Molecular-level Phenomena Research Articles

Role of Trap Optimization in a Heterostructure Carbon Nitride as a Methodology to Enhance Photocatalytic Hydrogen Production.

Carbon-based catalysts hold significant promise for photocatalytic hydrogen evolution. A critical challenge lies in optimizing the balance between electron longevity and its accumulation to avoid bottlenecks in photocatalytic efficiency. In this study, we introduce an innovative and efficient strategy for the rapid extraction (<100 fs) of photoinduced free electrons from a carbon-based catalyst without forming additional metal-based heterojunction hybrids. This method effectively prevents excessive accumulation of free carriers within the catalyst. The rapidly extracted electrons are then utilized for photocatalytic hydrogen production, resulting in a 10-fold increase in activity compared to the pristine catalyst, with platinum (3 wt%) used as a cocatalyst. Our strategy significantly enhances the performance of a state-of-the-art catalyst, offering a clean and cost-effective method for producing clean energy. This work demonstrates how a fundamental understanding of molecular-level phenomena and their optimization can pave the way for delivering clean and affordable energy to society.

Explainable Machine Learning Models to Predict Gibbs-Donnan Effect During Ultrafiltration and Diafiltration of High-Concentration Monoclonal Antibody Formulations.

Evaluating the Gibbs-Donnan and volume exclusion effects during protein ultrafiltration and diafiltration (UF/DF) is crucial in biopharmaceutical process development to precisely control the concentration of the drug substance in the final formulation. Understanding the interactions between formulation excipients and proteins under these conditions requires a domain-specific knowledge of molecular-level phenomena. This study developed gradient boosted tree models to predict the Gibbs-Donnan and volume exclusion effects for amino acids and therapeutic monoclonal antibodies using simple molecular descriptors. The models' predictions were interpreted by information gain and Shapley additive explanation (SHAP) values to understand the modes of action of the antibodies and excipients and to validate the models. The results translated feature effects in machine learning to real-world molecular interactions, which were cross-referenced with existing scientific literature for verification. The models were validated in pilot-scale manufacturing runs of two antibody products requiring high levels of concentration. By only requiring a molecule's biophysicochemical descriptors and process conditions, the proposed models provide an in silico alternative to conventional UF/DF experiments to accelerate process development and boost process understanding of the underlying molecular mechanisms through rational interpretation of the models.

Controlling molecular self-assembly of inverse-phosphocholine lipids at oxide interfaces with divalent cations

Sub-100 nm vesicles composed of inverse-phosphocholine (CP) lipids are versatile building blocks for interfacing with inorganic material surfaces and demonstrate unique chemical functionalities due to outward-facing phosphate groups. Compared to typical phospholipids, the surface presentation of CP lipids has enabled new covalent possibilities like click chemistry for drug delivery and imaging applications while utilizing this phosphate chemistry in noncovalent self-assembly contexts remains unexplored. Herein, we report that the presence of phosphate-binding divalent cations such as Ca2+ and Mg2+ can dramatically shift the self-assembly behavior of CP lipid vesicles on TiO2 and SiO2 surfaces, inhibiting spontaneous supported lipid bilayer (SLB) formation on TiO2 in cases where it typically occurs and promoting SLB formation on SiO2 in cases where lipid adsorption is otherwise negligible. Quartz crystal microbalance-dissipation (QCM-D) measurements were performed to track the corresponding adsorption kinetics and self-assembly outcomes. Importantly, an optimal combination of CP lipid fraction in vesicles and specific divalent cation type was also identified, which enabled SLB formation on both TiO2 and SiO2 surfaces under equivalent conditions. SLB fluidity on SiO2 surfaces was additionally confirmed by fluorescence recovery after photobleaching (FRAP) experiments. We rationalize these molecular-level phenomena in terms of vesicle stability and lipid-surface interactions, demonstrating a versatile approach to control CP lipid self-assembly with divalent cations.

Using Machine Learning and Infrared Spectroscopy to Quantify Species Concentrations in Battery Electrolytes

In this research, artificial intelligence, deep learning, and chemometric tools were coupled with operando spectroscopy of battery electrolytes to measure species concentrations and elucidate molecular interactions. FTIR spectra from an electrolyte composed of LiPF6 in ethylene carbonate (EC) and ethyl methyl carbonate (EMC) were analyzed with principal component analysis (PCA) and a convolutional neural network (CNN) to discern solvation behavior and quantify component concentrations during cell operation. PCA pinpointed exact band locations of solvation shifting behavior in the IR spectra and improved understanding of the relationship between spectral peak changes, lithium concentrations, and solvation behavior. The CNN was trained with spectral datasets of electrolytes with known lithium and solvent concentrations and made predictions with extraordinarily high accuracy. Additionally, the CNN interpreted FTIR spectral datasets from a graphite half-cell with EC/EMC/LiPF6 electrolyte and accurately determined the lithium concentration in the bulk electrolyte. The CNN also observed lithium depletion events in the graphite anode during battery cycling. These depletion events were previously investigated with traditional spectroscopic techniques but with large errors in absolute concentration. This research breaks new ground on using advanced computational tools for in situ and operando spectroscopic analysis of battery electrolytes to investigate complex molecular-level phenomena important for improving electrolyte transport and stability.

Exploring the distinct conformational preferences of allyl ethyl ether and allyl ethyl sulfide using rotational spectroscopy and computational chemistry.

The conformational energy landscapes of allyl ethyl ether (AEE) and allyl ethyl sulfide (AES) were investigated using Fourier transform microwave spectroscopy in the frequency range of 5-23GHz aided by density functional theory B3LYP-D3(BJ)/aug-cc-pVTZ calculations. The latter predicted highly competitive equilibria for both species, including 14 unique conformers of AEE and 12 for the sulfur analog AES within 14 kJ mol-1. The experimental rotational spectrum of AEE was dominated by transitions arising from its three lowest energy conformers, which differ in the arrangement of the allyl side chain, while in AES, transitions due to the two most stable forms, distinct in the orientation of the ethyl group, were observed. Splitting patterns attributed to methyl internal rotation were analyzed for AEE conformers I and II, and the corresponding V3 barriers were determined to be 12.172(55) and 12.373(32) kJ mol-1, respectively. The experimental ground state geometries of both AEE and AES were derived using the observed rotational spectra of the 13C and 34S isotopic species and are highly dependent on the electronic properties of the linking chalcogen (oxygen vs sulfur). The observed structures are consistent with a decrease in hybridization in the bridging atom from oxygen to sulfur. The molecular-level phenomena that drive the conformational preferences are rationalized through natural bond orbital and non-covalent interaction analyses. These show that interactions involving the lone pairs on the chalcogen atom with the organic side chains favor distinct geometries and energy orderings for the conformers of AEE and AES.

Roles of aqueous nonsolvents influencing the dynamic stability of poly-(n-butyl methacrylate) thin films at biologically relevant temperatures.

Poly-(n-butyl methacrylate) (PnBMA) is an important polymer in biomedical applications. Here we study the stability of PnBMA thin films prepared on top of slippery silicon substrates and exposed to nonsolvent aqueous incubation media like water and phosphate-buffered saline (PBS) at temperatures relevant to biological applications (37 °C, 25 °C and 4 °C). Dewetting hole growth experiments allowed us to probe the instability in PnBMA films upon incubation followed by thermal annealing. From the early stage of dewetting hole growth dynamics, we inferred that the stability of the thin PnBMA films decreases as a function of the duration and temperature of incubation, even though the films were found not to readily dewet at room temperature after incubation. It is also observed that water incubation makes films more unstable than incubation in PBS. We explained our observations as a combined effect of (i) an increase in surface energy of the PnBMA film due to incubation, (ii) an increased destabilizing effect due to the dominant polar interactions between the incubation medium and the PnBMA film and (iii) the plasticization effect of PnBMA films by the incubation media. Plasticization resulted in a decrease in the modulus of PnBMA thin films as a function of incubation time. The viscosity of PnBMA films upon incubation was found to be coupled to the decreasing modulus. Thus we infer that incubation in common aqueous nonsolvents can detrimentally affect the stability of polymers limiting their specific usages through a complex interplay of multiple molecular level phenomena.

Chemometric and Machine Learning Analysis of Lithium Concentration and Solvation Behavior in Li-Ion Battery Electrolytes

The demand for batteries is rapidly growing across a range of technologies. The increasingly diverse use cases for batteries require various capabilities, particularly requirements for high energy densities, that are currently unmet by traditional Li-ion batteries. Electrolyte stability proves to be a bottleneck for battery advancement towards energy dense chemistries beyond Li-ion, including metal anodes. In situ spectroscopy tools, such as Fourier transform infrared (FTIR) spectroscopy, Raman spectroscopy, and X-ray spectroscopy, have provided insight into critical molecular-level interactions in batteries during cycling. These in situ tools have yielded continuous improvement of electrolyte properties. Spectroscopy datasets, however, contain many nuances that challenge meaningful human understanding. Artificial intelligence and chemometric tools can be coupled with in situ spectroscopy to find relevant interpretations of spectral datasets and elucidate complex molecular phenomena. In this research, an analysis was performed on FTIR spectroscopy data from an electrolyte composed of LiPF6 in ethylene carbonate (EC) and ethyl methyl carbonate (EMC) to discern solvation behavior using principal component analysis (PCA) and a convolutional neural network (CNN). PCA pinpointed exact band locations of solvation shifting behavior in the IR spectra and improved understanding of the relationship between lithium concentrations and peak changes. The CNN was trained with spectral datasets of electrolytes with known lithium concentrations and then could predict lithium concentrations from spectral datasets with extraordinarily high accuracy. Additionally, the CNN interpreted FTIR spectral datasets from a graphite half-cell with EC/EMC/LiPF6 electrolyte and accurately determined the lithium concentration in the bulk electrolyte. The CNN also observed lithium depletion events (up to 10% lithium depletion) in the graphite anode during fast-charging cycles of the galvanostatic intermittent titration technique. This research breaks new ground on using advanced computational tools for in situ spectroscopic analysis of battery electrolytes and demonstrates an improved understanding of complex molecular-level phenomena in electrolytes.

Separation of Azeotropic Hydrofluorocarbon Refrigerant Mixtures: Thermodynamic and Kinetic Modeling for Binary Adsorption of HFC-32 and HFC-125 on Zeolite 5A.

Hydrofluorocarbons (HFCs) have been used extensively as refrigerants over the past four decades; however, HFCs are currently being phased out due to large global warming potentials. As the next generation of hydrofluoroolefin refrigerants are phased in, action must be taken to responsibly and sustainably deal with the HFCs currently in circulation. Ideally, unused HFCs can be reclaimed and recycled; however, many HFCs in circulation are azeotropic or near-azeotropic mixtures and must be separated before recycling. Previously, pure gas isotherm data were presented for both HFC-125 (pentafluoroethane) and HFC-32 (difluoromethane) with zeolite 5A, and it was concluded that this zeolite could separate refrigerant R-410A (50/50 wt % HFC-125/HFC-32). To further investigate the separation capabilities of zeolite 5A, binary adsorption was measured for the same system using the Integral Mass Balance method. Zeolite 5A showed a selectivity of 9.6-10.9 for HFC-32 over the composition range of 25-75 mol % HFC-125. Adsorbed phase activity coefficients were calculated from binary adsorption data. The Spreading Pressure Dependent, modified nonrandom two-liquid, and modified Wilson activity coefficient models were fit to experimental data, and the resulting activity coefficient models were used in Real Adsorbed Solution Theory (RAST). RAST binary adsorption model predictions were compared with Ideal Adsorbed Solution Theory (IAST) predictions made using the Dual-Site Langmuir, Tóth, and Jensen and Seaton pure gas isotherm models. Both IAST and RAST yielded qualitatively accurate predictions; however, quantitative accuracy was greatly improved using RAST models. Diffusion behavior of HFC-125 and HFC-32 was also investigated by fitting the isothermal Fickian diffusion model to kinetic data. Molecular-level phenomena were investigated to understand both thermodynamic and kinetic behaviors.

Mechano-Nanoarchitectonics: Design and Function.

Mechanical stimuli have rather ambiguous and less-specific features among various physical stimuli, but most materials exhibit a certain level of responses upon mechanical inputs. Unexplored sciences remain in mechanical responding systems as one of the frontiers of materials science. Nanoarchitectonics approaches for mechanically responding materials are discussed as mechano-nanoarchitectonics in this review article. Recent approaches on molecular and materials systems with mechanical response capabilities are first exemplified with two viewpoints: i) mechanical control of supramolecular assemblies and materials and ii) mechanical control and evaluation of atom/molecular level structures. In the following sections, special attentions on interfacial environments for mechano-nanoarchitectonics are emphasized. The section entitled iii) Mechanical Control of Molecular System at Dynamic Interface describes coupling of macroscopic mechanical forces and molecular-level phenomena. Delicate mechanical forces can be applied to functional molecules embedded at the air-water interface where operation of molecular machines and tuning of molecular receptors upon macroscopic mechanical actions are discussed. Finally, the important role of the interfacial media are further extended to the control of living cells as described in the section entitled iv) Mechanical Control of Biosystems. Pioneering approaches on cell fate regulations at liquid-liquid interfaces are discussed in addition to well-known mechanobiology.

Single-nanometer iron oxide nanoparticles as tissue-permeable MRI contrast agents

Magnetic nanoparticles are robust contrast agents for MRI and often produce particularly strong signal changes per particle. Leveraging these effects to probe cellular- and molecular-level phenomena in tissue can, however, be hindered by the large sizes of typical nanoparticle contrast agents. To address this limitation, we introduce single-nanometer iron oxide (SNIO) particles that exhibit superparamagnetic properties in conjunction with hydrodynamic diameters comparable to small, highly diffusible imaging agents. These particles efficiently brighten the signal in T1-weighted MRI, producing per-molecule longitudinal relaxation enhancements over 10 times greater than conventional gadolinium-based contrast agents. We show that SNIOs permeate biological tissue effectively following injection into brain parenchyma or cerebrospinal fluid. We also demonstrate that SNIOs readily enter the brain following ultrasound-induced blood-brain barrier disruption, emulating the performance of a gadolinium agent and providing a basis for future biomedical applications. These results thus demonstrate a platform for MRI probe development that combines advantages of small-molecule imaging agents with the potency of nanoscale materials.

Effect of temperature on concentrated electrolytes for advanced lithium ion batteries.

Salt-concentrated electrolytes are emerging as promising electrolytes for advanced lithium ion batteries (LIBs) that can offer high energy density and improved cycle life. To further improve these electrolytes, it is essential to understand their inherent behavior at various operating conditions of LIBs. Molecular dynamics (MD) simulations are extensively used to study various properties of electrolytes and explain the associated molecular-level phenomena. In this study, we use classical MD simulations to probe the properties of the concentrated electrolyte solution of 3 mol/kg lithium hexafluorophosphate (LiPF6) salt in the propylene carbonate solvent at various temperatures ranging from 298 to 378K. Our results reveal that the properties such as ionic diffusivity and molar conductivity of a concentrated electrolyte are more sensitive to temperature compared to that of dilute electrolytes. The residence time analysis shows that temperature affects the Li+ ion solvation shell dynamics significantly. The effect of temperature on the transport and dynamic properties needs to be accounted carefully while designing better thermal management systems for batteries made with concentrated electrolytes to garner the advantages of these electrolytes.

Supervised Machine Learning‐Based Classification of Li−S Battery Electrolytes

AbstractMachine learning (ML) approaches have the potential to create a paradigm shift in science, especially for multi‐variable problems at different levels. Modern battery R&amp;D is an area intrinsically dependent on proper understanding of many different molecular level phenomena and processes alongside evaluation of application level performance: energy, power, efficiency, life‐length, etc. One very promising battery technology is Li−S batteries, but the polysulfide solubility in the electrolyte must be managed. Today, many different electrolyte compositions and concepts are evaluated, but often in a more or less trial‐and‐error fashion. Herein, we show how supervised ML can be applied to accurately classify different Li−S battery electrolytes a priori based on predicting polysulfide solubility. The developed framework is a combined density functional theory (DFT) and statistical mechanics (COSMO‐RS) based quantitative structure‐property relationship (QSPR) model which easily can be extended to other battery technologies and electrolyte properties.

How the Shape and Chemistry of Molecular Penetrants Control Responsive Hydrogel Permeability.

The permeability of hydrogels for the selective transport of molecular penetrants (drugs, toxins, reactants, etc.) is a central property in the design of soft functional materials, for instance in biomedical, pharmaceutical, and nanocatalysis applications. However, the permeation of dense and hydrated polymer membranes is a complex multifaceted molecular-level phenomenon, and our understanding of the underlying physicochemical principles is still very limited. Here, we uncover the molecular principles of permeability and selectivity in hydrogel permeation. We combine the solution–diffusion model for permeability with comprehensive atomistic simulations of molecules of various shapes and polarities in a responsive hydrogel in different hydration states. We find in particular that dense collapsed states are extremely selective, owing to a delicate balance between the partitioning and diffusivity of the penetrants. These properties are sensitively tuned by the penetrant size, shape, and chemistry, leading to vast cancellation effects, which nontrivially contribute to the permeability. The gained insights enable us to formulate semiempirical rules to quantify and extrapolate the permeability categorized by classes of molecules. They can be used as approximate guiding (“rule-of-thumb”) principles to optimize penetrant or membrane physicochemical properties for a desired permeability and membrane functionality.

Do all rejuvenators improve asphalt performance?

Reclaimed asphalt pavement (RAP) and recycled asphalt shingles (RAS) contain significant amounts of bitumen and their application in new constructions have been promoted as a sustainable practice in the pavement industry and to promote circular economy. However, this effort has faced challenges such as the aged bitumen’s inferior properties compared to the virgin counterpart and its unknown contribution to new pavements. To address the latter issue, liquid additives were used under the general title of rejuvenators. That poses an additional challenge associated with the lack of clear metrics to differentiate between softeners and rejuvenators. While revitalising aged asphalt binder to regain its desirable properties would be an ideal solution, progress in this area has been limited by a lack of understanding of rejuvenation mechanisms at the molecular level and the absence of representative test parameter(s) to evaluate both efficacy and durability of rejuvenators. In this study, both laboratory experiments and computer modelling were combined to not only compare efficacy of several rejuvenators, but also explain why some rejuvenators are effective while others are not. It was found that all studied rejuvenators have a softening effect on aged asphalt, but only a few of them revitalised aged asphalts’ physicochemical and rheological properties at the binder level. At the mixture level, a lower number of gyratory compaction and reduced rate of crack propagation were observed after the addition of high performing rejuvenators to the asphalt mixtures containing RAP. The reduction in crack propagation rate could be attributed to both enhanced blending between an aged and virgin bitumen and the revitalisation of aged bitumen properties. The latter phenomenon was explained at the molecular level based on the rejuvenator’s ability to interact with aged asphaltene molecules intercalating into and separating asphaltene nanoaggregates. Accordingly, this molecular-level phenomenon is reflected in two ways: an increase in crossover modulus and crossover frequency at the binder level, and a reduced crack propagation rate at the mixture level.

Biomolecular Binding at Aqueous Interfaces of Langmuir Monolayers of Bioconjugated Amphiphilic Mesogenic Molecules: A Molecular Dynamics Study.

We report a molecular dynamics (MD) simulation study of protein binding at the aqueous-liquid crystal (LC) interfaces of bioconjugated mesogenic molecules. As a simple model of these interfaces, we use monolayers composed of biotin-conjugated or biotin-free amphiphilic mesogenic molecules and streptavidin in water. The all-atom MD simulations reveal that the binding of streptavidin to the biotin mesogenic monolayer is significantly stronger than that to biotin-free mesogenic monolayers. Although specific protein binding marginally increases the overall orientational order and the tilt of the biotin-conjugated mesogenic molecules of the monolayer, significant changes in tilt were observed near the bound protein (in contrast to the protein interaction with the monolayer without biotin). We also observe that specific protein binding changes the dynamic properties of the mesogens within the monolayer (e.g., lateral diffusion coefficients) and associated water. Overall, these MD simulations advance our understanding of the molecular-level phenomena involved in the binding of biomolecules and subsequent dynamic changes at the aqueous-LC interfaces. These results provide guidance to future molecular-level designs of biofunctional LC interfaces.

Supramolecular double-stranded Archimedean spirals and concentric toroids

Connecting molecular-level phenomena to larger scales and, ultimately, to sophisticated molecular systems that resemble living systems remains a considerable challenge in supramolecular chemistry. To this end, molecular self-assembly at higher hierarchical levels has to be understood and controlled. Here, we report unusual self-assembled structures formed from a simple porphyrin derivative. Unexpectedly, this formed a one-dimensional (1D) supramolecular polymer that coiled to give an Archimedean spiral. Our analysis of the supramolecular polymerization by using mass-balance models suggested that the Archimedean spiral is formed at high concentrations of the monomer, whereas other aggregation types might form at low concentrations. Gratifyingly, we discovered that our porphyrin-based monomer formed supramolecular concentric toroids at low concentrations. Moreover, a mechanistic insight into the self-assembly process permitted a controlled synthesis of these concentric toroids. This study both illustrates the richness of self-assembled structures at higher levels of hierarchy and demonstrates a topological effect in noncovalent synthesis.

Biofunctionalization of Silica Nanoparticles with Cell-Penetrating Peptides: Adsorption Mechanism and Binding Energy Estimation.

Nanoparticles represent one of the most promising materials as they have found application in bionanotechnology for enhanced imaging, diagnosis, and treatment of several diseases. Silica coating is widely used to improve colloidal stability and the binding affinity of nanoparticles for various organic molecules, such as cell-penetrating peptides (CPPs). The functionalization of the silica coating by CPPs is very promising, since it enhances the uptake of the nanoparticles due to the intrinsic ability of the CPPs to cross the cellular membrane. However, molecular level phenomena characterizing the CPPs interaction with silica-coated nanoparticles are not clarified yet. In this work, classical molecular dynamics has been used to shed light on the adsorption mechanism of several CPPs onto silica surfaces, in order to highlight the influence of the surface ionization's state on the adsorption mechanism. Our data highlight how the cationic peptides strongly interact with the anionic silica surfaces by H-bond formation between charged residues and the negatively charged siloxide groups, as well as ion pairing between the surfaces and the N-termini residues. Moreover, thanks to metadynamics simulations the CPP-silica binding affinity has been quantified. The free energy profile description allows providing insight into the relationship between ionization of silica nanoparticles and binding selectivity/specificity to CPP.

Inhibition Mechanisms of Rhodococcus Erythropolis 2'-Hydroxybiphenyl-2-sulfinate Desulfinase (DszB).

Naturally occurring enzymatic pathways enable highly specific, rapid thiophenic sulfur cleavage occurring at ambient temperature and pressure, which may be harnessed for the desulfurization of petroleum-based fuel. One pathway found in bacteria is a four-step catabolic pathway (the 4S pathway) converting dibenzothiophene (DBT), a common crude oil contaminant, into 2-hydroxybiphenyl (HBP) without disrupting the carbon-carbon bonds. 2'-Hydroxybiphenyl-2-sulfinate desulfinase (DszB), the rate-limiting enzyme in the enzyme cascade, is capable of selectively cleaving carbon-sulfur bonds. Accordingly, understanding the molecular mechanisms of DszB activity may enable development of the cascade as industrial biotechnology. Based on crystallographic evidence, we hypothesized that DszB undergoes an active site conformational change associated with the catalytic mechanism. Moreover, we anticipated this conformational change is responsible, in part, for enhancing product inhibition. Rhodococcus erythropolis IGTS8 DszB was recombinantly produced and purified via Escherichia coli BL21 to test these hypotheses. Activity and the resulting conformational change of DszB in the presence of HBP were evaluated. The activity of recombinant DszB was comparable to the natively expressed enzyme and was inhibited via competitive binding of the product, HBP. Using circular dichroism, global changes in DszB conformation were monitored in response to HBP concentration, which indicated that both product and substrate produced similar structural changes. Molecular dynamics (MD) simulations and free energy perturbation with Hamiltonian replica exchange molecular dynamics (FEP/λ-REMD) calculations were used to investigate the molecular-level phenomena underlying the connection between conformation change and kinetic inhibition. In addition to the HBP, MD simulations of DszB bound to common, yet structurally diverse, crude oil contaminants 2',2-biphenol (BIPH), 1,8-naphthosultam (NTAM), 2-biphenyl carboxylic acid (BCA), and 1,8-naphthosultone (NAPO) were performed. Analysis of the simulation trajectories, including root-mean-square fluctuation (RMSF), center of mass (COM) distances, and strength of nonbonded interactions, when compared with FEP/λ-REMD calculations of ligand binding free energy, showed excellent agreement with experimentally determined inhibition constants. Together, the results show that the combination of a molecule's hydrophobicity and nonspecific interactions with nearby functional groups contributes to a competitive inhibition mechanism that locks DszB in a closed conformation and precludes substrate access to the active site.

Direct Observation and Manipulation of Supramolecular Polymerization by High-Speed Atomic Force Microscopy.

Despite recent advances in mechanistic understanding and controlled-synthesis methodologies regarding synthetic supramolecular assemblies, it has remained challenging to capture the molecular-level phenomena in real time, thus hindering further progress in this research field. In this study, we applied high-speed atomic-force microscopy (AFM), which has extraordinary spatiotemporal resolution (1 nm and sub-100 ms), to capture dynamic events occurring during synthetic molecular self-assembly. High-speed AFM permitted the visualization of unique dynamic behavior, such as seeded growth and self-repair in real time. Furthermore, scanning-probe AFM permitted the site-specific manipulation and functionalization of a molecular self-assembly. This powerful combination of bottom-up and top-down approaches at the molecular level should enable targeted syntheses of unprecedented functional nanoarchitectures.

Molecular-level driving forces in lignocellulosic biomass deconstruction for bioenergy

The plant cell wall biopolymers lignin, cellulose and hemicellulose are potential renewable sources of clean biofuels and high-value chemicals. However, the complex 3D structure of lignocellulosic biomass is recalcitrant to deconstruction. Major efforts to overcome this recalcitrance have involved pretreating biomass before catalytic processing. This Perspective describes recent work aimed at elucidating the molecular-level physical phenomena that drive biomass assembly. These are at play in commonly employed aqueous-based and thermochemical pretreatments. Several key processes have been found to be driven by biomass solvation thermodynamics, an understanding of which therefore facilitates the rational improvement of methods aimed at the complete solubilization and fractionation of the major biomass components. This Perspective describes the physical molecular driving forces that stabilize native lignocellulosic plant biomass structures and govern thermochemical biomass pretreatments. Understanding these driving forces can help us to design efficient methods for deconstructing biomass into biofuels and other bioproducts.