Pub Date : 2026-03-10DOI: 10.1021/acs.jpcc.5c08611
Eric Johnsson,Shrinjay Sharma,Arvind Gangoli Rao,David Dubbeldam,Sofia Calero,Thijs J. H. Vlugt
Hydroisomerization of alkane isomers is an important step in the manufacture of current kerosene and sustainable aviation fuels. Zeolites are used as acid catalysts in this process. It is therefore important to have predictions of the adsorption capacity or maximum loading of hydrocarbons in zeolites. Here, a cascade model using machine learning models is used to predict the maximum loading of alkane isomers in zeolites. The cascade is composed of a gradient-boosted tree classifier stage that predicts whether adsorption occurs and a regressor predicting the value of the maximum loading. The final data set consists of 45 different adsorbates (both linear and branched alkanes up to C16) and 97 different zeolite structures, resulting in 4365 data points. Descriptors include information on the geometry and topology of zeolite channels as well as the shape and size of the adsorbates. Extra composite descriptors are also present to provide the physical basis for predictions. Multiple regressors of different natures are considered: support vector regressors, gradient-boosted trees, extreme gradient-boosted trees, and the TabPFN pretrained model. TabPFN yields the highest generalization performance and the lowest error. An interpretability analysis using SHAP reveals that the most influential descriptors are physically meaningful, highlighting steric and volumetric constraints as the primary factors controlling the prediction of qmax. It is shown that despite both the classifier and the regressor being insensitive to random splits in data, the regressor is prone to overfitting at low fractions of data withheld for testing. The cascade model is compared to an Artificial Neural Network for training and resource efficiency. Despite training being longer for the neural network, the final model is lighter in both memory and storage. This work is built on our previous research in predicting the Henry coefficients of long-chain alkanes in zeolites. Using this previous model and the findings of this work, one could construct the adsorption isotherm for any alkane, thus enabling the analysis of adsorption behavior of alkane mixtures using IAST.
{"title":"Predicting the Maximum Loading in Zeolites for Hydroisomerization Applications: A Machine Learning Approach","authors":"Eric Johnsson,Shrinjay Sharma,Arvind Gangoli Rao,David Dubbeldam,Sofia Calero,Thijs J. H. Vlugt","doi":"10.1021/acs.jpcc.5c08611","DOIUrl":"https://doi.org/10.1021/acs.jpcc.5c08611","url":null,"abstract":"Hydroisomerization of alkane isomers is an important step in the manufacture of current kerosene and sustainable aviation fuels. Zeolites are used as acid catalysts in this process. It is therefore important to have predictions of the adsorption capacity or maximum loading of hydrocarbons in zeolites. Here, a cascade model using machine learning models is used to predict the maximum loading of alkane isomers in zeolites. The cascade is composed of a gradient-boosted tree classifier stage that predicts whether adsorption occurs and a regressor predicting the value of the maximum loading. The final data set consists of 45 different adsorbates (both linear and branched alkanes up to C16) and 97 different zeolite structures, resulting in 4365 data points. Descriptors include information on the geometry and topology of zeolite channels as well as the shape and size of the adsorbates. Extra composite descriptors are also present to provide the physical basis for predictions. Multiple regressors of different natures are considered: support vector regressors, gradient-boosted trees, extreme gradient-boosted trees, and the TabPFN pretrained model. TabPFN yields the highest generalization performance and the lowest error. An interpretability analysis using SHAP reveals that the most influential descriptors are physically meaningful, highlighting steric and volumetric constraints as the primary factors controlling the prediction of qmax. It is shown that despite both the classifier and the regressor being insensitive to random splits in data, the regressor is prone to overfitting at low fractions of data withheld for testing. The cascade model is compared to an Artificial Neural Network for training and resource efficiency. Despite training being longer for the neural network, the final model is lighter in both memory and storage. This work is built on our previous research in predicting the Henry coefficients of long-chain alkanes in zeolites. Using this previous model and the findings of this work, one could construct the adsorption isotherm for any alkane, thus enabling the analysis of adsorption behavior of alkane mixtures using IAST.","PeriodicalId":61,"journal":{"name":"The Journal of Physical Chemistry C","volume":"79 1","pages":""},"PeriodicalIF":4.126,"publicationDate":"2026-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147383837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The high economic value of extra virgin olive oil (EVOO) makes it susceptible to adulteration with lower-cost oils, necessitating rapid and reliable analytical methods for authenticity assessment. In this work, a Raman spectroscopic approach combined with a multiscale Fusion Convolutional Residual Attention Network (MSF-CRAN) is developed for the identification and quantification of EVOO adulteration. Model interpretability is enhanced through the integration of SHapley Additive Explanations (SHAP), enabling the analysis of spectral feature contributions. To address limited sample availability in multicomponent adulteration systems, binary, ternary, and higher-order blended samples were prepared, and data set expansion was achieved using spectral feature transfer and generative adversarial networks (GANs). The proposed MSF-CRAN model demonstrates excellent performance, achieving 100% classification accuracy for adulteration identification and high quantitative accuracy for ternary mixtures with an R2 of 0.991 and a mean absolute error (MAE) of 0.0181. The results highlight the robustness and generalization capability of the proposed framework for the Raman-based analysis of complex multicomponent systems.
{"title":"Interpretable Deep Learning Framework Enables Authentication and Quantification of Olive Oil Adulteration by Raman Spectroscopy","authors":"Xue-Yang Xiong,Huan Chen,Jia-Sheng Lin,Fan-Li Zhang","doi":"10.1021/acs.jpcc.6c00601","DOIUrl":"https://doi.org/10.1021/acs.jpcc.6c00601","url":null,"abstract":"The high economic value of extra virgin olive oil (EVOO) makes it susceptible to adulteration with lower-cost oils, necessitating rapid and reliable analytical methods for authenticity assessment. In this work, a Raman spectroscopic approach combined with a multiscale Fusion Convolutional Residual Attention Network (MSF-CRAN) is developed for the identification and quantification of EVOO adulteration. Model interpretability is enhanced through the integration of SHapley Additive Explanations (SHAP), enabling the analysis of spectral feature contributions. To address limited sample availability in multicomponent adulteration systems, binary, ternary, and higher-order blended samples were prepared, and data set expansion was achieved using spectral feature transfer and generative adversarial networks (GANs). The proposed MSF-CRAN model demonstrates excellent performance, achieving 100% classification accuracy for adulteration identification and high quantitative accuracy for ternary mixtures with an R2 of 0.991 and a mean absolute error (MAE) of 0.0181. The results highlight the robustness and generalization capability of the proposed framework for the Raman-based analysis of complex multicomponent systems.","PeriodicalId":61,"journal":{"name":"The Journal of Physical Chemistry C","volume":"39 1","pages":""},"PeriodicalIF":4.126,"publicationDate":"2026-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147383836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-10DOI: 10.1021/acs.jpcc.5c08190
Denis V. Zhuravlev,Sergei A. Vasilkov,Ilia A. Elagin,Vladimir A. Chirkov,Oleg V. Levin
The reliability of extracting the electrical conductivity of electroactive polymer films from cyclic voltammetry (CV) on interdigitated electrodes (IDEs) is assessed using a data-driven, physics-based modeling framework. Pseudo-two-dimensional simulations replicate the IDE geometry and timing of the CV protocols. A porous-layer model that couples Butler–Volmer interfacial kinetics, radial diffusion of dopant anions within polymer globules, and lateral current flow in the IDE reproduces the salient voltammetric features. However, at typical scan rates, conductivity inferred from IDE current differences departs from the equilibrium value: pronounced hysteresis emerges, twin peaks develop, and occasional unphysical negative values appear. Analysis attributes the discrepancies to overpotential, spatially nonuniform doping across the film thickness, and uncompensated transient faradaic contributions to the current difference. Two accuracy-oriented protocols are evaluated: slowing the scan toward a quasi-equilibrium regime and employing staircase voltammetry with step holds until current stabilization. Both approaches recover conductivities consistent with the equilibrium relation and substantially reduce hysteresis. The workflow provides actionable guidance for obtaining reliable conductivity parameters for modeling and designing potentioresistive protective layers in lithium-ion cells and is transferable to other electrochemical systems that require an accurate description of conducting polymers.
{"title":"Limitations of Cyclic Voltammetry on Interdigitated Electrodes for Evaluating Polymer Conductivity","authors":"Denis V. Zhuravlev,Sergei A. Vasilkov,Ilia A. Elagin,Vladimir A. Chirkov,Oleg V. Levin","doi":"10.1021/acs.jpcc.5c08190","DOIUrl":"https://doi.org/10.1021/acs.jpcc.5c08190","url":null,"abstract":"The reliability of extracting the electrical conductivity of electroactive polymer films from cyclic voltammetry (CV) on interdigitated electrodes (IDEs) is assessed using a data-driven, physics-based modeling framework. Pseudo-two-dimensional simulations replicate the IDE geometry and timing of the CV protocols. A porous-layer model that couples Butler–Volmer interfacial kinetics, radial diffusion of dopant anions within polymer globules, and lateral current flow in the IDE reproduces the salient voltammetric features. However, at typical scan rates, conductivity inferred from IDE current differences departs from the equilibrium value: pronounced hysteresis emerges, twin peaks develop, and occasional unphysical negative values appear. Analysis attributes the discrepancies to overpotential, spatially nonuniform doping across the film thickness, and uncompensated transient faradaic contributions to the current difference. Two accuracy-oriented protocols are evaluated: slowing the scan toward a quasi-equilibrium regime and employing staircase voltammetry with step holds until current stabilization. Both approaches recover conductivities consistent with the equilibrium relation and substantially reduce hysteresis. The workflow provides actionable guidance for obtaining reliable conductivity parameters for modeling and designing potentioresistive protective layers in lithium-ion cells and is transferable to other electrochemical systems that require an accurate description of conducting polymers.","PeriodicalId":61,"journal":{"name":"The Journal of Physical Chemistry C","volume":"199 1","pages":""},"PeriodicalIF":4.126,"publicationDate":"2026-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147383832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Multilayered synthetic antiferromagnets (SAFs) are artificial three-dimensional (3D) architectures engineered to create novel, complex, and stable spin textures. Noninvasive and quantitative nanoscale magnetic imaging of the two-dimensional stray field profile at the sample surface is essential for understanding the fundamental properties of the spin-structure and being able to tailor them to achieve new functionalities. However, the deterministic detection of spin textures and their quantitative characterization at the nanoscale remain challenging. Here, we use nitrogen-vacancy scanning probe microscopy (NV-SPM) under ambient conditions to perform the first quantitative vector-field magnetometry measurements in the multilayered SAF [(Co/Pt)5/Co/Ru]3/(Co/Pt)6. We investigate the static and dynamic nanoscale properties of antiferromagnetic domains with boundaries hosting “one-dimensional” ferromagnetic stripes with ∼100 nm of width and periodic modulation of the magnetization. By employing NV-SPM measurements in different imaging modes and involving NV-probes with various crystallographic orientations, we demonstrate distinct fingerprints emerging from GHz-range spin noise and constant stray fields on the order of several mT. This provides quantitative insights into the structure of domains and domain walls, as well as, into magnetic noise associated with thermal spin-waves. Our work opens up new opportunities for quantitative vector-field magnetometry of modern magnetic materials with tailored 3D spin textures and stray field profiles, and potentially novel spin-wave dispersions─in a quantitative and noninvasive manner, with exceptional magnetic sensitivity and nanometer scale spatial resolution.
{"title":"Nanoscale NV-Center Magnetometry of a Synthetic Three-Dimensional Spin Texture","authors":"Ricardo Javier Peña Román,Sandip Maity,Fabian Samad,Dinesh Pinto,Simon Josephy,Andrea Morales,Attila Kákay,Klaus Kern,Olav Hellwig,Aparajita Singha","doi":"10.1021/acs.jpcc.6c00518","DOIUrl":"https://doi.org/10.1021/acs.jpcc.6c00518","url":null,"abstract":"Multilayered synthetic antiferromagnets (SAFs) are artificial three-dimensional (3D) architectures engineered to create novel, complex, and stable spin textures. Noninvasive and quantitative nanoscale magnetic imaging of the two-dimensional stray field profile at the sample surface is essential for understanding the fundamental properties of the spin-structure and being able to tailor them to achieve new functionalities. However, the deterministic detection of spin textures and their quantitative characterization at the nanoscale remain challenging. Here, we use nitrogen-vacancy scanning probe microscopy (NV-SPM) under ambient conditions to perform the first quantitative vector-field magnetometry measurements in the multilayered SAF [(Co/Pt)5/Co/Ru]3/(Co/Pt)6. We investigate the static and dynamic nanoscale properties of antiferromagnetic domains with boundaries hosting “one-dimensional” ferromagnetic stripes with ∼100 nm of width and periodic modulation of the magnetization. By employing NV-SPM measurements in different imaging modes and involving NV-probes with various crystallographic orientations, we demonstrate distinct fingerprints emerging from GHz-range spin noise and constant stray fields on the order of several mT. This provides quantitative insights into the structure of domains and domain walls, as well as, into magnetic noise associated with thermal spin-waves. Our work opens up new opportunities for quantitative vector-field magnetometry of modern magnetic materials with tailored 3D spin textures and stray field profiles, and potentially novel spin-wave dispersions─in a quantitative and noninvasive manner, with exceptional magnetic sensitivity and nanometer scale spatial resolution.","PeriodicalId":61,"journal":{"name":"The Journal of Physical Chemistry C","volume":"52 1","pages":""},"PeriodicalIF":4.126,"publicationDate":"2026-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147383768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Crystalline porous materials, such as covalent organic frameworks (COFs), have emerged as promising candidates for photocatalytic and optoelectronic applications due to their tunable architecture and capacity to mitigate charge recombination. The incorporation of highly aromatic organic building blocks that promote self-assembly and columnar growth enables the formation of COFs with a controlled layer thickness. However, the influence of interlayer stacking on the structural and optoelectronic behaviors of these materials remains poorly understood. In this work, we combine experimental and theoretical approaches to elucidate the stacking-induced evolution of perylene–Zn–porphyrin COFs. Spectroscopic and microscopic analyses, supported by density functional theory (DFT) calculations, reveal that self-assembly through AA stacking markedly modifies both the geometry and electronic structure. The transition from nonplanar 2D architectures to planar multilayered frameworks results in reduced band gaps, inversion of the frontier crystalline orbital delocalization, and a shift of absorption dominance toward the porphyrin units. These findings demonstrate that controlled layer stacking is a viable strategy to tailor the electronic and optical properties of stacked 2D COFs, paving the way for their integration into high-performance optoelectronic devices.
{"title":"Stacking Effects on the Optoelectronic Properties of 2D Perylene-Zn-Porphyrin-Based COFs","authors":"Valentin Diez-Cabanes,Sergio de-la-Huerta-Sainz,Elisabeth Escamilla,Pedro A. Marcos,Alfredo Bol-Arreba,Kathryn McCarthy,Roberto González-Gómez,Santiago Aparicio,Pau Farràs","doi":"10.1021/acs.jpcc.5c08341","DOIUrl":"https://doi.org/10.1021/acs.jpcc.5c08341","url":null,"abstract":"Crystalline porous materials, such as covalent organic frameworks (COFs), have emerged as promising candidates for photocatalytic and optoelectronic applications due to their tunable architecture and capacity to mitigate charge recombination. The incorporation of highly aromatic organic building blocks that promote self-assembly and columnar growth enables the formation of COFs with a controlled layer thickness. However, the influence of interlayer stacking on the structural and optoelectronic behaviors of these materials remains poorly understood. In this work, we combine experimental and theoretical approaches to elucidate the stacking-induced evolution of perylene–Zn–porphyrin COFs. Spectroscopic and microscopic analyses, supported by density functional theory (DFT) calculations, reveal that self-assembly through AA stacking markedly modifies both the geometry and electronic structure. The transition from nonplanar 2D architectures to planar multilayered frameworks results in reduced band gaps, inversion of the frontier crystalline orbital delocalization, and a shift of absorption dominance toward the porphyrin units. These findings demonstrate that controlled layer stacking is a viable strategy to tailor the electronic and optical properties of stacked 2D COFs, paving the way for their integration into high-performance optoelectronic devices.","PeriodicalId":61,"journal":{"name":"The Journal of Physical Chemistry C","volume":"6 10 1","pages":""},"PeriodicalIF":4.126,"publicationDate":"2026-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147383830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-10DOI: 10.1021/acs.jpcc.5c07217
Shehu Adam Ibrahim,Jinxue Yang,Tan Shi,Chen Zhang,Da Chen,Jing Li,Yang Li,Jeremiah Chinonso Mbazor,Yizhuo Zhang,Zhengxiong Su,Chenyang Lu
Vacancy formation energy governs atomic transport, radiation defect evolution, and phase stability in high-entropy alloys (HEAs). To develop an efficient predictive framework for this critical property, we employ support vector regression (SVR) to model vacancy formation energies in both random solid solution (RSS) and locally chemically ordered (LCO) structures. Three classes of atomic descriptors─neighbor-specific descriptors, average structural metrics, and smooth overlap of atomic positions (SOAP)─were used to capture the complexity of local environments. Among these, SOAP, which capture many-body correlations and provides rotationally and translationally invariant fingerprints, consistently achieved the highest accuracy, with test R2 values of up to ∼0.89 for RSS and ∼0.96 for LCO. The enhanced predictability of LCO-based models results from compositional inhomogeneity, where regions such as Cr-rich clusters strengthen composition-energy correlations that simplify the learning task. While models trained on the more diverse RSS vacancy formation energies generalized better to LCO environments, a mixed training set containing RSS and LCO dataset was shown to maintain high performance on diverse atomic environments. These findings demonstrate that descriptor choice and structural representation are critical for machine learning predictability of defect energetics and provide a framework that can be extended to other defect properties in complex alloys.
{"title":"Machine Learning Prediction of Vacancy Formation Energies in CoNiCrFe High-Entropy Alloy: The Role of Atomic Descriptors and Local Chemical Order","authors":"Shehu Adam Ibrahim,Jinxue Yang,Tan Shi,Chen Zhang,Da Chen,Jing Li,Yang Li,Jeremiah Chinonso Mbazor,Yizhuo Zhang,Zhengxiong Su,Chenyang Lu","doi":"10.1021/acs.jpcc.5c07217","DOIUrl":"https://doi.org/10.1021/acs.jpcc.5c07217","url":null,"abstract":"Vacancy formation energy governs atomic transport, radiation defect evolution, and phase stability in high-entropy alloys (HEAs). To develop an efficient predictive framework for this critical property, we employ support vector regression (SVR) to model vacancy formation energies in both random solid solution (RSS) and locally chemically ordered (LCO) structures. Three classes of atomic descriptors─neighbor-specific descriptors, average structural metrics, and smooth overlap of atomic positions (SOAP)─were used to capture the complexity of local environments. Among these, SOAP, which capture many-body correlations and provides rotationally and translationally invariant fingerprints, consistently achieved the highest accuracy, with test R2 values of up to ∼0.89 for RSS and ∼0.96 for LCO. The enhanced predictability of LCO-based models results from compositional inhomogeneity, where regions such as Cr-rich clusters strengthen composition-energy correlations that simplify the learning task. While models trained on the more diverse RSS vacancy formation energies generalized better to LCO environments, a mixed training set containing RSS and LCO dataset was shown to maintain high performance on diverse atomic environments. These findings demonstrate that descriptor choice and structural representation are critical for machine learning predictability of defect energetics and provide a framework that can be extended to other defect properties in complex alloys.","PeriodicalId":61,"journal":{"name":"The Journal of Physical Chemistry C","volume":"127 1","pages":""},"PeriodicalIF":4.126,"publicationDate":"2026-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147383835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-10DOI: 10.1021/acs.jpcc.6c00028
Tuan M. Duong,Dmitry Aldakov,Wai Li Ling,Le Si Dang,Gilles Nogues,Peter Reiss
Lead halide perovskite nanocrystals, particularly CsPbBr3, are prime candidates for a variety of optical and optoelectronic applications. However, their poor stability, especially under strong irradiation, limits their practical use. In this work, we have developed a synthetic route for CsPbBr3/AlOx core/shell structures with near-unity photoluminescence quantum yield and improved photostability. The shell growth is realized through a water-free sol–gel reaction at room temperature. This approach reduces the risks of particle ripening at higher temperatures and of damaging the core nanocrystals during conventional oxide shell formation, which releases water and alcohol as side products. Moreover, the slow kinetics of the reaction allowed control of the shell thickness down to a monolayer. Finally, a nanopatch antenna structure was fabricated using the core/shell nanocrystals sandwiched between a gold surface and silver nanocubes, which led to a more than 2-fold accelerated carrier dynamics of the perovskite nanocrystals showing a fast photoluminescence decay component of 130 ps. These results contribute to the integration of CsPbBr3/AlOx core/shell nanocrystals into optoelectronic devices requiring a high emission rate, such as single-photon emitters.
{"title":"Bright and Photostable Alumina-Coated Perovskite Nanocrystals for Integration into Quantum Emitters","authors":"Tuan M. Duong,Dmitry Aldakov,Wai Li Ling,Le Si Dang,Gilles Nogues,Peter Reiss","doi":"10.1021/acs.jpcc.6c00028","DOIUrl":"https://doi.org/10.1021/acs.jpcc.6c00028","url":null,"abstract":"Lead halide perovskite nanocrystals, particularly CsPbBr3, are prime candidates for a variety of optical and optoelectronic applications. However, their poor stability, especially under strong irradiation, limits their practical use. In this work, we have developed a synthetic route for CsPbBr3/AlOx core/shell structures with near-unity photoluminescence quantum yield and improved photostability. The shell growth is realized through a water-free sol–gel reaction at room temperature. This approach reduces the risks of particle ripening at higher temperatures and of damaging the core nanocrystals during conventional oxide shell formation, which releases water and alcohol as side products. Moreover, the slow kinetics of the reaction allowed control of the shell thickness down to a monolayer. Finally, a nanopatch antenna structure was fabricated using the core/shell nanocrystals sandwiched between a gold surface and silver nanocubes, which led to a more than 2-fold accelerated carrier dynamics of the perovskite nanocrystals showing a fast photoluminescence decay component of 130 ps. These results contribute to the integration of CsPbBr3/AlOx core/shell nanocrystals into optoelectronic devices requiring a high emission rate, such as single-photon emitters.","PeriodicalId":61,"journal":{"name":"The Journal of Physical Chemistry C","volume":"68 1","pages":""},"PeriodicalIF":4.126,"publicationDate":"2026-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147383838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-10DOI: 10.1021/acs.jpcc.5c07498
Bach Pham,Natasha W. Pettinger,Bern Kohler
The dynamics of charge carriers formed by UV excitation of few nm CeO2 nanoparticles (nanoceria) were studied by femtosecond transient absorption spectroscopy. Transient absorption bands of photogenerated electrons and holes are spectrally well separated greatly aiding the elucidation of their localization and trapping dynamics. Excitation above the optical band gap forms an electron small polaron (ESP) in the bulk of the nanoparticle and a localized hole at the surface in hundreds of fs. Ultrafast charge separation occurs because holes have much greater mobility than electrons in crystalline CeO2. From the mean first passage time for ESPs to diffuse to the particle surface, an activation barrier of 0.15 eV was determined for thermal hopping. While self-trapped excitons are not formed in the bulk of the nanoparticle, they form easily at the defect-rich surface when exciting below the optical band gap. The resulting surface polaron exciton decays nonradiatively with a half-life of 5 ps. This work offers the insight that the effectiveness of nanoceria, and possibly other metal oxides such as TiO2, as a photocatalyst arises from the self-trapping of just one of the carriers in the nanoparticle interior. It also shows that strategies that extend absorption to longer wavelengths by creating surface defects spoil the asymmetry and will likely not be productive for improving photocatalyst performance.
{"title":"Ultrafast Dynamics of Photogenerated Carriers in Cerium Oxide Nanoparticles","authors":"Bach Pham,Natasha W. Pettinger,Bern Kohler","doi":"10.1021/acs.jpcc.5c07498","DOIUrl":"https://doi.org/10.1021/acs.jpcc.5c07498","url":null,"abstract":"The dynamics of charge carriers formed by UV excitation of few nm CeO2 nanoparticles (nanoceria) were studied by femtosecond transient absorption spectroscopy. Transient absorption bands of photogenerated electrons and holes are spectrally well separated greatly aiding the elucidation of their localization and trapping dynamics. Excitation above the optical band gap forms an electron small polaron (ESP) in the bulk of the nanoparticle and a localized hole at the surface in hundreds of fs. Ultrafast charge separation occurs because holes have much greater mobility than electrons in crystalline CeO2. From the mean first passage time for ESPs to diffuse to the particle surface, an activation barrier of 0.15 eV was determined for thermal hopping. While self-trapped excitons are not formed in the bulk of the nanoparticle, they form easily at the defect-rich surface when exciting below the optical band gap. The resulting surface polaron exciton decays nonradiatively with a half-life of 5 ps. This work offers the insight that the effectiveness of nanoceria, and possibly other metal oxides such as TiO2, as a photocatalyst arises from the self-trapping of just one of the carriers in the nanoparticle interior. It also shows that strategies that extend absorption to longer wavelengths by creating surface defects spoil the asymmetry and will likely not be productive for improving photocatalyst performance.","PeriodicalId":61,"journal":{"name":"The Journal of Physical Chemistry C","volume":"264 1","pages":""},"PeriodicalIF":4.126,"publicationDate":"2026-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147383834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-10DOI: 10.1021/acs.jpcc.5c08242
Filip Vuković,Anna Niggas,Levin Mihlan,Zhen Yao,Armin Gölzhäuser,Louise Fréville,Vladislav Stroganov,Andrey Turchanin,Jürgen Schnack,Nigel A. Marks,Richard A. Wilhelm
Carbon nanomembranes (CNMs) are nanometer-thin disordered carbon materials that are suitable for a range of applications, from energy generation and storage through to water filtration. The structure–property relationships of these nanomembranes are challenging to study using traditional experimental characterization techniques, primarily due to the radiation sensitivity of the free-standing membrane. Highly charged ion spectroscopy is a novel characterization method that is able to infer structural details of the carbon nanomembrane without concern about induced damage affecting the measurements. Here we employ molecular dynamics simulations to produce candidate structural models of terphenylthiol-based CNMs with varying degrees of nanoscale porosity and compare predicted ion charge exchange data and tensile moduli to experiment. The results suggest that the in-vacuum CNM composition likely comprises a significant fraction of under-coordinated carbon, with an open subnanometer porous structure. Such a carbon network would be reactive in the atmosphere and would be presumably stabilized by hydrogen and oxygen groups under atmospheric conditions.
{"title":"Revealing the Innate Subnanometer Porous Structure of Carbon Nanomembranes with Molecular Dynamics Simulations and Highly-Charged Ion Spectroscopy","authors":"Filip Vuković,Anna Niggas,Levin Mihlan,Zhen Yao,Armin Gölzhäuser,Louise Fréville,Vladislav Stroganov,Andrey Turchanin,Jürgen Schnack,Nigel A. Marks,Richard A. Wilhelm","doi":"10.1021/acs.jpcc.5c08242","DOIUrl":"https://doi.org/10.1021/acs.jpcc.5c08242","url":null,"abstract":"Carbon nanomembranes (CNMs) are nanometer-thin disordered carbon materials that are suitable for a range of applications, from energy generation and storage through to water filtration. The structure–property relationships of these nanomembranes are challenging to study using traditional experimental characterization techniques, primarily due to the radiation sensitivity of the free-standing membrane. Highly charged ion spectroscopy is a novel characterization method that is able to infer structural details of the carbon nanomembrane without concern about induced damage affecting the measurements. Here we employ molecular dynamics simulations to produce candidate structural models of terphenylthiol-based CNMs with varying degrees of nanoscale porosity and compare predicted ion charge exchange data and tensile moduli to experiment. The results suggest that the in-vacuum CNM composition likely comprises a significant fraction of under-coordinated carbon, with an open subnanometer porous structure. Such a carbon network would be reactive in the atmosphere and would be presumably stabilized by hydrogen and oxygen groups under atmospheric conditions.","PeriodicalId":61,"journal":{"name":"The Journal of Physical Chemistry C","volume":"14 1","pages":""},"PeriodicalIF":4.126,"publicationDate":"2026-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147383831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-10DOI: 10.1021/acs.jpcc.5c07927
Sara T. Gebre,Heungsoo Kim,Daniel C. Ratchford,William A. Maza,Viktoriia E. Babicheva,Evgeniya Lock,Jeffrey C. Owrutsky,Adam D. Dunkelberger
Epsilon near zero (ENZ) materials have potential in various applications such as all-optical switching and quantum information. Materials that support ENZ modes include metamaterials, semiconductors, and transparent conducting oxides (TCOs) like indium tin oxide (ITO). ITO supports an ENZ mode in the near-infrared (IR), giving rise to large nonlinearities and enabling strong optically induced changes in its refractive index. Recently, a perovskite TCO, La-doped BaSnO3 (LBSO) has demonstrated wide tunability of the ENZ wavelength ranging from the near IR to mid-IR regions. In this work, we use a reflective gold layer to access the Ferrell-Berreman (FB) mode of LBSO, a special class of leaky optical mode that occurs at the material’s ENZ wavelength. The FB mode has strong extinction, making it an ideal candidate for reflection modulation. Here, we interrogate the charge carrier dynamics of multiple Au-coated LBSO samples with varying ENZ wavelengths, paying special attention to the tuning behavior of the FB mode. We find that, upon UV excitation, injected charge carriers are long-lived compared to other transparent conducting oxides and that the photoexcited carriers induce strong modulation of the FB mode. This Au-coated LBSO shows promise for infrared optical switching applications, especially those requiring high thermal stability.
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