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Control of solvent adsorption in porous liquids through solvent-solvent interactions
Type 3 Porous Liquids (PLs) are a new class of materials that offer transformative potential for gas capture and utilization. These PLs are comprised of a bulky solvent with a suspended empty nanoporous material that enables selective and high-capacity capture from dilute and complex gas steams. The relationship between the nanoporous material, the structure of the solvent molecules, and the time dependence of solvent adsorption into the nanoporous material governs long term adsorption properties. Herein, molecular dynamics (MD) calculations evaluated the solvent dynamics of nine neat solvents with a broad range of chemical identities and experimental density measurements probed the time-dependent adsorption of these solvents into the ZIF-8 metal-organic frameworks (MOFs) to form PL dispersions over three weeks. Two of the nine dispersion, using glyceryl triacetate and 2’-hydroxyacetopheneone as solvents, presented sufficiently low solvent infiltration—characterized by solvent adsorption less than 40% of the ZIF-8 pore volume—to be viable as PLs. The experimental solvent adsorption data for ZIF-8 dispersed in the four aromatic solvents (acetophenone, methylbenzoate, 2-isopropylphenol, and 2’-hydroxyacetophenone) was fit to a kinetic model to quantify rate of solvent adsorption into ZIF-8. The rates of adsorption of these four similar-sized solvents demonstrated that solvent adsorption is slowed and limited by solvent clustering dynamics that are, in turn, driven by intermolecular hydrogen bonding. Ultimately, experimental solvent sorption measurements indicated that glyceryl triacetate and 2’-hydroxyacetophenone with ZIF-8 formed PLs with the most stable porosity and the greatest potential for gas capture. In conclusion, the combined experimental-computational materials exploration framework used here reveals a novel relationship between molecular scale solvent dynamics and macroscale solvent adsorption kinetics critical for the discovery and rapid evaluation of Type 3 PL compositions.
The influence of stacking faults on mechanical behavior of advanced materials
We report there are abundant studies on deformation mechanisms in metallic materials dominated by grain boundaries and twin boundaries. In comparison, the influence of stacking faults on deformation mechanisms of materials remains less well understood. Recent studies have shown that when the density of stacking faults increases sharply, their influence on mechanical behavior of metallic or ceramic materials becomes prominent, and in fact, dominates in some cases. This article reviews recent research progress on how nanoscale stacking faults impact the mechanical behavior of metallic and ceramic materials. We primarily focus on studies that reveal the formation of pre-existing stacking faults, their interactions with dislocations, and phase transformations during plastic deformation as evidenced by experiments and simulations. The aim of this review is to highlight the potential opportunities in using stacking faults to tailor the deformation mechanisms of advanced materials.
Mono-material product design with bio-based, circular, and biodegradable polymers
The traditional multi-material product design of plastic products significantly complicates both existing and emerging recycling processes. Here, we discuss the concept of mono-material product design, which is based on circular and biodegradable polymers made of a single monomer, delivering tailorable properties via molecular or macromolecular engineering without changing its chemical makeup or composition.
Physics-informed graph neural networks for predicting cetane number with systematic data quality analysis
Designing alternative fuels for advanced compression ignition engines necessitates a predictive model for cetane number (CN). In this study, the physics-informed graph neural networks are introduced for a reliable CN prediction by considering molecular features pertinent to the physical properties of molecules that affect CN. The reliability of measured data is another key factor to consider for improving the predictive model. Various experimental instruments for measuring CN exist, including standard and non-standard methods. In this regard, a systematic data quality analysis was carried out for the total 630 CNs collected from literature and new measurements in this study using Advanced Fuel Ignition Delay Analyzer (AFIDA). The results from this data curation process were reflected in the model by imposing lower sample weights on the data coming from less reliable measurement techniques. This approach effectively maximized the prediction accuracy while incorporating data from all available sources. Using the sample weights decreased the mean absolute error (MAE) up to 0.8 CN units. The accuracy was also improved by introducing the CN-related physical properties (the number of hydrogen bond donors and acceptors); the test set MAE is 5.74 and 7.01 for the model with and without such properties, respectively. Investigating molecular structural effects on CN was also carried out to gain chemical insights into factors used to design new fuel candidates. The dimensionality reduction analysis of feature vectors showed a clear clustering in terms of functional groups and CN and the structural effect derived from the model was consistent with the physicochemical insights. Finally, this physics-informed model and data curation would be helpful for accurate CN prediction and inform rational fuel design.
Chiral Induced Spin Selectivity Gives a New Twist on Spin-Control in Chemistry
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Chemical Bonding Governs Complex Magnetism in MnPt 5 P
Subtle changes in chemical bonds may result in dramatic revolutions in magnetic properties in solid-state materials. MnPt 5 P, a derivative of the rare-earth-free ferromagnetic MnPt 5 As, was discovered and is presented in this work. Specifically, MnPt 5 P was synthesized, and its crystal structure and chemical composition were characterized by X-ray diffraction as well as energy-dispersive X-ray spectroscopy. Accordingly, MnPt 5 P crystallizes in the layered tetragonal structure with the space group P4/mmm (No. 123), in which the face-shared Mn@Pt 12 polyhedral layers are separated by P layers. In contrast to the ferromagnetism observed in MnPt 5 As, the magnetic properties measurements on MnPt 5 P show antiferromagnetic ordering occurs at ~188 K with a strong magnetic anisotropy in and out of the ab-plane. Moreover, a spin-flop transition appears when a high magnetic field is applied. An A-type antiferromagnetic structure was obtained from the analysis of powder neutron diffraction (PND) patterns collected at 150 and 9 K. Calculated electronic structures imply that hybridization of Mn-3d and Pt-5d orbitals is critical for both the structural stability and observed magnetic properties. Semiempirical molecular orbitals calculations on both MnPt 5 P and MnPt 5 As indicate that the lack of 4p character on the P atoms at the highest occupied molecular orbital (HOMO) in MnPt 5 P may cause the different magnetic behavior in MnPt 5 P compared to MnPt 5 As. The discovery of MnPt 5 P, along with our previously reported MnPt5As, parametrizes the end points of a tunable system to study the chemical bonding which tunes the magnetic ordering from ferromagnetism to antiferromagnetism with the strong spin–orbit coupling (SOC) effect.
Trans-Influence in Dinuclear Pt(III) Complexes: Electronic Structure, σ-Donation, and Pt–Pt Spin–Spin Coupling
This study investigates the trans influence in dinuclear platinum(III) complexes using a combined approach of ab initio molecular dynamics and natural localized molecular orbital (NLMO) analysis. Focusing on pivalamidate-bridged Pt III complexes with axial ligands of varying σ-donation strength, it is quantified how ligand−metal interactions propagate through the Pt−Pt bond, and how they affect bond polarization, axial water coordination, and 1 J PtPt spin−spin coupling constants. NLMO analysis reveals quantitatively that strong σ-donating ligands polarize the Pt−Pt bond, shifting the electron density toward the opposite platinum center. The polarization mechanism is identified as the primary reason for the observed reduction of 1 J PtPt , because the bond polarization diminishes the transmission of the nuclear magnetic spin-induced electron spin density through the Pt−Pt bond. Additionally, the destabilization of axial water coordination at the opposite Pt site can be rationalized through a polarizationinduced Pt IV − Pt II -like mixed-valence character.
Chiral Four-Wave Mixing Signals with Circularly Polarized X-ray Pulses
Chiral four-wave mixing signals are calculated using the irreducible tensor formalism. Different polarization and crossing angle configurations allow to single out the magnetic dipole and the electric quadrupole interactions. Other configurations can reveal that the chiral interaction occurs at a given step within the nonlinear interaction pathways. Applications are made to the study of valence excitations of S-ibuprofen by chiral stimulated X-ray Raman signals at the carbon K-edge and by chiral visible 2D electronic spectroscopy.
Spatially Resolved Mapping of Three-Dimensional Molecular Orientations with ~2 nm Spatial Resolution through Tip-Enhanced Raman Scattering
We record local optical field images of silver nanocubes (75 nm) using tip-enhanced Raman (TER) spectral imaging. The images that we observe are consistent with several recent reports from our group, but here, we demonstrate sub-2 nm spatial resolution in local optical field nanoimaging under ambient laboratory conditions. This is achieved by scanning the substrate (nanocube on Si) relative to a 4-thiobenzonitrile (TBN)-functionalized Ag-coated TER probe. The spatial resolution that we obtain necessitates that only a few molecules govern the recorded optical response; molecular orientation becomes an important consideration in such measurements. We model the orientation through geometry optimization of a TBN molecule chemisorbed onto an Ag79 cluster (sphere with a ~1 nm diameter). Using the computed orientation of the cluster-bound molecule, we then model the optical response using formalism that accounts for the orientation of the molecule relative to vector components of the local optical fields. We find optimal agreement between experiment and theory. In effect, this work reveals the parallels between single-molecule Raman scattering and high-spatial-resolution TER spectroscopy, even when the images themselves cannot be used to visualize a single molecule in real space.
Properties of Erythritol Tetranitrate from Molecular Dynamics Simulation
Not Available
Molecular Parameters Governing the Elastic Properties of Brush Particle Films
Not provided.
Single-Molecule Conductance through Hybrid Radially and Linearly π-Conjugated Macromolecules Reveals an Unusual Intramolecular π-Interaction
We describe the design, synthesis, and single-molecule junction conductance of π-electron molecules bearing both radial and linear π-conjugation pathways, whereby cycloparaphenylene (CPP) radial cores are π-extended linearly with aryl alkyne substituents as models for previously reported CPP-arylene ethynylene conjugated polymers. Although radially and linearly conjugated molecules have been studied previously in isolation as junction-bridging molecular electronic units, this is the first study to examine molecules where both topologies are operative. Our results reveal that the presence of radial CPP components within the junction-spanning pathway leads to a reduction in the conductance of the backbone compared to model linear phenyl substituents. Through tight-binding and DFT-based calculations, we attribute this conductance change to intramolecular van der Waals (vdW) interactions between the CPP ring and the junction-spanning arylene-ethynylene molecular backbone. These interactions induce changes in the dihedral angles of the backbone, leading to a reduced overlap of π orbitals within the molecular junction.
Engineering Edge States of Graphene Nanoribbons for Narrow-Band Photoluminescence
Solid-state narrow-band light emitters are on-demand for quantum optoelectronics. Current approaches based on defect engineering in low-dimensional materials usually introduce a broad range of emission centers. Here, we report narrow-band light emission from covalent heterostructures fused to the edges of graphene nanoribbons (GNRs) by controllable on-surface reactions from molecular precursors. Two types of heterojunction (HJ) states are realized by sequentially synthesizing GNRs and graphene nanodots (GNDs) and then coupling them together. HJs between armchair GNDs and armchair edges of the GNR are coherent and give rise to narrow-band photoluminescence. In contrast, HJs between the armchair GNDs and the zigzag ends of GNRs are defective and give rise to nonradiative states near the Fermi level. At low temperatures, sharp photoluminescence emissions with peak energy range from 2.03 to 2.08 eV and line widths of 2–5 meV are observed. Furthermore, the radiative HJ states are uniform, and the optical transition energy is controlled by the band gaps of GNRs and GNDs. As these HJs can be synthesized in a large quantity with atomic precision, this finding highlights a route to programmable and deterministic creation of quantum light emitters.
Bayesian Conavigation: Dynamic Designing of the Material Digital Twins via Active Learning
Scientific advancement is universally based on the dynamic interplay between theoretical insights, modeling, and experimental discoveries. However, this feedback loop is often slow, including delayed community interactions and the gradual integration of experimental data into theoretical frameworks. This challenge is particularly exacerbated in domains dealing with high-dimensional object spaces, such as molecules and complex microstructures. Hence, the integration of theory within automated and autonomous experimental setups, or theory in the loop-automated experiment, is emerging as a crucial objective for accelerating scientific research. The critical aspect is to use not only theory but also on-the-fly theory updates during the experiment. Furthermore, we introduce a method for integrating theory into the loop through Bayesian conavigation of theoretical model space and experimentation. Our approach leverages the concurrent development of surrogate models for both simulation and experimental domains at the rates determined by latencies and costs of experiments and computation, alongside the adjustment of control parameters within theoretical models to minimize epistemic uncertainty over the experimental object spaces. This methodology facilitates the creation of digital twins of material structures, encompassing both the surrogate model of behavior that includes the correlative part and the theoretical model itself. While being demonstrated here within the context of functional responses in ferroelectric materials, our approach holds promise for broader applications, such as the exploration of optical properties in nanoclusters, microstructure-dependent properties in complex materials, and properties of molecular systems.
Field-Driven Out-of-Equilibrium Collective Patterns for Swarm Micro-Robotics
Soft robotics has been rapidly advancing, offering significant improvements over traditional rigid robotic systems through the use of compliant materials that enhance adaptability and interaction with the environment. However, current approaches face critical challenges, including the reliance on complex “top-down” fabrication techniques and the difficulty of wireless powering and control at the microscale. Swarm robotics introduces a paradigm shift, leveraging collective dynamics to achieve cooperative and adaptable behaviors among multiple robotic units. Inspired by nature, this “bottom-up” approach enables swarm robots to execute task-specific reconfigurations, enhancing flexibility and robustness. Field-driven active colloids emerge as a promising platform for swarm microrobotics, capable of self-propulsion and self-organization into dynamic collective patterns under external field excitation and manipulation. These systems mimic biologically inspired swarm behaviors, such as flocking and vortex formation, providing a versatile foundation for designing innovative swarm microrobots. Here, this review discusses the principles of electric and magnetic field-driven collective self-organization, focusing on the particle dynamics, the emergence of collective swarm patterns, and illustrative examples of functional swarm microrobots. It concludes with future perspectives on harnessing these systems for adaptive, scalable, and multifunctional microrobotic applications.
Validated Reactive Force Field Quantifies MXene Interfacial Properties, Mechanics, and Thermal Transport
MXenes combine rich surface chemistry, mechanical strength, and high conductivity for a multitude of emerging applications. Predictive modeling supports accelerated materials designs and has been limited by the absence of validated and transferable force fields. Here, we introduce an interpretable, reactive INTERFACE force field (IFF and IFF-R) for Ti 3 C 2 T x MXenes that is trained based on chemical knowledge and achieves quantitative agreement with experiments across lattice parameters (<0.5%), density (<0.2%), liquid contact angles, Raman spectra, and the in-plane elastic modulus (∼320 GPa). The models cover surface terminations from hydroxyl (−OH) to fluorine (−F) groups and are extensible to other chemistries. We introduce pH-resolved surface chemistry and identify dopamine adsorption mechanisms at MXene–aqueous interfaces supported by QCM-D and UV–Vis experiments. The data reveal coplanar and perpendicular binding modes and concentration-dependent multilayer assembly. We predict previously inaccessible properties, including termination-dependent cleavage energies, interlayer shear moduli and dynamic shear failure, nanoindentation and brittle fracture, anisotropic in-plane and out-of-plane thermal conductivities, including the role of defects. Agreement with available experimental data is consistently close and exceeds DFT accuracy across the benchmark properties examined. The IFF/IFF-R model is compatible with CHARMM, AMBER, OPLS, and CVFF force fields for simulations of MXenes with diverse surface terminations, electrolyte interfaces, biointerfaces, and polymer composites without additional parameters. Parameter sets, 3D models, and analysis scripts are provided for community use. The validated, reactive, and transferable IFF framework facilitates predictive design of MXene-based films, membranes, sensing interfaces, and composites.