The AUREX cell: a versatile operando electrochemical cell for studying catalytic materials using X-ray diffraction, total scattering and X-ray absorption spectroscopy under working conditions.

Understanding the structure-property relationship in electrocatalysts under working conditions is crucial for the rational design of novel and improved catalytic materials. This paper presents the Aarhus University reactor for electrochemical studies using X-rays (AUREX) operando electrocatalytic flow cell, designed as an easy-to-use versatile setup with a minimal background contribution and a uniform flow field to limit concentration polarization and handle gas formation. The cell has been employed to measure operando total scattering, diffraction and absorption spectroscopy as well as simultaneous combinations thereof on a commercial silver electrocatalyst for proof of concept. This combination of operando techniques allows for monitoring of the short-, medium- and long-range structure under working conditions, including an applied potential, liquid electrolyte and local reaction environment. The structural transformations of the Ag electrocatalyst are monitored with non-negative matrix factorization, linear combination analysis, the Pearson correlation coefficient matrix, and refinements in both real and reciprocal space. Upon application of an oxidative potential in an Ar-saturated aqueous 0.1 M KHCO3/K2CO3 electrolyte, the face-centered cubic (f.c.c.) Ag gradually transforms first to a trigonal Ag2CO3 phase, followed by the formation of a monoclinic Ag2CO3 phase. A reducing potential immediately reverts the structure to the Ag (f.c.c.) phase. Following the electrochemical-reaction-induced phase transitions is of fundamental interest and necessary for understanding and improving the stability of electrocatalysts, and the operando cell proves a versatile setup for probing this. In addition, it is demonstrated that, when studying electrochemical reactions, a high energy or short exposure time is needed to circumvent beam-induced effects.

Why are information-theoretic descriptors powerful predictors of atomic and molecular polarizabilities.

CONTEXT: We rationalize the excellent performance of information-theoretic descriptors for predicting atomic and molecular polarizabilities. It seems that descriptors which capture information about the change in valence-shell structure, especially the relative Fisher information measures, are particularly useful. Using this, we can rationalize why the G3 form of the relative Fisher information, which measures the deviation of effective nuclear charge between an atom-in-a-molecule and the reference pro-atom, is especially effective as a predictor of molecular polarizability. METHODS: There are no methods used in this paper, which relies on mathematical derivation and analysis.

Structure-Glass Transition Relationships in Non-Isocyanate Polyhydroxyurethanes.

The molecular dynamics, with an emphasis on the calorimetric and dynamic glass transitions, of non-isocyanate polyhydroxyurethanes (PHUs) produced by the equimolar polyaddition of polyether-based dicyclic carbonates (P-CCs) and various short diamines was studied. The diamine component consisted of a short aliphatic diamine (1,4-diaminobutane, DAB) and a more complex 'characteristic' diamine. The study was conducted to investigate (i) the chemical structure of the characteristic amine, (ii) its molar ratio, and (iii) the structure and molar mass of the P-CC. Infrared spectroscopy, differential scanning calorimetry, and broadband dielectric spectroscopy were employed. The P-CC, constituting the bulk of the systems, was the most crucial component for the glass transition. The characteristic amine influenced the glass transition as a result of its bulky structure, but also presumably as a result of the introduction of free volume and the formation of hydrogen bonds. The dynamic glass transition (α relaxation) trace in the Arrhenius plots showed a subtle change at a certain temperature that merits further study in the future. The charge mobility was fully coupled with the molecular mobility, as evidenced by dc conductivity being directly proportional to the characteristic frequency of α relaxation. The fluctuation in carbonyl units (ß relaxation) was mildly affected by changes in their immediate environment.

A QSPR Model for Henry's Law Constants of Organic Compounds in Water and Ethanol for Distilled Spirits.

Henry's law describes the vapor-liquid equilibrium for dilute gases dissolved in a liquid solvent phase. Descriptions of vapor-liquid equilibrium allow the design of improved separations in the food and beverage industry. The consumer experience of taste and odor are greatly affected by the liquid and vapor phase behavior of organic compounds. This study presents a machine learning (ML) based model that allows quick, accurate predictions of Henry's law constants (kH) for many common organic compounds. Users input only a Simplified Molecular-Input Line-Entry System (SMILES) string or a common English name, and the model returns Henry's law estimates for compounds in water and ethanol. Training was performed on 5,690 compounds. Training data were gathered from an existing database and were supplemented with quantum mechanical (QM) calculations. An extra trees regression model was generated that predicts kH with a mean absolute error of 1.3 in log space and an R2 of 0.98. The model is applied to common flavor and odor compounds in bourbon whiskey as a test case for food and beverage applications.

Leveraging experimental and computational tools for advancing carbon capture adsorbents research.

CO2 emissions have been steadily increasing and have been a major contributor for climate change compelling nations to take decisive action fast. The average global temperature could reach 1.5 °C by 2035 which could cause a significant impact on the environment, if the emissions are left unchecked. Several strategies have been explored of which carbon capture is considered the most suitable for faster deployment. Among different carbon capture solutions, adsorption is considered both practical and sustainable for scale-up. But the development of adsorbents that can exhibit satisfactory performance is typically done through the experimental approach. This hit and trial method is costly and time consuming and often success is not guaranteed. Machine learning (ML) and other computational tools offer an alternate to this approach and is accessible to everyone. Often, the research towards materials focuses on maximizing its performance under simulated conditions. The aim of this study is to present a holistic view on progress in material research for carbon capture and the various tools available in this regard. Thus, in this review, we first present a context on the workflow for carbon capture material development before providing various machine learning and computational tools available to support researchers at each stage of the process. The most popular application of ML models is for predicting material performance and recommends that ML approaches can be utilized wherever possible so that experimentations can be focused on the later stages of the research and development.

Liquid nitrogen ball-milled mechanochemical modification of starches with typically selected A, B and C crystal types on multiscale structure and physicochemical properties.

This study investigated the effect of liquid nitrogen ball-milled mechanochemical treatment on multiscale structure and physicochemical properties of starches with typically selected A (rice starch, ReS), B (potato starch, PtS) and C (pea starch, PeS) crystal types. The morphology of starch samples changed from integral granules to irregular fragments, and the interaction between the exposure OH bonds led to a serious agglomeration. As the treatment times extended, the crystalline structure of starch samples was gradually destroyed, and the excessive treatment approached amorphization. Moreover, the thermal stability of starch samples showed the downward tendency; and with amorphization increased, the swelling power (SP), solubility (S), water absorption capacity (WAC), oil absorption capacity (OAC) and hydrolysis rate of starch samples gradually increased. The obtained results provided a theoretical foundation for broadening the application range of ball-milled starches with different crystal types.

From protein structure to an optimized chromatographic capture step using multiscale modeling.

Optimizing a biopharmaceutical chromatographic purification process is currently the greatest challenge during process development. A lack of process understanding calls for extensive experimental efforts in pursuit of an optimal process. In silico techniques, such as mechanistic or data driven modeling, enhance the understanding, allowing more cost-effective and time efficient process optimization. This work presents a modeling strategy integrating quantitative structure property relationship (QSPR) models and chromatographic mechanistic models (MM) to optimize a cation exchange (CEX) capture step, limiting experiments. In QSPR, structural characteristics obtained from the protein structure are used to describe physicochemical behavior. This QSPR information can be applied in MM to predict the chromatogram and optimize the entire process. To validate this approach, retention profiles of six proteins were determined experimentally from mixtures, at different pH (3.5, 4.3, 5.0, and 7.0). Four proteins at different pH's were used to train QSPR models predicting the retention volumes and characteristic charge, subsequently the equilibrium constant was determined. For an unseen protein knowing only the protein structure, the retention peak difference between the modeled and experimental peaks was 0.2% relative to the gradient length (60 column volume). Next, the CEX capture step was optimized, demonstrating a consistent result in both the experimental and QSPR-based methods. The impact of model parameter confidence on the final optimization revealed two viable process conditions, one of which is similar to the optimization achieved using experimentally obtained parameters. The multiscale modeling approach reduces the required experimental effort by identification of initial process conditions, which can be optimized.

Mapping Biomaterial Complexity by Machine Learning.

Biomaterials often have subtle properties that ultimately drive their bespoke performance. Given this nuanced structure-function behavior, the standard scientific approach of one experiment at a time or design of experiment methods is largely inefficient for the discovery of complex biomaterials. More recently, high-throughput experimentation coupled with machine learning methods has matured beyond expert users allowing scientists and engineers from diverse backgrounds to access these powerful data science tools. As a result, we now have the opportunity to strategically utilize all available data from high-throughput experiments to train efficacious models and map the structure-function behavior of biomaterials for their discovery. Herein, we discuss this necessary shift to data-driven determination of structure-function properties of biomaterials as we highlight how machine learning is leveraged in identifying physicochemical cues for biomaterials in tissue engineering, gene delivery, drug delivery, protein stabilization, and antifouling materials. We also discuss data-mining approaches that are coupled with machine learning to map biomaterial functions that reduce the load on experimental approaches for faster biomaterial discovery. Ultimately, harnessing the prowess of machine learning will lead to accelerated discovery and development of optimal biomaterial designs.

Halogen Bond Unlocks Ultra-High Birefringence.

Anisotropy is crucial for birefringence (Δn) in optical materials, but optimizing it remains a formidable challenge (Δn >0.3). Supramolecular frameworks incorporating π-conjugated components are promising for achieving enhanced birefringence because of their structural diversity and inherent anisotropy. Herein, we first synthesized (C6H6NO2)+Cl- (NAC) and then constructed a halogen-bonded supramolecular framework I+(C6H4NO2)- (INA) by halogen aliovalent substitution of Cl- with I+. The organic moieties are protonated and deprotonated nicotinic acid (NA), respectively. The antiparallel arrangement of birefringent-active units in NAC and INA leads to significant differences in the bonding characteristics between the interlayer and intralayer domains. Moreover, the [O⋅⋅⋅I+⋅⋅⋅N] halogen bond in 1D [I+(C6H4NO2)-] chain exhibits stronger interactions and stricter directionality, resulting in a more pronounced in-plane anisotropy between the intrachain and interchain directions. Consequently, INA exhibits exceptional birefringent performance, with a value of 0.778 at 550 nm, twice that of NAC (0.363 at 550 nm). This value significantly exceeds those of commercial birefringent crystals, such as CaCO3 (0.172 at 546 nm), and is the highest reported value among ultraviolet birefringent crystals. This work presents a novel design strategy that employs halogen bonds as connection sites and modes for birefringent-active units, opening new avenues for developing high-performance birefringent crystals.

Three-dimensional representation of the pure electric-dipole and the mixed first hyperpolarizabilities: The modified unit sphere representation.

In this work, the theory of the modified unit sphere representation (mUSR) has been proposed as a computational tool suitable for the three-dimensional representation of the pure electric-dipole [ ß λ µ ν ( - 2 ω ; ω , ω ) ] as well as of the mixed electric-dipole/magnetic-dipole [ α J λ µ ν ( - 2 ω ; ω , ω ) and ß J λ µ ν ( - 2 ω ; ω , ω ) ] or electric-dipole/electric-quadrupole [ α K λ µ ν o ( - 2 ω ; ω , ω ) and ß K λ µ ν o ( - 2 ω ; ω , ω ) ] first hyperpolarizabilities. These five quantities are Cartesian tensors and they are responsible for the chiral signal in the chiroptical version of the hyper-Rayleigh scattering (HRS) spectroscopy, namely the HRS optical activity (HRS-OA) spectroscopy. For the first time, for each hyperpolarizability, alongside with the three-dimensional representation of the whole (i.e., reducible) Cartesian tensors, the mUSRs are developed for each of the irreducible Cartesian tensors (ICTs) that constitute them. This scheme has been applied to a series of three (chiral) hexahelicene molecules containing different degrees of electron-withdrawing (quinone) groups and characterized by the same (positive) handedness. For these molecules, the mUSR shows that, upon substitution, the most remarkable qualitative and semi-quantitative (enhancement of the molecular responses) effects are obtained for the pure electric-dipole and for the mixed electric-dipole/magnetic-dipole hyperpolarizabilities.

Structure-Property Relationships in Auxetic Liquid Crystal Elastomers-The Effect of Spacer Length.

Auxetics are materials displaying a negative Poisson's ratio, i.e., getting thicker in one or both transverse axes when subject to strain. In 2018, liquid crystal elastomers (LCEs) displaying auxetic behaviour, achieved via a biaxial reorientation, were first reported. Studies have since focused on determining the physics underpinning the auxetic response, with investigations into structure-property relationships within these systems so far overlooked. Herein, we report the first structure-property relationships in auxetic LCEs, examining the effect of changes to the length of the spacer chain. We demonstrate that for LCEs with between six and four carbons in the spacer, an auxetic response is observed, with the threshold strain required to achieve this response varying from 56% (six carbon spacers) to 81% (four carbon spacers). We also demonstrate that Poisson's ratios as low as -1.3 can be achieved. Further, we report that the LCEs display smectic phases with spacers of seven or more carbons; the resulting internal constraints cause low strains at failure, preventing an auxetic response. We also investigate the dependence of the auxetic threshold on the dynamics of the samples, finding that when accounting for the glass transition temperature of the LCEs, the auxetic thresholds converge around 56%, regardless of spacer length.

Underlying Roles of Polyol Additives in Promoting CO2 Capture in PEI/Silica Adsorbents.

Solid-supported amines having low molecular weight branched poly(ethylenimine) (PEI) physically impregnated into porous solid supports are promising adsorbents for CO2 capture. Co-impregnating short-chain poly(ethylene glycol) (PEG) together with PEI alters the performance of the adsorbent, delivering improved amine efficiency (AE, mol CO2 sorbed/mol N) and faster CO2 uptake rates. To uncover the physical basis for this improved gas capture performance, we probe the distribution and mobility of the polymers in the pores via small angle neutron scattering (SANS), solid-state NMR, and molecular dynamic (MD) simulation studies. SANS and MD simulations reveal that PEG displaces wall-bound PEI, making amines more accessible for CO2 sorption. Solid-state NMR and MD simulation suggest intercalation of PEG into PEI domains, separating PEI domains and reducing amine-amine interactions, providing potential PEG-rich and amine-poor interfacial domains that bind CO2 weakly via physisorption while providing facile pathways for CO2 diffusion. Contrary to a prior literature hypothesis, no evidence is obtained for PEG facilitating PEI mobility in solid supports. Instead, the data suggest that PEG chains coordinate to PEI, forming larger bodies with reduced mobility compared to PEI alone. We also demonstrate promising CO2 uptake and desorption kinetics at varied temperatures, facilitated by favorable amine distribution.

Structural and Photophysical Trends in Rhenium(I) Carbonyl Complexes with 2,2':6',2″-Terpyridines.

This is the first comprehensive review of rhenium(I) carbonyl complexes with 2,2':6',2″-terpyridine-based ligands (R-terpy)-encompassing their synthesis, molecular features, photophysical behavior, and potential applications. Particular attention has been devoted to demonstrating how the coordination mode of 2,2':6',2″-terpyridine (terpy-κ2N and terpy-κ3N), structural modifications of terpy framework (R), and the nature of ancillary ligands (X-mono-negative anion, L-neutral ligand) may tune the photophysical behavior of Re(I) complexes [Re(X/L)(CO)3(R-terpy-κ2N)]0/+ and [Re(X/L)(CO)2(R-terpy-κ3N)]0/+. Our discussion also includes homo- and heteronuclear multicomponent systems with {Re(CO)3(R-terpy-κ2N)} and {Re(CO)2(R-terpy-κ3N)} motifs. The presented structure-property relationships are of high importance for controlling the photoinduced processes in these systems and making further progress in the development of more efficient Re-based luminophores, photosensitizers, and photocatalysts for modern technologies.

Pushing the Limits of Heat Conduction in Covalent Organic Frameworks Through High-Throughput Screening of Their Thermal Conductivity.

Tailor-made materials featuring large tunability in their thermal transport properties are highly sought-after for diverse applications. However, achieving `user-defined' thermal transport in a single class of material system with tunability across a wide range of thermal conductivity values requires a thorough understanding of the structure-property relationships, which has proven to be challenging. Herein, large-scale computational screening of covalent organic frameworks (COFs) for thermal conductivity is performed, providing a comprehensive understanding of their structure-property relationships by leveraging systematic atomistic simulations of 10,750 COFs with 651 distinct organic linkers. Through the data-driven approach, it is shown that by strategic modulation of their chemical and structural features, the thermal conductivity can be tuned from ultralow (≈0.02 W m-1 K-1) to exceptionally high (≈50 W m-1 K-1) values. It is revealed that achieving high thermal conductivity in COFs requires their assembly through carbon-carbon linkages with densities greater than 500 kg m-3, nominal void fractions (in the range of ≈0.6-0.9) and highly aligned polymeric chains along the heat flow direction. Following these criteria, it is shown that these flexible polymeric materials can possess exceptionally high thermal conductivities, on par with several fully dense inorganic materials. As such, the work reveals that COFs mark a new regime of materials design that combines high thermal conductivities with low densities.

An Unprecedented [BO2]-Based Deep-Ultraviolet Transparent Nonlinear Optical Crystal by Superhalogen Substitution.

Solid-state structures with the superhalogen [BO2]- have thus far only been observed with a few compounds whose syntheses require high reaction temperatures and complicated procedures, while their optical properties remain almost completely unexplored. Herein, we report a facile, energy-efficient synthesis of the first [BO2]-based deep-ultraviolet (deep-UV) transparent oxide K9[B4O5(OH)4]3(CO3)(BO2) ⋅ 7H2O (KBCOB). Detailed structural characterization and analysis confirm that KBCOB possesses a rare four-in-one three-dimensional quasi-honeycomb framework, with three π-conjugated anions ([BO2]-, [BO3]3-, and [CO3]2-) and one non-π-conjugated anion ([BO4]5-) in the one crystal. The evolution from the traditional halogenated nonlinear optical (NLO) analogues to KBCOB by superhalogen [BO2]- substitution confers deep-UV transparency (<190 nm), a large second-harmonic generation response (1.0×KH2PO4 @ 1064 nm), and a 15-fold increase in birefringence. This study affords a new route to the facile synthesis of functional [BO2]-based oxides, paving the way for the development of next-generation high-performing deep-UV NLO materials.

Sr2[B5O8(OH)]2 ⋠[B(OH)3] ⋠H2O: A Strontium Borate That Shows Deep-Ultraviolet-Transparent Nonlinear Optical Properties.

A new noncentrosymmetric strontium borate, P1-Sr2[B5O8(OH)]2 ⋅ [B(OH)3] ⋅ H2O (1), has been synthesized under the hydrothermal condition. The P1-Sr2[B5O8(OH)]2 ⋅ [B(OH)3] ⋅ H2O shows a layered B-O network with 9-ring windows in the ab plane. Sr2+ cations, H3BO3, and H2O molecules are located in the voids of layers and interlayers, respectively. The P1-Sr2[B5O8(OH)]2 ⋅ [B(OH)3] ⋅ H2O is the first synthetic phase of veatchite, while the other three polymorphs are found in different natural minerals. This strontium borate is a potential deep-ultraviolet-transparent nonlinear-optical (NLO) crystal whose second-harmonic-generation (SHG) intensity is 1.7 times that of KH2PO4 (KDP) and is phase-matchable. The short wavelength cutoff edge of compound 1 is below 190 nm. Density functional theory (DFT) calculations show that the B-O units are responsible for the nonlinear optical property.

Driving Triplet State Population in Benzothioxanthene Imide Dyes: Let's twist!

Controlling the formation of photoexcited triplet states is critical for many (photo)chemical and physical applications. Here, we demonstrate that a permanent out-of-plane distortion of the benzothioxanthene imide (BTI) dye promotes intersystem crossing by increasing spin-orbit coupling. This manipulation was achieved through a subtle chemical modification, specifically the bay-area methylation. Consequently, this simple yet efficient approach expands the catalog of known molecular engineering strategies for synthesizing heavy atom-free, dual redox-active, yet still emissive and synthetically accessible photosensitizers.

Advancing material property prediction: using physics-informed machine learning models for viscosity.

In materials science, accurately computing properties like viscosity, melting point, and glass transition temperatures solely through physics-based models is challenging. Data-driven machine learning (ML) also poses challenges in constructing ML models, especially in the material science domain where data is limited. To address this, we integrate physics-informed descriptors from molecular dynamics (MD) simulations to enhance the accuracy and interpretability of ML models. Our current study focuses on accurately predicting viscosity in liquid systems using MD descriptors. In this work, we curated a comprehensive dataset of over 4000 small organic molecules' viscosities from scientific literature, publications, and online databases. This dataset enabled us to develop quantitative structure-property relationships (QSPR) consisting of descriptor-based and graph neural network models to predict temperature-dependent viscosities for a wide range of viscosities. The QSPR models reveal that including MD descriptors improves the prediction of experimental viscosities, particularly at the small data set scale of fewer than a thousand data points. Furthermore, feature importance tools reveal that intermolecular interactions captured by MD descriptors are most important for viscosity predictions. Finally, the QSPR models can accurately capture the inverse relationship between viscosity and temperature for six battery-relevant solvents, some of which were not included in the original data set. Our research highlights the effectiveness of incorporating MD descriptors into QSPR models, which leads to improved accuracy for properties that are difficult to predict when using physics-based models alone or when limited data is available.

Dataset of process-structure-property feature relationships for AlSi10Mg material fabricated using laser powder bed fusion additive manufacturing.

This dataset reports microstructure and mechanical property features of AlSi10Mg manufactured using laser powder bed fusion over a wide range of processing conditions. Samples were fabricated with different combinations of laser power, scan speed, and hatch spacing to probe dense regimes as well as porous samples resulting from keyholing and lack of fusion. Pore and grain/sub-grain features for each processing set were quantified. Sample porosity was measured using Archimedes density measurements and X-ray computed tomography (XCT). XCT was also used to characterize the surface roughness of samples along with pore size and morphology. Electron backscatter diffraction (EBSD) was used to characterize the grain size and morphology while scanning electron microscope (SEM) imaging and was used to measure solidification cell size. Uniaxial tension tests were performed to ascertain yield and ultimate tensile strengths, elongation, and elastic modulus, and microhardness was measured using Vickers indentation.