al.'s supplementary tables, this study built 161 loading-capacity observations with 110 descriptors and 444 cell-viability observations with 25 descriptors, optimized histogram-based gradient boosting regression (HGBR) and partial least squares regression (PLSR) with differential evolution under five-fold grouped cross-validation, and constrained exact duplicate records to the same partition to prevent information leakage; on the duplicate-safe 20% holdout HGBR was strongest for cell viability (R² = 0.774, RMSE = 11.655 percentage points, MAE = 8.153, AARD = 16.69%) while PLSR was best for loading capacity (R² = 0.557, RMSE = 0.345 g/g, MAE = 0.210 g/g), yet holding out entire source publications drove all R² values negative (viability −0.391 and −0.
Using data reconstructed from Wang et al.'s supplementary tables, this study built 161 loading-capacity observations with 110 descriptors and 444 cell-viability observations with 25 descriptors, optimized histogram-based gradient boosting regression (HGBR) and partial least squares regression (PLSR) with differential evolution under five-fold grouped cross-validation, and constrained exact duplicate records to the same partition to prevent information leakage; on the duplicate-safe 20% holdout HGBR was strongest for cell viability (R² = 0.774, RMSE = 11.655 percentage points, MAE = 8.153, AARD = 16.69%) while PLSR was best for loading capacity (R² = 0.557, RMSE = 0.345 g/g, MAE = 0.210 g/g), yet holding out entire source publications drove all R² values negative (viability −0.391 and −0.
Leakage-aware ML predicts MOF drug loading and cell viability at in-domain R² of 0.557 and 0.774, but R² turns negative when whole publications are held out
Using data reconstructed from Wang et al.'s supplementary tables, this study built 161 loading-capacity observations with 110 descriptors and 444 cell-viability observations with 25 descriptors, optimized histogram-based gradient boosting regression (HGBR) and partial least squares regression (PLSR) with differential evolution under five-fold grouped cross-validation, and constrained exact duplicate records to the same partition to prevent information leakage; on the duplicate-safe 20% holdout HGBR was strongest for cell viability (R² = 0.774, RMSE = 11.655 percentage points, MAE = 8.153, AARD = 16.69%) while PLSR was best for loading capacity (R² = 0.557, RMSE = 0.345 g/g, MAE = 0.210 g/g), yet holding out entire source publications drove all R² values negative (viability −0.391 and −0.
Using data reconstructed from Wang et al.'s supplementary tables, this study built 161 loading-capacity observations with 110 descriptors and 444 cell-viability observations with 25 descriptors, optimized histogram-based gradient boosting regression (HGBR) and partial least squares regression (PLSR) with differential evolution under five-fold grouped cross-validation, and constrained exact duplicate records to the same partition to prevent information leakage; on the duplicate-safe 20% holdout HGBR was strongest for cell viability (R² = 0.774, RMSE = 11.655 percentage points, MAE = 8.153, AARD = 16.69%) while PLSR was best for loading capacity (R² = 0.557, RMSE = 0.345 g/g, MAE = 0.210 g/g), yet holding out entire source publications drove all R² values negative (viability −0.391 and −0.
ChemPhysChem Sep 28, 2026
Using tensiometry, dynamic light scattering, small-angle neutron scattering, and steady-state and time-resolved fluorescence at 30 °C, this study examined four Gemini surfactants (8-4-8, 10-4-10, 12-4-12, 10-8-10) at 100 mM with and without 100 mM NaBr or NaSal, and found that longer alkyl chains lower the CMC (about 55.4 mM to about 1.2 mM) and enlarge micelles with higher aggregation numbers (13 to 39), that increasing the spacer from 4 to 8 carbons shrinks micelles (D_h about 2.6 nm to about 1.1 nm), that NaBr swells micelles by electrostatic screening (D_h about 4.8 nm) while NaSal elongates them markedly (D_h about 15.
Using tensiometry, dynamic light scattering, small-angle neutron scattering, and steady-state and time-resolved fluorescence at 30 °C, this study examined four Gemini surfactants (8-4-8, 10-4-10, 12-4-12, 10-8-10) at 100 mM with and without 100 mM NaBr or NaSal, and found that longer alkyl chains lower the CMC (about 55.4 mM to about 1.2 mM) and enlarge micelles with higher aggregation numbers (13 to 39), that increasing the spacer from 4 to 8 carbons shrinks micelles (D_h about 2.6 nm to about 1.1 nm), that NaBr swells micelles by electrostatic screening (D_h about 4.8 nm) while NaSal elongates them markedly (D_h about 15.
Gemini surfactant chain length and spacer tune micelle structure, while NaSal grows micelles from 2.6 nm to 15.8 nm and lengthens MC540 fluorescence lifetime
Using tensiometry, dynamic light scattering, small-angle neutron scattering, and steady-state and time-resolved fluorescence at 30 °C, this study examined four Gemini surfactants (8-4-8, 10-4-10, 12-4-12, 10-8-10) at 100 mM with and without 100 mM NaBr or NaSal, and found that longer alkyl chains lower the CMC (about 55.4 mM to about 1.2 mM) and enlarge micelles with higher aggregation numbers (13 to 39), that increasing the spacer from 4 to 8 carbons shrinks micelles (D_h about 2.6 nm to about 1.1 nm), that NaBr swells micelles by electrostatic screening (D_h about 4.8 nm) while NaSal elongates them markedly (D_h about 15.
Using tensiometry, dynamic light scattering, small-angle neutron scattering, and steady-state and time-resolved fluorescence at 30 °C, this study examined four Gemini surfactants (8-4-8, 10-4-10, 12-4-12, 10-8-10) at 100 mM with and without 100 mM NaBr or NaSal, and found that longer alkyl chains lower the CMC (about 55.4 mM to about 1.2 mM) and enlarge micelles with higher aggregation numbers (13 to 39), that increasing the spacer from 4 to 8 carbons shrinks micelles (D_h about 2.6 nm to about 1.1 nm), that NaBr swells micelles by electrostatic screening (D_h about 4.8 nm) while NaSal elongates them markedly (D_h about 15.
International Journal of Corrosion and Scale Inhibition Sep 28, 2026
In a question-and-answer dialogue with ChatGPT, the authors compare normative definitions from GOST 9.106–2021, NACE/ASTM G193-22 and ISO 8044:2024, point to the ISO requirement that an inhibitor be "present in the corrosion system" and to the absence of a distinction between the bulk environment and the near-surface region, and then define an inhibitor through its action on the metal or the corrosion system, offering working definitions of a chemisorbed layer, a conversion coating and a bulk-phase anticorrosion coating, and arriving at a refined definition that excludes substances whose action significantly changes the concentration of corrosive components in the bulk environment, forms a conversion coating, or involves applying a bulk-phase anticorrosion coating.
In a question-and-answer dialogue with ChatGPT, the authors compare normative definitions from GOST 9.106–2021, NACE/ASTM G193-22 and ISO 8044:2024, point to the ISO requirement that an inhibitor be "present in the corrosion system" and to the absence of a distinction between the bulk environment and the near-surface region, and then define an inhibitor through its action on the metal or the corrosion system, offering working definitions of a chemisorbed layer, a conversion coating and a bulk-phase anticorrosion coating, and arriving at a refined definition that excludes substances whose action significantly changes the concentration of corrosive components in the bulk environment, forms a conversion coating, or involves applying a bulk-phase anticorrosion coating.
Andreev and Vesely, in dialogue with ChatGPT, propose a refined definition of "corrosion inhibitor" that covers the protection after-effect of pretreatment and separates inhibition from conversion treatment and bulk-phase coatings
In a question-and-answer dialogue with ChatGPT, the authors compare normative definitions from GOST 9.106–2021, NACE/ASTM G193-22 and ISO 8044:2024, point to the ISO requirement that an inhibitor be "present in the corrosion system" and to the absence of a distinction between the bulk environment and the near-surface region, and then define an inhibitor through its action on the metal or the corrosion system, offering working definitions of a chemisorbed layer, a conversion coating and a bulk-phase anticorrosion coating, and arriving at a refined definition that excludes substances whose action significantly changes the concentration of corrosive components in the bulk environment, forms a conversion coating, or involves applying a bulk-phase anticorrosion coating.
In a question-and-answer dialogue with ChatGPT, the authors compare normative definitions from GOST 9.106–2021, NACE/ASTM G193-22 and ISO 8044:2024, point to the ISO requirement that an inhibitor be "present in the corrosion system" and to the absence of a distinction between the bulk environment and the near-surface region, and then define an inhibitor through its action on the metal or the corrosion system, offering working definitions of a chemisorbed layer, a conversion coating and a bulk-phase anticorrosion coating, and arriving at a refined definition that excludes substances whose action significantly changes the concentration of corrosive components in the bulk environment, forms a conversion coating, or involves applying a bulk-phase anticorrosion coating.
Journal of Advanced Ceramics Sep 28, 2026
This work uses a random forest model to classify the phase composition of multi-rare-earth-principal-component RE2Si2O7 disilicates into single-beta, single-gamma, single-delta/mixed delta+gamma, and separate phase, identifies the average RE3+ cationic radius and the deviation of RE3+ cationic radius as the most influential factors, validates the model by predicting the phase compositions of (Gdx1Hox2Ybx3Lux4)2Si2O7 and (Ndx1Hox2Ybx3Lux4)2Si2O7 systems with experimental characterization of representative compositions, links phase formation through high-throughput DFT calculations to low energy costs for accommodating configurational randomness and rapid convergence of the configurational entropy of mixing with increased excitation energy, and establishes quantitative design criteria for si
This work uses a random forest model to classify the phase composition of multi-rare-earth-principal-component RE2Si2O7 disilicates into single-beta, single-gamma, single-delta/mixed delta+gamma, and separate phase, identifies the average RE3+ cationic radius and the deviation of RE3+ cationic radius as the most influential factors, validates the model by predicting the phase compositions of (Gdx1Hox2Ybx3Lux4)2Si2O7 and (Ndx1Hox2Ybx3Lux4)2Si2O7 systems with experimental characterization of representative compositions, links phase formation through high-throughput DFT calculations to low energy costs for accommodating configurational randomness and rapid convergence of the configurational entropy of mixing with increased excitation energy, and establishes quantitative design criteria for si
Random forest classifies multi-rare-earth disilicate phase compositions and yields mean-and-deviation radius criteria for single-phase beta/gamma design
This work uses a random forest model to classify the phase composition of multi-rare-earth-principal-component RE2Si2O7 disilicates into single-beta, single-gamma, single-delta/mixed delta+gamma, and separate phase, identifies the average RE3+ cationic radius and the deviation of RE3+ cationic radius as the most influential factors, validates the model by predicting the phase compositions of (Gdx1Hox2Ybx3Lux4)2Si2O7 and (Ndx1Hox2Ybx3Lux4)2Si2O7 systems with experimental characterization of representative compositions, links phase formation through high-throughput DFT calculations to low energy costs for accommodating configurational randomness and rapid convergence of the configurational entropy of mixing with increased excitation energy, and establishes quantitative design criteria for si
This work uses a random forest model to classify the phase composition of multi-rare-earth-principal-component RE2Si2O7 disilicates into single-beta, single-gamma, single-delta/mixed delta+gamma, and separate phase, identifies the average RE3+ cationic radius and the deviation of RE3+ cationic radius as the most influential factors, validates the model by predicting the phase compositions of (Gdx1Hox2Ybx3Lux4)2Si2O7 and (Ndx1Hox2Ybx3Lux4)2Si2O7 systems with experimental characterization of representative compositions, links phase formation through high-throughput DFT calculations to low energy costs for accommodating configurational randomness and rapid convergence of the configurational entropy of mixing with increased excitation energy, and establishes quantitative design criteria for si
International Journal of Corrosion and Scale Inhibition Sep 28, 2026
Using SMILES strings of 317 organic corrosion inhibitor compounds, this study computed and retained all 208 two-dimensional molecular descriptors via RDKit, expanded the training set with mixup-interpolated virtual samples, compared Random Forest, Bagging and Gradient Boosting, and interpreted the models with SHAP, correlation analysis, partial dependence plots and a Williams plot; Random Forest performed best on the original data (test MAE 4.568, RMSE 6.122, R² 0.718), R² for all three models rose from roughly 0.66–0.70 to above 0.90 and then saturated as virtual samples grew from 100 to 10,000, and SHAP identified topological descriptors Chi4n, Chi3v, Chi1n, Chi4v plus MolMR as the most influential features.
Using SMILES strings of 317 organic corrosion inhibitor compounds, this study computed and retained all 208 two-dimensional molecular descriptors via RDKit, expanded the training set with mixup-interpolated virtual samples, compared Random Forest, Bagging and Gradient Boosting, and interpreted the models with SHAP, correlation analysis, partial dependence plots and a Williams plot; Random Forest performed best on the original data (test MAE 4.568, RMSE 6.122, R² 0.718), R² for all three models rose from roughly 0.66–0.70 to above 0.90 and then saturated as virtual samples grew from 100 to 10,000, and SHAP identified topological descriptors Chi4n, Chi3v, Chi1n, Chi4v plus MolMR as the most influential features.
Keeping all 208 molecular descriptors and adding mixup virtual samples, random forest reaches R² 0.72 for corrosion inhibition efficiency, with SHAP pointing to Chi4n and other topological descriptors
Using SMILES strings of 317 organic corrosion inhibitor compounds, this study computed and retained all 208 two-dimensional molecular descriptors via RDKit, expanded the training set with mixup-interpolated virtual samples, compared Random Forest, Bagging and Gradient Boosting, and interpreted the models with SHAP, correlation analysis, partial dependence plots and a Williams plot; Random Forest performed best on the original data (test MAE 4.568, RMSE 6.122, R² 0.718), R² for all three models rose from roughly 0.66–0.70 to above 0.90 and then saturated as virtual samples grew from 100 to 10,000, and SHAP identified topological descriptors Chi4n, Chi3v, Chi1n, Chi4v plus MolMR as the most influential features.
Using SMILES strings of 317 organic corrosion inhibitor compounds, this study computed and retained all 208 two-dimensional molecular descriptors via RDKit, expanded the training set with mixup-interpolated virtual samples, compared Random Forest, Bagging and Gradient Boosting, and interpreted the models with SHAP, correlation analysis, partial dependence plots and a Williams plot; Random Forest performed best on the original data (test MAE 4.568, RMSE 6.122, R² 0.718), R² for all three models rose from roughly 0.66–0.70 to above 0.90 and then saturated as virtual samples grew from 100 to 10,000, and SHAP identified topological descriptors Chi4n, Chi3v, Chi1n, Chi4v plus MolMR as the most influential features.
arXiv Sep 27, 2026
The work builds a computational library of 146 single-walled carbon nanotube (SWCNT)–SWCNT junctions and analyses their magnetotransport with an automated workflow combining molecular dynamics, tight-binding theory, Peierls magnetic coupling, and non-equilibrium Green's functions, followed by machine-learning analysis; it finds that the averaged first transmission-step value is governed primarily by the mean chiral angle of the two nanotubes, that the junction energy gap depends predominantly on the metallic or semiconducting character of the constituent nanotubes, that temperature generally suppresses the averaged transmission while reducing the extracted gap, that a perpendicular magnetic field affects transmission much more strongly than the gap, that signatures of interference-driven t
The work builds a computational library of 146 single-walled carbon nanotube (SWCNT)–SWCNT junctions and analyses their magnetotransport with an automated workflow combining molecular dynamics, tight-binding theory, Peierls magnetic coupling, and non-equilibrium Green's functions, followed by machine-learning analysis; it finds that the averaged first transmission-step value is governed primarily by the mean chiral angle of the two nanotubes, that the junction energy gap depends predominantly on the metallic or semiconducting character of the constituent nanotubes, that temperature generally suppresses the averaged transmission while reducing the extracted gap, that a perpendicular magnetic field affects transmission much more strongly than the gap, that signatures of interference-driven t
A 146-junction single-walled carbon nanotube library shows mean chiral angle sets the averaged transmission step while metallic or semiconducting character sets the junction gap
The work builds a computational library of 146 single-walled carbon nanotube (SWCNT)–SWCNT junctions and analyses their magnetotransport with an automated workflow combining molecular dynamics, tight-binding theory, Peierls magnetic coupling, and non-equilibrium Green's functions, followed by machine-learning analysis; it finds that the averaged first transmission-step value is governed primarily by the mean chiral angle of the two nanotubes, that the junction energy gap depends predominantly on the metallic or semiconducting character of the constituent nanotubes, that temperature generally suppresses the averaged transmission while reducing the extracted gap, that a perpendicular magnetic field affects transmission much more strongly than the gap, that signatures of interference-driven t
The work builds a computational library of 146 single-walled carbon nanotube (SWCNT)–SWCNT junctions and analyses their magnetotransport with an automated workflow combining molecular dynamics, tight-binding theory, Peierls magnetic coupling, and non-equilibrium Green's functions, followed by machine-learning analysis; it finds that the averaged first transmission-step value is governed primarily by the mean chiral angle of the two nanotubes, that the junction energy gap depends predominantly on the metallic or semiconducting character of the constituent nanotubes, that temperature generally suppresses the averaged transmission while reducing the extracted gap, that a perpendicular magnetic field affects transmission much more strongly than the gap, that signatures of interference-driven t

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