Nanoparticle Assembly and Oriented Attachment: Correlating Controlling Factors to the Resulting Structures

Graphene-Assisted Electron-Based Imaging of Individual Organic and Biological Macromolecules: Structure and Transient Dynamics

Characterizing the structures, interactions, and dynamics of molecules in their native liquid state is a long-existing challenge in chemistry, molecular science, and biophysics with profound scientific significance. Advanced transmission electron microscopy (TEM)-based imaging techniques with the use of graphene emerged as promising tools, mainly due to their performance on spatial and temporal resolution. This review focuses on the various approaches to achieving high-resolution imaging of individual molecules and their transient interactions. We highlight the crucial role of graphene grids in cryogenic electron microscopy for achieving Ångstrom-level resolution for resolving molecular structures and the importance of graphene liquid cells in liquid-phase TEM for directly observing dynamics with subnanometer resolution at a frame rate of several frames per second, as well as the cross-talks of the two imaging modes. To understand the chemistry and physics encoded in these molecular movies, incorporating machine learning algorithms for image analysis provides a promising approach that further bolsters the resolution adventure. Besides reviewing the recent advances and methodologies in TEM imaging of individual molecules using graphene, this review also outlines future directions to improve these techniques and envision problems in molecular science, chemistry, and biology that could benefit from these experiments.

Unravelling complex mechanisms in materials processes with cryogenic electron microscopy

Investigating nanoscale structural variations, including heterogeneities, defects, and interfacial characteristics, is crucial for gaining insight into material properties and functionalities. Cryogenic electron microscopy (cryo-EM) is developing as a powerful tool in materials science particularly for non-invasively understanding nanoscale structures of materials. These advancements bring us closer to the ultimate goal of correlating nanoscale structures to bulk functional outcomes. However, while understanding mechanisms from structural information requires analysis that closely mimics operation conditions, current challenges in cryo-EM imaging and sample preparation hinder the extraction of detailed mechanistic insights. In this Perspective, we discuss the innovative strategies and the potential for using cryo-EM for revealing mechanisms in materials science, with examples from high-resolution imaging, correlative elemental analysis, and three-dimensional and time-resolved analysis. Furthermore, we propose improvements in cryo-sample preparation, optimized instrumentation setup for imaging, and data interpretation techniques to enable the wider use of cryo-EM and achieve deeper context into materials to bridge structural observations with mechanistic understanding.

Non-Reciprocity, Metastability, and Dynamic Reconfiguration in Co-Assembly of Active and Passive Particles

Living organisms often exhibit non-reciprocal interactions where the forces acting on the objects are not equal in magnitude or opposite in direction. The combination of reciprocal and non-reciprocal interactions between synthetic building blocks remains largely unexplored. Here, out-of-equilibrium assemblies of non-motile isotropic passive and metal-patched motile active particles are formed by overlapping bulk interactions with directed self-propulsion. An external alternating current (AC) electric field generates concurrent dipolar and induced-charge electrophoretic forces between the particles which are evaluated using microscopy. The interaction force measurements allow to determine the degree of reciprocity in interactions, which is tunable by designing the active particle and its trajectory. While linearly-propelled active particles evade assembly with passive particles, helically propelled active particles form active-passive clusters with dynamic reconfiguration and long-lived metastability. Large clusters display programmable fluctuations and reconfigurability by controlling the fraction of active particles. The study establishes principles of integrating reciprocal and non-reciprocal interactions in guided colloidal assembly of reconfigurable metastable structures.

Crystallization of 2D TiO2 Nanosheets via Oriented Attachment of 1D Coordination Polymer

Demystifying the molecular mechanism of growth is vital for the rational design, synthesis, and optimization of functional nanomaterials. Despite the promising perspectives and extensive efforts, the growth mechanism of atomically thin TiO2(B) nanosheets remains unclear, hence it is difficult to tune their band and surface structures. Herein, we report an oriented attachment-based crystallization mechanism of TiO2(B) nanosheets from a 1D titanium glycolate coordination polymer through hydrolysis and condensation. With time-tracking experiments, this 1D coordination polymer is found to be an intermediate in the synthesis of TiO2(B) nanosheets by using Ti alkoxides and chlorides as precursors, suggesting the universality of the 1D-to-2D growth mechanism. Such a side-to-side attachment pathway bridges the classical and nonclassical interpretations of crystallization, and meanwhile hints at the possibility of other 1D complexes as potential precursors for 2D materials.

Taming Two-Dimensional Polymerization by a Machine-Learning Discovered Crystallization Model

Rapidly synthesizing high-quality two-dimensional covalent organic frameworks (2D COFs) is crucial for their practical applications. While strategies such as slow monomer addition have been developed based on an empirical understanding of their formation process, quantitative guidance remains absent, which prohibits precise optimizations of the experimental conditions. Here, we use a machine-learning approach that overcomes the challenges associated with bottom-up model derivation for the non-classical 2D COF crystallization processes. The resulting model, referred to as NEgen1, establishes correlations among the induction time, nucleation rate, growth rate, bond-forming rate constants, and common solution synthesis conditions for 2D COFs that grow by a nucleation-elongation mechanism. The results elucidate the detailed competition between the nucleation and growth dynamics in solution, which has been inappropriately described previously by classical, empirical models with assumptions invalid for 2D COF polymerization. By understanding the dynamic processes at play, the NEgen1 model reveals a simple strategy of gradually increasing monomer addition speed for growing large 2D COF crystals. This insight enables us to rapidly synthesize large COF-5 colloids, which could only be achieved previously by prolonged reaction times or by introducing chemical modulators. These results highlight the potential for systematically improving the crystal quality of 2D COFs, which has wide-reaching relevance for many of the applications where 2D COFs are speculated to be valuable.

Ion Correlations Decrease Particle Aggregation Rate by Increasing Hydration Forces at Interfaces

The connection between solution structure, particle forces, and emergent phenomena at solid-liquid interfaces remains ambiguous. In this case study on boehmite aggregation, we established a connection between interfacial solution structure, emerging hydration forces between two approaching particles, and the resulting structure and kinetics of particle aggregation. In contrast to expectations from continuum-based theories, we observed a nonmonotonic dependence of the aggregation rate on the concentration of sodium chloride, nitrate, or nitrite, decreasing by 15-fold in 4 molal compared to 1 molal solutions. These results are accompanied by an increase in repulsive hydration forces and interfacial oscillatory features from 0.27-0.31 nm in 0.01 molal to 0.38-0.52 nm in 2 molal. Moreover, molecular dynamics (MD) simulations indicated that these changes correspond to enhanced ion correlations near the interface and produced loosely bound aggregates that retain electrolyte between the particles. We anticipate that these results will enable the prediction of particle aggregation, attachment, and assembly, with broad relevance to interfacial phenomena.

Role of Solvents in Oriented Attachment of Ag Nanoparticles: Insights from Molecular Dynamics Simulations and Topological Analysis

Although the solvation force is considered one of the key forces behind the oriented attachment (OA), the precise roles of solvents in this process remain incompletely elucidated. In this study, we examined the effect of solvent polarities (water, acetone, and chloroform) on the attachment of silver nanoparticles by calculating the free energy curves for the OA process. The observed magnitudes of the binding energies and approaching and dissociation energy barriers are commensurate with the respective solvent polarities. Consequently, OA is more likely to occur in acetone with an intermediate permittivity relative to that of water and chloroform. Additionally, we identified a topological descriptor, namely, the Euler characteristic, of the solvent network, especially the water network, between two approaching surfaces, which manifests a linear correlation with the observed free energy profiles. This descriptor holds promise as a quantitative tool for predicting interactions between nanoparticles in solvent environments featuring hydrogen bond networks.

Effect of Solvent Composition on Non-DLVO Forces and Oriented Attachment of Zinc Oxide Nanoparticles

Oriented attachment (OA) occurs when nanoparticles in solution align their crystallographic axes prior to colliding and subsequently fuse into single crystals. Traditional colloidal theories such as DLVO provide a framework for evaluating OA but fail to capture key particle interactions due to the atomistic details of both the crystal structure and the interfacial solution structure. Using zinc oxide as a model system, we investigated the effect of the solvent on short-ranged and long-ranged particle interactions and the resulting OA mechanism. In situ TEM imaging showed that ZnO nanocrystals in toluene undergo long-range attraction comparable to 1kT at separations of 10 nm and 3kT near particle contact. These observations were rationalized by considering non-DLVO interactions, namely, dipole-dipole forces and torques between the polar ZnO nanocrystals. Langevin dynamics simulations showed stronger interactions in toluene compared to methanol solvents, consistent with the experimental results. Concurrently, we performed atomic force microscopy measurements using ZnO-coated probes for the short-ranged interaction. Our data are relevant to another type of non-DLVO interaction, namely, the repulsive solvation force. Specifically, the solvation force was stronger in water compared to ethanol and methanol, due to the stronger hydrogen bonding and denser packing of water molecules at the interface. Our results highlight the importance of non-DLVO forces in a general framework for understanding and predicting particle aggregation and attachment.

Dansyl-tagged xanthate ester as a capping agent to synthesize fluorescent silver nanoparticles with binding affinity toward serum albumin

Developing multifunctional nanomaterials with distinct photochemical properties, such as high quantum yield, improved photostability, and good biocompatibility is critical for a wide range of biomedical applications. Motivated by this, we designed and synthesized a dansyl-tagged xanthate-based capping agent (DX) for the synthesis of fluorescent silver nanoparticles (AgNPs). The capping agent DX was characterized by 1H and 13C-NMR, LC-MS, and FT-IR. The synthesized DX-capped fluorescent AgNPs were thoroughly characterized by UV-visible spectroscopy, fluorescence spectroscopy, field emission scanning electron microscope (FE-SEM), transmission electron microscope (TEM), dynamic light scattering (DLS), and zeta potential. The fluorescent AgNPs showed distinct surface plasmon resonance absorption at λmax = 414 nm, fluorescence at λmax = 498 nm, quantum yield = 0.24, zeta potential = +18.6 mV, average size = 18.2 nm. Furthermore, the biological activity of the fluorescent AgNPs was validated by its interaction with the most abundant protein in the blood, that is, BSA (Bovine serum albumin) and HSA (Human serum albumin) with binding constant of 2.34 × 104 M-1 and 2.14 × 104 M-1 respectively. Interestingly, fluorescence resonance energy transfer (FRET) was observed between the fluorescent AgNPs and BSA/HSA with a FRET efficiency of 77.23% and 56.36%, respectively, indicating strong interaction between fluorescent AgNPs and BSA/HSA.

Nanocrystal Assemblies: Current Advances and Open Problems

We explore the potential of nanocrystals (a term used equivalently to nanoparticles) as building blocks for nanomaterials, and the current advances and open challenges for fundamental science developments and applications. Nanocrystal assemblies are inherently multiscale, and the generation of revolutionary material properties requires a precise understanding of the relationship between structure and function, the former being determined by classical effects and the latter often by quantum effects. With an emphasis on theory and computation, we discuss challenges that hamper current assembly strategies and to what extent nanocrystal assemblies represent thermodynamic equilibrium or kinetically trapped metastable states. We also examine dynamic effects and optimization of assembly protocols. Finally, we discuss promising material functions and examples of their realization with nanocrystal assemblies.

In Situ TEM Tracking of Disconnection Motion and Atomic Cooperative Reorientation in the Oriented Attachment of Pt Nanoparticles

The oriented attachment (OA) of nanoparticles (NPs) is an important crystal growth mechanism in many materials. However, a comprehensive understanding of the atomic-scale alignment and attachment processes is still lacking. We conducted in situ atomic resolution studies using high-resolution transmission electron microscopy to reveal how two Pt NPs coalesce into a single particle via OA, which involves the formation of atomic-scale links and a grain boundary (GB) between the NPs, as well as GB migration. Density functional theory calculations showed that the system energy changes as a function of the number of disconnections during the coalescence process. Additionally, the formation and annihilation processes of disconnection are always accompanied by the cooperative reorientation motion of atoms. These results further elucidate the growth mechanism of OA at the atomic scale, providing microscopic insights into OA dynamics and a framework for the development of processing strategies for nanocrystalline materials.

Molecular Mechanisms of Sorbed Ion Effects during Boehmite Particle Aggregation

Classical theories of particle aggregation, such as Derjaguin-Landau-Verwey-Overbeek (DLVO), do not explain recent observations of ion-specific effects or the complex concentration dependence for aggregation. Thus, here, we probe the molecular mechanisms by which selected alkali nitrate ions (Na+, K+, and NO3-) influence aggregation of the mineral boehmite (γ-AlOOH) nanoparticles. Nanoparticle aggregation was analyzed using classical molecular dynamics (CMD) simulations coupled with the metadynamics rare event approach for stoichiometric surface terminations of two boehmite crystal faces. Calculated free energy landscapes reveal how electrolyte ions alter aggregation on different crystal faces relative to pure water. Consistent with experimental observations, we find that adding an electrolyte significantly reduces the energy barrier for particle aggregation (∼3-4×). However, in this work, we show this is due to the ions disrupting interstitial water networks, and that aggregation between stoichiometric (010) basal-basal surfaces is more favorable than between (001) edge-edge surfaces (∼5-6×) due to the higher interfacial water densities on edge surfaces. The interfacial distances in the interlayer between aggregated particles with electrolytes (∼5-10 Å) are larger than those in pure water (a few Ångströms). Together, aggregation/disaggregation in salt solutions is predicted to be more reversible due to these lower energy barriers, but there is uncertainty on the magnitudes of the energies that lead to aggregation at the molecular scale. By analyzing the peak water densities of the first monolayer of interstitial water as a proxy for solvent ordering, we find that the extent of solvent ordering likely determines the structures of aggregated states as well as the energy barriers to move between them. The results suggest a path for developing a molecular-level basis to predict the synergies between ions and crystal faces that facilitate aggregation under given solution conditions. Such fundamental understanding could be applied extensively to the aggregation and precipitation utilization in the biological, pharmaceutical, materials design, environmental remediation, and geological regimes.

High-Throughput Computational Screening of Solid-Binding Peptides

Inspired by biomineralization, a naturally occurring, protein-facilitated process, solid-binding peptides (SBPs) have gained much attention for their potential to fabricate various shaped nanocrystals and hierarchical nanostructures. The advantage of SBPs over other traditionally used synthetic polymers or short ligands is their tunable interaction with the solid material surface via carefully programmed sequence and being solution-dependent simultaneously. However, designing a sequence with targeted binding affinity or selectivity often involves intensive processes such as phage display, and only a limited number of sequences can be identified. Other computational efforts have also been introduced, but the validation process remains prohibitively expensive once a suitable sequence has been identified. In this paper, we present a new model to rapidly estimate the binding free energy of any given sequence to a solid surface. We show how the overall binding of a polypeptide can be estimated from the free energy contribution of each residue based on the statistics of the thermodynamically stable structure ensemble. We validated our model using five silica-binding peptides of different binding affinities and lengths and showed that the model is accurate and robust across a wider range of chemistries and binding strengths. The computational cost of this method can be as low as 3% of the commonly used enhanced sampling scheme for similar studies and has a great potential to be used in high-throughput algorithms to obtain larger training data sets for machine learning SBP screening.

Unraveling the Complex Interactions: Machine Learning Approaches to Predict Bacterial Survival against ZnO and Lanthanum-Doped ZnO Nanoparticles

The rise in antibiotic-resistant bacteria is a global health challenge. Due to their unique properties, metal oxide nanoparticles show promise in addressing this issue. However, optimizing these properties requires a deep understanding of complex interactions. This study incorporated data-driven machine learning to predict bacterial survival against lanthanum-doped ZnO nanoparticles. The effect of incorporation of lanthanum ions on ZnO was analyzed. Even with high lanthanum concentration, no significant variations in structural, morphological, and optical properties were observed. The antibacterial activity of La-doped ZnO nanoparticles against Gram-positive and Gram-negative bacteria was qualitatively and quantitatively evaluated. Nanoparticles induce 60%, 95%, and 55% bacterial death against Escherichia coli, Pseudomonas aeruginosa, and Staphylococcus aureus, respectively. Algorithms such as Multilayer Perceptron, K-Nearest Neighbors, Gradient Boosting, and Extremely Random Trees were used to predict the bacterial survival percentage. Extremely Random Trees performed the best among these models with 95.08% accuracy. A feature relevance analysis extracted the most significant attributes to predict the bacterial survival percentage. Lanthanum content and particle size were irrelevant, despite what can be assumed. This approach offers a promising avenue for developing effective and tailored strategies to reduce the time and cost of developing antimicrobial nanoparticles.

Towards predictive control of reversible nanoparticle assembly with solid-binding proteins

Although a broad range of ligand-functionalized nanoparticles and physico-chemical triggers have been exploited to create stimuli-responsive colloidal systems, little attention has been paid to the reversible assembly of unmodified nanoparticles with non-covalently bound proteins. Previously, we reported that a derivative of green fluorescent protein engineered with oppositely located silica-binding peptides mediates the repeated assembly and disassembly of 10-nm silica nanoparticles when pH is toggled between 7.5 and 8.5. We captured the subtle interplay between interparticle electrostatic repulsion and their protein-mediated short-range attraction with a multiscale model energetically benchmarked to collective system behavior captured by scattering experiments. Here, we show that both solution conditions (pH and ionic strength) and protein engineering (sequence and position of engineered silica-binding peptides) provide pathways for reversible control over growth and fragmentation, leading to clusters ranging in size from 25 nm protein-coated particles to micrometer-size aggregate. We further find that the higher electrolyte environment associated with successive cycles of base addition eventually eliminates reversibility. Our model accurately predicts these multiple length scales phenomena. The underpinning concepts provide design principles for the dynamic control of other protein- and particle-based nanocomposites.

Effects of particle shape and surface roughness on van der Waals interactions and coupling to dynamics in nanocrystals

The van der Waals interaction between colloids and nanoparticles is one of the key components to understanding particle aggregation, attachment, and assembly. While the ubiquity of anisotropic particle shapes and surface roughness is well-recognized in nanocrystalline materials, the effects of both on van der Waals forces and torques have not been adequately investigated. In this study, we develop a numerical scheme to determine the van der Waals forces and torques between cubic particles with multiple configurations and relative orientations. Our results show that the van der Waals torque due to anisotropic particle shapes is appreciable at nearly all configurations and mutual angles, outcompeting Brownian torque for various materials systems and conditions. Surface roughness enhances this particle shape effect, resulting in stronger van der Waals interactions ascribed to protrusions on the surfaces. Moreover, a scaling analysis indicates that the surface roughness alters the separation dependence of the van der Waals force and, more importantly, significantly influences the dynamics of two approaching particles. Our results clearly demonstrate that surface roughness and anisotropic shape play a crucial role in the energetics and kinetics of various particle-scale and emergent phenomena, such as crystal growth by oriented attachment, nanomaterials synthesis and assembly, mud flow rheology, as well as the deposition of natural nanocrystals within the subsurface.

In Situ Transmission Electron Microscopy Investigation on Oriented Attachment of Nanodiamonds

Oriented attachment (OA) plays an important role in the assembly of nanoparticles and the regulation of their size and morphology, which is expected to be an effective means to modulate the properties of nanodiamonds (NDs). However, there remains a dearth of comprehensive investigation into the OA mechanism of NDs. Using in situ transmission electron microscopy, we conducted atomic-resolution investigation on the OA events of ND pairs under electron beam irradiation. The occurrence of an OA event is contingent upon the alignment between two ND surfaces, and the coalesced particles undergo recrystallization to form spherical shapes. Both experimental observations and molecular dynamics (MD) simulations reveal that ND pairs exhibit a preference for coalescing along the {111} surfaces. Additionally, MD simulations indicate that kinetic factors, such as contact surface area and contact angle, also influence the coalescence process.

Coupling of Nd doping and oxygen-rich vacancy in CoMoO4@NiMoO4 nanoflowers toward advanced supercapacitors and photocatalytic degradation

In this paper, we successfully prepared rare earth element-doped 0.8% Nd-CoMoO4@NiMoO4 nanoflowers with a large specific surface area using the sol-gel method for the first time. In the experiment, we added a structure-directing agent to successfully assemble the nanosheets into a three-dimensional ordered micro-flower shape. By using the strategy of forming a flower-shaped morphology with a structure-directing agent and doping Nd elements to generate oxygen vacancies, the problems of the collapse of the active material structure and slow reaction kinetics were solved. Through relevant electrochemical performance tests, it was found that when the rare earth element Nd was doped at a concentration of 0.8%, the material exhibited exceptional specific capacitance (2387 F g-1 at 1 A g-1) and cycling stability (99.3% after 10 000 cycles at 5 A g-1). These performance characteristics far surpassed those of the other synthesized products. We assembled 0.8% Nd-CoMoO4@NiMoO4 with hydrophilic CNTs into an asymmetric device, 0.8% Nd-CoMoO4@NiMoO4//CNTs. This device exhibited high specific capacitance (262 F g-1 at 1 A g-1) and cycling stability (99.2% after 3000 cycles), with a good energy storage effect. In addition, 0.8% Nd-CoMoO4@NiMoO4 has a low band gap, which broadens the absorption range of the product and improves the utilization rate of visible light. The photocatalyst showed good degradation efficiency (all exceeding 96%) and cycling stability (96%) for all four dyes. This paper provides a new strategy and method for preparing doped polymetallic mixtures, which has potential application value.

Programmable Modular Acoustic Microrobots

Microrobots have emerged as promising tools for biomedical and in vivo applications, leveraging their untethered actuation capabilities and miniature size. Despite extensive research on diversifying multi-actuation modes for single types of robots, these tiny machines tend to have limited versatility while navigating different environments or performing specific tasks. To overcome such limitations, self-assembly microstructures with on-demand reconfiguration capabilities have gained recent attention as the future of biocompatible microrobotics, as they can address drug delivery, microsurgery, and organoid development processes. Reversible modular reconfiguration structures require specific arrangements of particles that can assume several shapes when external fields are applied. We show how magnetic interaction can be used to assemble cylindrical microrobots into modular microstructures with different shapes. The motion actuation of the formed microstructure happens due to an external acoustic field, which generates responsive forces in the air bubbles trapped in the inner cavity of the robots. An external magnetic field can also steer these structures. We illustrate these capabilities by assembling the robots into different shapes that can swim and be steered, showing the potential to perform biomedical applications. Furthermore, we confirm the biocompatibility of the cylindrical microrobot used as the building blocks of our microstructure. Exposing Chinese Hamster Ovary cells to our microrobots for 24 hours demonstrates cell viability when in contact with the microrobot.

Understanding ZIF particle chemical etching dynamics and morphology manipulation: in situ liquid phase electron microscopy and 3D electron tomography application

In situ liquid phase transmission electron microscopy (TEM) and three-dimensional electron tomography are powerful tools for investigating the growth mechanism of MOFs and understanding the factors that influence their particle morphology. However, their combined application to the study of MOF etching dynamics is limited due to the challenges of the technique such as sample preparation, limited field of view, low electron density, and data analysis complexity. In this research, we present a study employing in situ liquid phase TEM to investigate the etching mechanism of colloidal zeolitic imidazolate framework (ZIF) nanoparticles. The etching process involves two distinct stages, resulting in the development of porous structures as well as partially and fully hollow morphologies. The etching process is induced by exposure to an acid solution, and both in situ and ex situ experiments demonstrate that the outer layer etches faster leading to overall volume shrinking (stage I) while the inner layer etches faster giving a hollow morphology (stage II), although both the outer layer and inner layer have been etched in the whole process. 3D electron tomography was used to quantify the properties of the hollow structures which show that the ZIF-67 crystal etching rate is larger than that of the ZIF-8 crystal at the same pH value. This study provides valuable insights into MOF particle morphology control and can lead to the development of novel MOF-based materials with tailored properties for various applications.

Bubble-Mediated Large-Scale Hierarchical Assembly of Ultrathin Pt Nanowire Network Monolayer at Gas/Liquid Interfaces

Extensive macroscale two-dimensional (2-D) platinum (Pt) nanowire network (NWN) sheets are created through a hierarchical self-assembly process with the aid of biomolecular ligands. The Pt NWN sheet is assembled from the attachment growth of 1.9 nm-sized 0-D nanocrystals into 1-D nanowires featuring a high density of grain boundaries, which then interconnect to form monolayer network structures extending into centimeter-scale size. Further investigation into the formation mechanism reveals that the initial emergence of NWN sheets occurs at the gas/liquid interfaces of the bubbles produced by sodium borohydride (NaBH₄) during the synthesis process. Upon the rupture of these bubbles, an exocytosis-like process releases the Pt NWN sheets at the gas/liquid surface, which subsequently merge into a continuous monolayer Pt NWN sheet. The Pt NWN sheets exhibit outstanding oxygen reduction reaction (ORR) activities, with specific and mass activities 12.0 times and 21.2 times greater, respectively, than those of current state-of-the-art commercial Pt/C electrocatalysts.

Metal-Ligand Interactions and Their Roles in Controlling Nanoparticle Formation and Functions

ConspectusFunctional nanoparticles (NPs) have been studied extensively in the past decades for their unique nanoscale properties and their promising applications in advanced nanosciences and nanotechnologies. One critical component of studying these NPs is to prepare monodisperse NPs so that their physical and chemical properties can be tuned and optimized. Solution phase reactions have provided the most reliable processes for fabricating such monodisperse NPs in which metal-ligand interactions play essential roles in the synthetic controls. These interactions are also key to stabilizing the preformed NPs for them to show the desired electronic, magnetic, photonic, and catalytic properties. In this Account, we summarize some representative organic bipolar ligands that have recently been explored to control NP formation and NP functions. These include aliphatic acids, alkylphosphonic acids, alkylamines, alkylphosphines, and alkylthiols. This ligand group covers metal-ligand interactions via covalent, coordination, and electrostatic bonds that are most commonly employed to control NP sizes, compositions, shapes, and properties. The metal-ligand bonding effects on NP nucleation rate and growth can now be more thoroughly investigated by in situ spectroscopic and theoretical studies. In general, to obtain the desired NP size and monodispersity requires rational control of the metal/ligand ratios, concentrations, and reaction temperatures in the synthetic solutions. In addition, for multicomponent NPs, the binding strength of ligands to various metal surfaces needs to be considered in order to prepare these NPs with predesigned compositions. The selective ligand binding onto certain facets of NPs is also key to anisotropic growth of NPs, as demonstrated in the synthesis of one-dimensional nanorods and nanowires. The effects of metal-ligand interactions on NP functions are discussed in two aspects, electrochemical catalysis for CO₂ reduction and electronic transport across NP assemblies. We first highlight recent advances in using surface ligands to promote the electrochemical reduction of CO₂. Several mechanisms are discussed, including the modification of the catalyst surface environment, electron transfer through the metal-organic interface, and stabilization of the CO₂ reduction intermediates, all of which facilitate selective CO₂ reduction. These strategies lead to better understanding of molecular level control of catalysis for further catalyst optimization. Metal-ligand interaction in magnetic NPs can also be used to control tunneling magnetoresistance properties across NPs in NP assemblies by tuning NP interparticle spacing and surface spin polarization. In all, metal-ligand interactions have yielded particularly promising directions for tuning CO₂ reduction selectivity and for optimizing nanoelectronics, and the concepts can certainly be extended to rationalize NP engineering at atomic/molecular precision for the fabrication of sensitive functional devices that will be critical for many nanotechnological applications.

Theory of Anisotropic Metal Nanostructures

A significant challenge in the development of functional materials is understanding the growth and transformations of anisotropic colloidal metal nanocrystals. Theory and simulations can aid in the development and understanding of anisotropic nanocrystal syntheses. The focus of this review is on how results from first-principles calculations and classical techniques, such as Monte Carlo and molecular dynamics simulations, have been integrated into multiscale theoretical predictions useful in understanding shape-selective nanocrystal syntheses. Also, examples are discussed in which machine learning has been useful in this field. There are many areas at the frontier in condensed matter theory and simulation that are or could be beneficial in this area and these prospects for future progress are discussed.