Christopher Taylor, Patrick Butler, Graeme Matthew Day
Computational crystal structure prediction (CSP) is an increasingly powerful technique in materials discovery, due to its ability to reveal trends and permit insight across the possibility space of crystal structures of a candidate molecule, beyond simply the observed structure(s). In this work, we demonstrate the reliability and scalability of CSP methods for small, rigid organic molecules by performing in-depth CSP investigations for over 1000 such compounds, the largest survey of its kind to-date. We show that this highly-efficient force-field-based CSP approach is superbly predictive, locating 99.4% of observed experimental structures, and ranking a large majority of these (74%) as among the most stable possible structures (to within uncertainty due to thermal effects). We present two examples of insights such large predicted datasets can permit, examining the space group preferences of organic molecular crystals and rationalising empirical rules concerning the spontaneous resolution of chiral molecules. Finally, we exploit this large and diverse dataset for developing transferable machine-learned energy potentials for the organic solid state, training a neural network lattice energy correction to force field energies that offers substantial improvements to the already impressive energy rankings, and a MACE equivariant message-passing neural network for crystal structure reoptimisation. We conclude that the excellent performance and reliability of the CSP workflow enables the creation of very large datasets of broad utility and explanatory power in materials design.
{"title":"Predictive crystallography at scale: mapping, validating, and learning from 1,000 crystal energy landscapes","authors":"Christopher Taylor, Patrick Butler, Graeme Matthew Day","doi":"10.1039/d4fd00105b","DOIUrl":"https://doi.org/10.1039/d4fd00105b","url":null,"abstract":"Computational crystal structure prediction (CSP) is an increasingly powerful technique in materials discovery, due to its ability to reveal trends and permit insight across the possibility space of crystal structures of a candidate molecule, beyond simply the observed structure(s). In this work, we demonstrate the reliability and scalability of CSP methods for small, rigid organic molecules by performing in-depth CSP investigations for over 1000 such compounds, the largest survey of its kind to-date. We show that this highly-efficient force-field-based CSP approach is superbly predictive, locating 99.4% of observed experimental structures, and ranking a large majority of these (74%) as among the most stable possible structures (to within uncertainty due to thermal effects). We present two examples of insights such large predicted datasets can permit, examining the space group preferences of organic molecular crystals and rationalising empirical rules concerning the spontaneous resolution of chiral molecules. Finally, we exploit this large and diverse dataset for developing transferable machine-learned energy potentials for the organic solid state, training a neural network lattice energy correction to force field energies that offers substantial improvements to the already impressive energy rankings, and a MACE equivariant message-passing neural network for crystal structure reoptimisation. We conclude that the excellent performance and reliability of the CSP workflow enables the creation of very large datasets of broad utility and explanatory power in materials design.","PeriodicalId":76,"journal":{"name":"Faraday Discussions","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141256188","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}
Marija Blazic, Candice Gautier, Thomas Norberg, Mikael Widersten
Epoxide hydrolase StEH1, from potato, is similar in overall structural fold and catalytic mechanism to haloalkane dehalogenase DhlA from Xanthobacter autotrophicus. StEH1 displays low (promiscuous) hydrolytic activity with (2-chloro)- and (2-bromo)ethanebenzene producing 2-phenylethanol. To investigate possibilities to amplify these very low dehalogenase activities, StEH1 was subjected to targeted randomized mutagenesis at five active-site amino acid residues and the resulting protein library was challenged for reactivity towards a bait chloride substrate. Enzymes catalyzing the first half-reaction of a hydrolytic cycle were isolated following monovalent phage display of the mutated proteins. Several StEH1 derived enzymes were identified with enhanced dehalogenase activities.
{"title":"High-throughput selection of (new) enzymes: phage display-mediated isolation of alkyl halide hydrolases from a library of active-site mutated epoxide hydrolases.","authors":"Marija Blazic, Candice Gautier, Thomas Norberg, Mikael Widersten","doi":"10.1039/d4fd00001c","DOIUrl":"https://doi.org/10.1039/d4fd00001c","url":null,"abstract":"<p><p>Epoxide hydrolase StEH1, from potato, is similar in overall structural fold and catalytic mechanism to haloalkane dehalogenase DhlA from <i>Xanthobacter autotrophicus</i>. StEH1 displays low (promiscuous) hydrolytic activity with (2-chloro)- and (2-bromo)ethanebenzene producing 2-phenylethanol. To investigate possibilities to amplify these very low dehalogenase activities, StEH1 was subjected to targeted randomized mutagenesis at five active-site amino acid residues and the resulting protein library was challenged for reactivity towards a bait chloride substrate. Enzymes catalyzing the first half-reaction of a hydrolytic cycle were isolated following monovalent phage display of the mutated proteins. Several StEH1 derived enzymes were identified with enhanced dehalogenase activities.</p>","PeriodicalId":76,"journal":{"name":"Faraday Discussions","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141199017","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}
Lei Chen, Carlos Bornes, Oscar Bengtsson, Andreas Erlebach, Ben Slater, Lukáš Grajciar, Christopher J. Heard
One of the main limitations in supporting experimental characterization of Al siting/pairing via modelling is the high computational cost of ab initio calculations. For this reason, most works rely on static or very short dynamical simulations, considering limited Al pairing/siting combinations. As a result, comparison with experiment suffers from a large degree of uncertainty. To alleviate this limitation we have developed neural network potentials (NNPs) which can dynamically sample across broad configurational and chemical spaces of sodium-form aluminosilicate zeolites, preserving the level of accuracy of the ab initio (dispersion-corrected metaGGA) training set. By exploring a wide range of Al/Na arrangements and a combination of experimentally relevant Si/Al ratios, we found that the 23Na NMR spectra of dehydrated high-silica CHA zeolite offer an opportunity to assess the distribution and pairing of Al atoms. We observed that the 23Na chemical shift is sensitive not only to the location of sodium in 6- and 8MRs, but also to the Al-Sin-Al sequence length. Furthermore, neglect of thermal and dynamical contributions were found to lead to errors of several ppm, and have a profound influence on the shape of the spectra and the dipolar coupling constants, thus necessitating the long-term dynamical simulations made feasible by NNPs. Finally, we obtained a predictive regression model for 23Na chemical shift in CHA (Si/Al = 35, 17, 11) that circumvents the need for expensive NMR density functional calculations and can be easily extended to other zeolite frameworks. By combining NNPs and regression methods, we can expedite the simulations of NMR properties and capture the effect dynamics on the spectra, which is often overlooked in computational studies despite its clear manifestation in experimental setups.
{"title":"A machine learning approach for dynamical modelling of Al distributions in zeolites via 23Na/27Al solid-state NMR","authors":"Lei Chen, Carlos Bornes, Oscar Bengtsson, Andreas Erlebach, Ben Slater, Lukáš Grajciar, Christopher J. Heard","doi":"10.1039/d4fd00100a","DOIUrl":"https://doi.org/10.1039/d4fd00100a","url":null,"abstract":"One of the main limitations in supporting experimental characterization of Al siting/pairing via modelling is the high computational cost of ab initio calculations. For this reason, most works rely on static or very short dynamical simulations, considering limited Al pairing/siting combinations. As a result, comparison with experiment suffers from a large degree of uncertainty. To alleviate this limitation we have developed neural network potentials (NNPs) which can dynamically sample across broad configurational and chemical spaces of sodium-form aluminosilicate zeolites, preserving the level of accuracy of the ab initio (dispersion-corrected metaGGA) training set. By exploring a wide range of Al/Na arrangements and a combination of experimentally relevant Si/Al ratios, we found that the <small><sup>23</sup></small>Na NMR spectra of dehydrated high-silica CHA zeolite offer an opportunity to assess the distribution and pairing of Al atoms. We observed that the <small><sup>23</sup></small>Na chemical shift is sensitive not only to the location of sodium in 6- and 8MRs, but also to the Al-Si<small><sub>n</sub></small>-Al sequence length. Furthermore, neglect of thermal and dynamical contributions were found to lead to errors of several ppm, and have a profound influence on the shape of the spectra and the dipolar coupling constants, thus necessitating the long-term dynamical simulations made feasible by NNPs. Finally, we obtained a predictive regression model for <small><sup>23</sup></small>Na chemical shift in CHA (Si/Al = 35, 17, 11) that circumvents the need for expensive NMR density functional calculations and can be easily extended to other zeolite frameworks. By combining NNPs and regression methods, we can expedite the simulations of NMR properties and capture the effect dynamics on the spectra, which is often overlooked in computational studies despite its clear manifestation in experimental setups.","PeriodicalId":76,"journal":{"name":"Faraday Discussions","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141256526","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}
Phillip M. Maffettone, William Fletcher, Thomas Christian Nicholas, Volker L. Deringer, Jane R. Allison, Lorna Smith, Andrew Goodwin
The pair distribution function (PDF) is an important metric for characterising structure in complex materials, but it is well known that meaningfully different structural models can sometimes give rise to equivalent PDFs. In this paper, we discuss the use of model likelihoods as a general approach for discriminating between such homometric structure solutions. Drawing on two main case studies---one concerning the structure of a small peptide and the other amorphous calcium carbonate---we show how consideration of model likelihood can help drive robust structure solution even in cases where the PDF is particularly information poor. The obvious thread of these individual case studies is the potential role for machine learning approaches to help guide structure determination from the PDF, and our paper finishes with some forward-looking discussion along these lines.
对分布函数（PDF）是表征复杂材料结构的重要指标，但众所周知，有意义的不同结构模型有时会产生等效的 PDF。在本文中，我们将讨论如何使用模型似然值作为区分此类等效结构解的一般方法。通过两个主要的案例研究--一个是关于小肽的结构，另一个是关于无定形碳酸钙--我们展示了即使在 PDF 信息特别贫乏的情况下，考虑模型似然性如何有助于推动稳健的结构求解。这些单独案例研究的明显线索是机器学习方法在帮助指导 PDF 结构确定方面的潜在作用，我们的论文最后沿着这些线索进行了一些前瞻性讨论。
{"title":"When can we trust structural models derived from pair distribution function measurements?","authors":"Phillip M. Maffettone, William Fletcher, Thomas Christian Nicholas, Volker L. Deringer, Jane R. Allison, Lorna Smith, Andrew Goodwin","doi":"10.1039/d4fd00106k","DOIUrl":"https://doi.org/10.1039/d4fd00106k","url":null,"abstract":"The pair distribution function (PDF) is an important metric for characterising structure in complex materials, but it is well known that meaningfully different structural models can sometimes give rise to equivalent PDFs. In this paper, we discuss the use of model likelihoods as a general approach for discriminating between such homometric structure solutions. Drawing on two main case studies---one concerning the structure of a small peptide and the other amorphous calcium carbonate---we show how consideration of model likelihood can help drive robust structure solution even in cases where the PDF is particularly information poor. The obvious thread of these individual case studies is the potential role for machine learning approaches to help guide structure determination from the PDF, and our paper finishes with some forward-looking discussion along these lines.","PeriodicalId":76,"journal":{"name":"Faraday Discussions","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141191340","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}
Christopher M Collins, Hasan Sayeed, George Darling, John B Claridge, Taylor D. Sparks, Matthew J. Rosseinsky
The prediction of new compounds via crystal structure prediction may transform how the materials chemistry community discovers new compounds. In the prediction of inorganic crystal structures there are three distinct classes of prediction; Performing crystal structure prediction via heuristic algorithms, using a range of established crystal structure prediction codes, an emerging community using generative machine learning models to predict crystal structures directly and the use of mathematical optimisation to solve crystal structures exactly. In this work, we demonstrate the combination of heuristic and generative machine learning, the use of a generative machine learning model to produce the starting population of crystal structures for a heuristic algorithm and discuss the benefits, demonstrating the method on eight known compounds with reported crystal structures and three hypothetical compounds. We show that the integration of machine learning structure generation with heuristic structure prediction results in both faster compute times per structure and lower energies. This work provides to the community a set of eleven compounds with varying chemistry and complexity that can be used as a benchmark for new crystal structure prediction methods as they emerge.
{"title":"Integration of generative machine learning with the heuristic crystal structure prediction code FUSE","authors":"Christopher M Collins, Hasan Sayeed, George Darling, John B Claridge, Taylor D. Sparks, Matthew J. Rosseinsky","doi":"10.1039/d4fd00094c","DOIUrl":"https://doi.org/10.1039/d4fd00094c","url":null,"abstract":"The prediction of new compounds via crystal structure prediction may transform how the materials chemistry community discovers new compounds. In the prediction of inorganic crystal structures there are three distinct classes of prediction; Performing crystal structure prediction via heuristic algorithms, using a range of established crystal structure prediction codes, an emerging community using generative machine learning models to predict crystal structures directly and the use of mathematical optimisation to solve crystal structures exactly. In this work, we demonstrate the combination of heuristic and generative machine learning, the use of a generative machine learning model to produce the starting population of crystal structures for a heuristic algorithm and discuss the benefits, demonstrating the method on eight known compounds with reported crystal structures and three hypothetical compounds. We show that the integration of machine learning structure generation with heuristic structure prediction results in both faster compute times per structure and lower energies. This work provides to the community a set of eleven compounds with varying chemistry and complexity that can be used as a benchmark for new crystal structure prediction methods as they emerge.","PeriodicalId":76,"journal":{"name":"Faraday Discussions","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141190890","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}
Matteo Boventi, Michele Mauri, Franca Castiglione, Roberto Simonutti
Hydrophobic non-ionic (type V) deep eutectic solvents (DESs) are recently emerging as new class of sustainable materials that have shown unique properties in several applications. In this study, type V DESs thymol:camphor, menthol:thymol and eutectic mixtures (EMs) based on menthol:carboxylic acids with variable chain length are experimentally investigated by xenon NMR Spectroscopy aiming to clarify the peculiar nanostructure of these materials. The results obtained from the analysis of the 129Xe chemical shifts and of the longitudinal relaxation times reveal a correlation between the deviation from ideality of the DESs and their structure free volume. Moreover, the effect of the variation of DESs and EMs composition on the liquid structure is also investigated.
疏水性非离子（V 型）深共晶溶剂（DES）是最近出现的一类新型可持续材料，在多种应用中显示出独特的性能。本研究通过氙核磁共振波谱对百里酚：樟脑、薄荷醇：百里酚和基于薄荷醇：羧酸的共晶混合物（EMs）进行了实验研究，旨在阐明这些材料的特殊纳米结构。对 129Xe 化学位移和纵向弛豫时间的分析结果表明，DES 的理想度偏差与其结构自由体积之间存在相关性。此外，还研究了 DESs 和 EMs 成分的变化对液体结构的影响。
{"title":"Exploring the structure of type V deep eutectic solvents by Xenon NMR Spectroscopy","authors":"Matteo Boventi, Michele Mauri, Franca Castiglione, Roberto Simonutti","doi":"10.1039/d4fd00083h","DOIUrl":"https://doi.org/10.1039/d4fd00083h","url":null,"abstract":"Hydrophobic non-ionic (type V) deep eutectic solvents (DESs) are recently emerging as new class of sustainable materials that have shown unique properties in several applications. In this study, type V DESs thymol:camphor, menthol:thymol and eutectic mixtures (EMs) based on menthol:carboxylic acids with variable chain length are experimentally investigated by xenon NMR Spectroscopy aiming to clarify the peculiar nanostructure of these materials. The results obtained from the analysis of the 129Xe chemical shifts and of the longitudinal relaxation times reveal a correlation between the deviation from ideality of the DESs and their structure free volume. Moreover, the effect of the variation of DESs and EMs composition on the liquid structure is also investigated.","PeriodicalId":76,"journal":{"name":"Faraday Discussions","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141191059","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}
Scanning electrochemical cell microscopy (SECCM) is a powerful nanoscale electrochemical technique that advances our understanding of heterogeneity at the electrode-electrolyte interface. Dual-channel nanopipettes can often serve as the probe, and a voltage bias between the channels can control the local electrolyte environment via migration and electroosmotic flow (EOF). The ability to elucidate and predict the contribution of each transport is desirable. In this work, we measured the limiting current of different redox molecules to experimentally elucidate the contribution of migration and EOF at the droplet-substrate interface in SECCM. The results were further supported by fluorescence imaging and finite element modeling. We showed that redox mediators with high charge, such as Ru(NH3)63+, migration contributes 5× as much mass transport limiting current compared to EOF when a bias voltage is applied. The exact contribution of each mode at a given potential bias depends on the electrical double layer structure, which can be tuned by the surface charge and solution composition. The contribution can be quantitatively predicted in the finite element model. Our findings will enable the precise control of mass transport in dual-channel SECCM and potentially open new scanning modes in SECCM via precise control of reaction flux.
{"title":"Controlling Droplet Cell Environment in Scanning Electrochemical Cell Microscopy (SECCM) via Migration and Electroosmotic Flow","authors":"Samuel F Wenzel, Heekwon Lee, Hang Ren","doi":"10.1039/d4fd00080c","DOIUrl":"https://doi.org/10.1039/d4fd00080c","url":null,"abstract":"Scanning electrochemical cell microscopy (SECCM) is a powerful nanoscale electrochemical technique that advances our understanding of heterogeneity at the electrode-electrolyte interface. Dual-channel nanopipettes can often serve as the probe, and a voltage bias between the channels can control the local electrolyte environment via migration and electroosmotic flow (EOF). The ability to elucidate and predict the contribution of each transport is desirable. In this work, we measured the limiting current of different redox molecules to experimentally elucidate the contribution of migration and EOF at the droplet-substrate interface in SECCM. The results were further supported by fluorescence imaging and finite element modeling. We showed that redox mediators with high charge, such as Ru(NH3)63+, migration contributes 5× as much mass transport limiting current compared to EOF when a bias voltage is applied. The exact contribution of each mode at a given potential bias depends on the electrical double layer structure, which can be tuned by the surface charge and solution composition. The contribution can be quantitatively predicted in the finite element model. Our findings will enable the precise control of mass transport in dual-channel SECCM and potentially open new scanning modes in SECCM via precise control of reaction flux.","PeriodicalId":76,"journal":{"name":"Faraday Discussions","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141168992","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}
Ion adsorption and dynamics in porous carbons is crucial for many technologies such as energy storage and desalination. Nuclear Magnetic Resonance (NMR) spectroscopy is a key method to investigate such systems thanks to the possibility to distinguish adsorbed (in-pore) and bulk (ex-pore) species in the spectra. However, the large variety of magnetic environments experienced by the ions adsorbed in the particles and the existence of dynamic exchange between the inside of the particles and the bulk renders the intepretation of the NMR experiments very complex. In this work, we optimise and apply a mesoscopic model to simulate NMR spectra of ions in systems where carbon particles of different sizes can be considered. We demonstrate that even for monodisperse systems, complex NMR spectra, with broad and narrow peaks, can be observed. We then show that the inclusion of polydispersity is essential to recover some experimentally observed features, such as the co-existence of peaks assigned to in-pore, exchange and bulk. Indeed, the variety of exchange rates between in-pore and ex-pore environments, present in experiments but not taken into account in analytical models, is necessary to reproduce the complexity of experimental NMR spectra.
{"title":"Investigating the effect of particle size distribution and complex exchange dynamics on NMR spectra of ions diffusing in disordered porous carbons through a mesoscopic model","authors":"El Hassane Lahrar, Celine Merlet","doi":"10.1039/d4fd00082j","DOIUrl":"https://doi.org/10.1039/d4fd00082j","url":null,"abstract":"Ion adsorption and dynamics in porous carbons is crucial for many technologies such as energy storage and desalination. Nuclear Magnetic Resonance (NMR) spectroscopy is a key method to investigate such systems thanks to the possibility to distinguish adsorbed (in-pore) and bulk (ex-pore) species in the spectra. However, the large variety of magnetic environments experienced by the ions adsorbed in the particles and the existence of dynamic exchange between the inside of the particles and the bulk renders the intepretation of the NMR experiments very complex. In this work, we optimise and apply a mesoscopic model to simulate NMR spectra of ions in systems where carbon particles of different sizes can be considered. We demonstrate that even for monodisperse systems, complex NMR spectra, with broad and narrow peaks, can be observed. We then show that the inclusion of polydispersity is essential to recover some experimentally observed features, such as the co-existence of peaks assigned to in-pore, exchange and bulk. Indeed, the variety of exchange rates between in-pore and ex-pore environments, present in experiments but not taken into account in analytical models, is necessary to reproduce the complexity of experimental NMR spectra.","PeriodicalId":76,"journal":{"name":"Faraday Discussions","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141191061","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}
Susana Ramalhete, Hayley Green, Jesús Angulo, Dinu Iuga, László Fábián, Gareth O. Lloyd, Yaroslav Z. Khimyak
Supramolecular hydrogels have a wide range of applications in the biomedical field, acting as scaffolds for cell culture, matrices for tissue engineering and vehicles for drug delivery. L-Phenylalanine (Phe) is a natural amino acid that plays a significant role in physiological and pathophysiological processes (phenylketonuria and assembly of fibrils linked to tissue damage). Since Myerson et al. (2002) reported that Phe forms a fibrous network in vitro, Phe’s self-assembly processes in water have been thoroughly investigated. We have reported structural control over gelation by introduction of a halogen atom in the aromatic ring of Phe, driving changes in the packing motifs, and therefore, dictating gelation functionality. The additional level of control gained by the preparation of multi-component gel systems offers significant advantages in tuning functional properties of such materials. Gaining molecular level information on the distribution of gelators between the inherent structural and dynamic heterogeneities of these materials remains a considerable challenge. Using multicomponent gels based on Phe and amino-L-phenylalanine (NH2-Phe) we explored the patterns of ordered/disordered domains in the gel fibres and will attempt to come up with general trends of interactions in the gel fibres and at the fibre/solution interfaces. Phe and NH2-Phe were found to self-assemble in water into crystalline hydrogels. The determined faster dynamics of exchange between gel and solution states of NH2-Phe in comparison with Phe was correlated with weaker intermolecular interactions, highlighting the role of head groups in dictating the strength of intermolecular interactions. In the mixed Phe/ NH2-Phe systems, at low concentration of NH2-Phe, disruption of the network was promoted by interference of the aliphatics of NH2-Phe with electrostatic interactions between Phe molecules. At high concentrations of NH2-Phe, multiple gelator hydrogels were formed with crystal habits different from those of the pure gel fibres. NMR crystallography approaches combining the strengths of solid- and solution state NMR proved particularly suitable to obtain structural and dynamic insights into “ordered” fibres, solution phase and fibre/solution interfaces in these gels. These findings are supported by the plethora of experimental (diffraction, rheology, microscopy, thermal analysis) and computational (crystal structure prediction, DFT based approaches and MD simulations) methods.
{"title":"Probing assembly/disassembly of ordered molecular hydrogels","authors":"Susana Ramalhete, Hayley Green, Jesús Angulo, Dinu Iuga, László Fábián, Gareth O. Lloyd, Yaroslav Z. Khimyak","doi":"10.1039/d4fd00081a","DOIUrl":"https://doi.org/10.1039/d4fd00081a","url":null,"abstract":"Supramolecular hydrogels have a wide range of applications in the biomedical field, acting as scaffolds for cell culture, matrices for tissue engineering and vehicles for drug delivery. L-Phenylalanine (Phe) is a natural amino acid that plays a significant role in physiological and pathophysiological processes (phenylketonuria and assembly of fibrils linked to tissue damage). Since Myerson et al. (2002) reported that Phe forms a fibrous network in vitro, Phe’s self-assembly processes in water have been thoroughly investigated. We have reported structural control over gelation by introduction of a halogen atom in the aromatic ring of Phe, driving changes in the packing motifs, and therefore, dictating gelation functionality. The additional level of control gained by the preparation of multi-component gel systems offers significant advantages in tuning functional properties of such materials. Gaining molecular level information on the distribution of gelators between the inherent structural and dynamic heterogeneities of these materials remains a considerable challenge. Using multicomponent gels based on Phe and amino-L-phenylalanine (NH2-Phe) we explored the patterns of ordered/disordered domains in the gel fibres and will attempt to come up with general trends of interactions in the gel fibres and at the fibre/solution interfaces. Phe and NH2-Phe were found to self-assemble in water into crystalline hydrogels. The determined faster dynamics of exchange between gel and solution states of NH2-Phe in comparison with Phe was correlated with weaker intermolecular interactions, highlighting the role of head groups in dictating the strength of intermolecular interactions. In the mixed Phe/ NH2-Phe systems, at low concentration of NH2-Phe, disruption of the network was promoted by interference of the aliphatics of NH2-Phe with electrostatic interactions between Phe molecules. At high concentrations of NH2-Phe, multiple gelator hydrogels were formed with crystal habits different from those of the pure gel fibres. NMR crystallography approaches combining the strengths of solid- and solution state NMR proved particularly suitable to obtain structural and dynamic insights into “ordered” fibres, solution phase and fibre/solution interfaces in these gels. These findings are supported by the plethora of experimental (diffraction, rheology, microscopy, thermal analysis) and computational (crystal structure prediction, DFT based approaches and MD simulations) methods.","PeriodicalId":76,"journal":{"name":"Faraday Discussions","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141146591","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}
Here we examine the question of the chemical models widely used to describe dense solutions, particularly ionic solutions. First of all, a simple macroscopic analysis shows that in the case of weak interactions, taking into account aggregated species amounts to modelling an effective attraction between solutes, although the stoichiometry used does not necessarily correspond to atomic reality. We then use a rigorous microscopic analysis to explain how, in the very general case, chemical models can be obtained from an atomic physical description. We show that there are no good or bad chemical models as long as we consider exact calculations. To obtain the simplest possible description, it is nevertheless advisable to take the speciation criterion that minimises the excess terms. Molecular simulations show that very often species can be defined simply by grouping ions which are in direct contact. In some cases, the appearance of macroscale clusters can be predicted.
{"title":"Chemical Models for Dense Solutions","authors":"Jean-Francois Dufreche, Bertrand Siboulet, Magali Duvail","doi":"10.1039/d4fd00084f","DOIUrl":"https://doi.org/10.1039/d4fd00084f","url":null,"abstract":"Here we examine the question of the chemical models widely used to describe dense solutions, particularly ionic solutions. First of all, a simple macroscopic analysis shows that in the case of weak interactions, taking into account aggregated species amounts to modelling an effective attraction between solutes, although the stoichiometry used does not necessarily correspond to atomic reality. We then use a rigorous microscopic analysis to explain how, in the very general case, chemical models can be obtained from an atomic physical description. We show that there are no good or bad chemical models as long as we consider exact calculations. To obtain the simplest possible description, it is nevertheless advisable to take the speciation criterion that minimises the excess terms. Molecular simulations show that very often species can be defined simply by grouping ions which are in direct contact. In some cases, the appearance of macroscale clusters can be predicted.","PeriodicalId":76,"journal":{"name":"Faraday Discussions","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141146611","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：