Secondary structure determines electron transport in peptides.

Proteins play a key role in biological electron transport, but the structure-function relationships governing the electronic properties of peptides are not fully understood. Despite recent progress, understanding the link between peptide conformational flexibility, hierarchical structures, and electron transport pathways has been challenging. Here, we use single-molecule experiments, molecular dynamics (MD) simulations, nonequilibrium Green's function-density functional theory (NEGF-DFT), and unsupervised machine learning to understand the role of secondary structure on electron transport in peptides. Our results reveal a two-state molecular conductance behavior for peptides across several different amino acid sequences. MD simulations and Gaussian mixture modeling are used to show that this two-state molecular conductance behavior arises due to the conformational flexibility of peptide backbones, with a high-conductance state arising due to a more defined secondary structure (beta turn or 310 helices) and a low-conductance state occurring for extended peptide structures. These results highlight the importance of helical conformations on electron transport in peptides. Conformer selection for the peptide structures is rationalized using principal component analysis of intramolecular hydrogen bonding distances along peptide backbones. Molecular conformations from MD simulations are used to model charge transport in NEGF-DFT calculations, and the results are in reasonable qualitative agreement with experiments. Projected density of states calculations and molecular orbital visualizations are further used to understand the role of amino acid side chains on transport. Overall, our results show that secondary structure plays a key role in electron transport in peptides, which provides broad avenues for understanding the electronic properties of proteins.

Dissociation of Centrally and Peripherally Induced Transcranial Magnetic Stimulation Effects in Nonhuman Primates.

Transcranial magnetic stimulation (TMS) is a noninvasive brain stimulation method that is rapidly growing in popularity for studying causal brain-behavior relationships. However, its dose-dependent centrally induced neural mechanisms and peripherally induced sensory costimulation effects remain debated. Understanding how TMS stimulation parameters affect brain responses is vital for the rational design of TMS protocols. Studying these mechanisms in humans is challenging because of the limited spatiotemporal resolution of available noninvasive neuroimaging methods. Here, we leverage invasive recordings of local field potentials in a male and a female nonhuman primate (rhesus macaque) to study TMS mesoscale responses. We demonstrate that early TMS-evoked potentials show a sigmoidal dose-response curve with stimulation intensity. We further show that stimulation responses are spatially specific. We use several control conditions to dissociate centrally induced neural responses from auditory and somatosensory coactivation. These results provide crucial evidence regarding TMS neural effects at the brain circuit level. Our findings are highly relevant for interpreting human TMS studies and biomarker developments for TMS target engagement in clinical applications.SIGNIFICANCE STATEMENT Transcranial magnetic stimulation (TMS) is a widely used noninvasive brain stimulation method to stimulate the human brain. To advance its utility for clinical applications, a clear understanding of its underlying physiological mechanisms is crucial. Here, we perform invasive electrophysiological recordings in the nonhuman primate brain during TMS, achieving a spatiotemporal precision not available in human EEG experiments. We find that evoked potentials are dose dependent and spatially specific, and can be separated from peripheral stimulation effects. This means that TMS-evoked responses can indicate a direct physiological stimulation response. Our work has important implications for the interpretation of human TMS-EEG recordings and biomarker development.

Enhanced Electron Transport in Nonconjugated Radical Oligomers Occurs by Tunneling.

Incorporating temperature- and air-stable organic radical species into molecular designs is a potentially advantageous means of controlling the properties of electronic materials. However, we still lack a complete understanding of the structure-property relationships of organic radical species at the molecular level. In this work, the charge transport properties of (2,2,6,6-tetramethylpiperidin-1-yl)oxyl (TEMPO) radical-containing nonconjugated molecules are studied using single-molecule charge transport experiments and molecular modeling. Importantly, the TEMPO pendant groups promote temperature-independent molecular charge transport in the tunneling region relative to the quenched and closed-shell phenyl pendant groups. Results from molecular modeling show that the TEMPO radicals interact with the gold metal electrodes near the interface to facilitate a high-conductance conformation. Overall, the large enhancement of charge transport by incorporation of open-shell species into a single nonconjugated molecular component opens exciting avenues for implementing molecular engineering in the development of next-generation electronic devices based on novel nonconjugated radical materials.

The Spatial Reach of Neuronal Coherence and Spike-Field Coupling across the Human Neocortex.

Neuronal coherence is thought to be a fundamental mechanism of communication in the brain, where synchronized field potentials coordinate synaptic and spiking events to support plasticity and learning. Although the spread of field potentials has garnered great interest, little is known about the spatial reach of phase synchronization, or neuronal coherence. Functional connectivity between different brain regions is known to occur across long distances, but the locality of synchronization across the neocortex is understudied. Here we used simultaneous recordings from electrocorticography (ECoG) grids and high-density microelectrode arrays to estimate the spatial reach of neuronal coherence and spike-field coherence (SFC) across frontal, temporal, and occipital cortices during cognitive tasks in humans. We observed the strongest coherence within a 2-3 cm distance from the microelectrode arrays, potentially defining an effective range for local communication. This range was relatively consistent across brain regions, spectral frequencies, and cognitive tasks. The magnitude of coherence showed power law decay with increasing distance from the microelectrode arrays, where the highest coherence occurred between ECoG contacts, followed by coherence between ECoG and deep cortical local field potential (LFP), and then SFC (i.e., ECoG > LFP > SFC). The spectral frequency of coherence also affected its magnitude. Alpha coherence (8-14 Hz) was generally higher than other frequencies for signals nearest the microelectrode arrays, whereas delta coherence (1-3 Hz) was higher for signals that were farther away. Action potentials in all brain regions were most coherent with the phase of alpha oscillations, which suggests that alpha waves could play a larger, more spatially local role in spike timing than other frequencies. These findings provide a deeper understanding of the spatial and spectral dynamics of neuronal synchronization, further advancing knowledge about how activity propagates across the human brain.SIGNIFICANCE STATEMENT Coherence is theorized to facilitate information transfer across cerebral space by providing a convenient electrophysiological mechanism to modulate membrane potentials in spatiotemporally complex patterns. Our work uses a multiscale approach to evaluate the spatial reach of phase coherence and spike-field coherence during cognitive tasks in humans. Locally, coherence can reach up to 3 cm around a given area of neocortex. The spectral properties of coherence revealed that alpha phase-field and spike-field coherence were higher within ranges <2 cm, whereas lower-frequency delta coherence was higher for contacts farther away. Spatiotemporally shared information (i.e., coherence) across neocortex seems to reach farther than field potentials alone.

Electron Tomography and Machine Learning for Understanding the Highly Ordered Structure of Leafhopper Brochosomes.

Insects known as leafhoppers (Hemiptera: Cicadellidae) produce hierarchically structured nanoparticles known as brochosomes that are exuded and applied to the insect cuticle, thereby providing camouflage and anti-wetting properties to aid insect survival. Although the physical properties of brochosomes are thought to depend on the leafhopper species, the structure-function relationships governing brochosome behavior are not fully understood. Brochosomes have complex hierarchical structures and morphological heterogeneity across species, due to which a multimodal characterization approach is required to effectively elucidate their nanoscale structure and properties. In this work, we study the structural and mechanical properties of brochosomes using a combination of atomic force microscopy (AFM), electron microscopy (EM), electron tomography, and machine learning (ML)-based quantification of large and complex scanning electron microscopy (SEM) image data sets. This suite of techniques allows for the characterization of internal and external brochosome structures, and ML-based image analysis methods of large data sets reveal correlations in the structure across several leafhopper species. Our results show that brochosomes are relatively rigid hollow spheres with characteristic dimensions and morphologies that depend on leafhopper species. Nanomechanical mapping AFM is used to determine a characteristic compression modulus for brochosomes on the order of 1-3 GPa, which is consistent with crystalline proteins. Overall, this work provides an improved understanding of the structural and mechanical properties of leafhopper brochosomes using a new set of ML-based image classification tools that can be broadly applied to nanostructured biological materials.

Expanding the Molecular Alphabet of DNA-Based Data Storage Systems with Neural Network Nanopore Readout Processing.

DNA is a promising next-generation data storage medium, but challenges remain with synthesis costs and recording latency. Here, we describe a prototype of a DNA data storage system that uses an extended molecular alphabet combining natural and chemically modified nucleotides. Our results show that MspA nanopores can discriminate different combinations and ordered sequences of natural and chemically modified nucleotides in custom-designed oligomers. We further demonstrate single-molecule sequencing of the extended alphabet using a neural network architecture that classifies raw current signals generated by Oxford Nanopore sequencers with an average accuracy exceeding 60% (39× larger than random guessing). Molecular dynamics simulations show that the majority of modified nucleotides lead to only minor perturbations of the DNA double helix. Overall, the extended molecular alphabet may potentially offer a nearly 2-fold increase in storage density and potentially the same order of reduction in the recording latency, thereby enabling new implementations of molecular recorders.

A new paradigm for investigating real-world social behavior and its neural underpinnings.

Eye tracking and other behavioral measurements collected from patient-participants in their hospital rooms afford a unique opportunity to study natural behavior for basic and clinical translational research. We describe an immersive social and behavioral paradigm implemented in patients undergoing evaluation for surgical treatment of epilepsy, with electrodes implanted in the brain to determine the source of their seizures. Our studies entail collecting eye tracking with other behavioral and psychophysiological measurements from patient-participants during unscripted behavior, including social interactions with clinical staff, friends, and family in the hospital room. This approach affords a unique opportunity to study the neurobiology of natural social behavior, though it requires carefully addressing distinct logistical, technical, and ethical challenges. Collecting neurophysiological data synchronized to behavioral and psychophysiological measures helps us to study the relationship between behavior and physiology. Combining across these rich data sources while participants eat, read, converse with friends and family, etc., enables clinical-translational research aimed at understanding the participants' disorders and clinician-patient interactions, as well as basic research into natural, real-world behavior. We discuss data acquisition, quality control, annotation, and analysis pipelines that are required for our studies. We also discuss the clinical, logistical, and ethical and privacy considerations critical to working in the hospital setting.

Ongoing neural oscillations influence behavior and sensory representations by suppressing neuronal excitability.

The ability to process and respond to external input is critical for adaptive behavior. Why, then, do neural and behavioral responses vary across repeated presentations of the same sensory input? Ongoing fluctuations of neuronal excitability are currently hypothesized to underlie the trial-by-trial variability in sensory processing. To test this, we capitalized on intracranial electrophysiology in neurosurgical patients performing an auditory discrimination task with visual cues: specifically, we examined the interaction between prestimulus alpha oscillations, excitability, task performance, and decoded neural stimulus representations. We found that strong prestimulus oscillations in the alpha+ band (i.e., alpha and neighboring frequencies), rather than the aperiodic signal, correlated with a low excitability state, indexed by reduced broadband high-frequency activity. This state was related to slower reaction times and reduced neural stimulus encoding strength. We propose that the alpha+ rhythm modulates excitability, thereby resulting in variability in behavior and sensory representations despite identical input.

Efficient Intermolecular Charge Transport in π-Stacked Pyridinium Dimers Using Cucurbit[8]uril Supramolecular Complexes.

Intermolecular charge transport through π-conjugated molecules plays an essential role in biochemical redox processes and energy storage applications. In this work, we observe highly efficient intermolecular charge transport upon dimerization of pyridinium molecules in the cavity of a synthetic host (cucurbit[8]uril, CB[8]). Stable, homoternary complexes are formed between pyridinium molecules and CB[8] with high binding affinity, resulting in an offset stacked geometry of two pyridiniums inside the host cavity. The charge transport properties of free and dimerized pyridiniums are characterized using a scanning tunneling microscope-break junction (STM-BJ) technique. Our results show that π-stacked pyridinium dimers exhibit comparable molecular conductance to isolated, single pyridinium molecules, despite a longer transport pathway and a switch from intra- to intermolecular charge transport. Control experiments using a CB[8] homologue (cucurbit[7]uril, CB[7]) show that the synthetic host primarily serves to facilitate dimer formation and plays a minimal role on molecular conductance. Molecular modeling using density functional theory (DFT) reveals that pyridinium molecules are planarized upon dimerization inside the host cavity, which facilitates charge transport. In addition, the π-stacked pyridinium dimers possess large intermolecular LUMO-LUMO couplings, leading to enhanced intermolecular charge transport. Overall, this work demonstrates that supramolecular assembly can be used to control intermolecular charge transport in π-stacked molecules.

Role of Interfacial Interactions in the Graphene-Directed Assembly of Monolayer Conjugated Polymers.

Development of graphene-organic hybrid electronics is one of the most promising directions for next-generation electronic materials. However, it remains challenging to understand the graphene-organic semiconductor interactions right at the interface, which is key to designing hybrid electronics. Herein, we study the influence of graphene on the multiscale morphology of solution-processed monolayers of conjugated polymers (PII-2T, DPP-BTz, DPP2T-TT, and DPP-T-TMS). The strong interaction between graphene and PII-2T was manifested in the high fiber density and high film coverage of monolayer films deposited on graphene compared to plasma SiO2 substrates. The monolayer films on graphene also exhibited a higher relative degree of crystallinity and dichroic ratio or polymer alignment, i.e., higher degree of order. Raman spectroscopy revealed the increased backbone planarity of the conjugated polymers upon deposition on graphene as well as the existence of electronic interaction across the interface. This speculation was further substantiated by the results of photoelectron spectroscopy (XPS and UPS) of PII-2T, which showed a decrease in binding energy of several atomic energy levels, movement of the Fermi level toward HOMO, and an increase in work function, all of which indicate p-doping of the polymer. Our results provide a new level of understanding on graphene-polymer interactions at nanoscopic interfaces and the consequent impact on multiscale morphology, which will aid in the design of efficient graphene-organic hybrid electronics.

Designing Multicomponent Polymer Colloids for Self-Stratifying Films.

Aqueous polymer colloids known as latexes are widely used in coating applications. Multicomponent latexes comprised of two incompatible polymeric species organized into a core-shell particle morphology are a promising system for self-stratifying coatings that spontaneously partition into multiple layers, thereby yielding complex structured coatings requiring only a single application step. Developing new materials for self-stratifying coatings requires a clear understanding of the thermodynamic and kinetic properties governing phase separation and polymeric species transport. In this work, we study phase separation and self-stratification in polymer films based on multicomponent acrylic (shell) and acrylic-silicone (core) latex particles. Our results show that the molecular weight of the shell polymer and heat aging conditions of the film critically determine the underlying transport phenomena, which ultimately controls phase separation in the film. Unentangled shell polymers result in efficient phase separation within hours with heat aging at reasonable temperatures, whereas entangled shell polymers effectively inhibit phase separation even under extensive heat aging conditions over a period of months due to kinetic limitations. Transmission electron microscopy is used to track morphological changes as a function of thermal aging. Interestingly, our results show that the rheological properties of the latex films are highly sensitive to morphology, and linear shear rheology is used to understand morphological changes. Overall, these results highlight the importance of bulk rheology as a simple and effective tool for understanding changes in morphology in multicomponent latex films.

Differential contributions of synaptic and intrinsic inhibitory currents to speech segmentation via flexible phase-locking in neural oscillators.

Current hypotheses suggest that speech segmentation-the initial division and grouping of the speech stream into candidate phrases, syllables, and phonemes for further linguistic processing-is executed by a hierarchy of oscillators in auditory cortex. Theta (∼3-12 Hz) rhythms play a key role by phase-locking to recurring acoustic features marking syllable boundaries. Reliable synchronization to quasi-rhythmic inputs, whose variable frequency can dip below cortical theta frequencies (down to ∼1 Hz), requires "flexible" theta oscillators whose underlying neuronal mechanisms remain unknown. Using biophysical computational models, we found that the flexibility of phase-locking in neural oscillators depended on the types of hyperpolarizing currents that paced them. Simulated cortical theta oscillators flexibly phase-locked to slow inputs when these inputs caused both (i) spiking and (ii) the subsequent buildup of outward current sufficient to delay further spiking until the next input. The greatest flexibility in phase-locking arose from a synergistic interaction between intrinsic currents that was not replicated by synaptic currents at similar timescales. Flexibility in phase-locking enabled improved entrainment to speech input, optimal at mid-vocalic channels, which in turn supported syllabic-timescale segmentation through identification of vocalic nuclei. Our results suggest that synaptic and intrinsic inhibition contribute to frequency-restricted and -flexible phase-locking in neural oscillators, respectively. Their differential deployment may enable neural oscillators to play diverse roles, from reliable internal clocking to adaptive segmentation of quasi-regular sensory inputs like speech.

Divalent cations promote TALE DNA-binding specificity.

Recent advances in gene editing have been enabled by programmable nucleases such as transcription activator-like effector nucleases (TALENs) and CRISPR-Cas9. However, several open questions remain regarding the molecular machinery in these systems, including fundamental search and binding behavior as well as role of off-target binding and specificity. In order to achieve efficient and specific cleavage at target sites, a high degree of target site discrimination must be demonstrated for gene editing applications. In this work, we studied the binding affinity and specificity for a series of TALE proteins under a variety of solution conditions using in vitro fluorescence methods and molecular dynamics (MD) simulations. Remarkably, we identified that TALEs demonstrate high sequence specificity only upon addition of small amounts of certain divalent cations (Mg2+, Ca2+). However, under purely monovalent salt conditions (K+, Na+), TALEs bind to specific and non-specific DNA with nearly equal affinity. Divalent cations preferentially bind to DNA over monovalent cations, which attenuates non-specific interactions between TALEs and DNA and further stabilizes specific interactions. Overall, these results uncover new mechanistic insights into the binding action of TALEs and further provide potential avenues for engineering and application of TALE- or TALEN-based systems for genome editing and regulation.

Transition between Nonresonant and Resonant Charge Transport in Molecular Junctions.

Efficient long-range charge transport is required for high-performance molecular electronic devices. Resonant transport is thought to occur in single molecule junctions when molecular frontier orbital energy levels align with electrode Fermi levels, thereby enabling efficient transport without molecular or environmental relaxation. Despite recent progress, we lack a systematic understanding of the transition between nonresonant and resonant transport for molecular junctions with different chemical compositions. In this work, we show that molecular junctions undergo a reversible transition from nonresonant tunneling to resonant transport as a function of applied bias. Transient bias-switching experiments show that the nonresonant to resonant transition is reversible with the applied bias. We determine a general quantitative relationship that describes the transition voltage as a function of the molecular frontier orbital energies and electrode Fermi levels. Overall, this work highlights the importance of frontier orbital energy alignment in achieving efficient charge transport in molecular devices.

Crossmodal Phase Reset and Evoked Responses Provide Complementary Mechanisms for the Influence of Visual Speech in Auditory Cortex.

Natural conversation is multisensory: when we can see the speaker's face, visual speech cues improve our comprehension. The neuronal mechanisms underlying this phenomenon remain unclear. The two main alternatives are visually mediated phase modulation of neuronal oscillations (excitability fluctuations) in auditory neurons and visual input-evoked responses in auditory neurons. Investigating this question using naturalistic audiovisual speech with intracranial recordings in humans of both sexes, we find evidence for both mechanisms. Remarkably, auditory cortical neurons track the temporal dynamics of purely visual speech using the phase of their slow oscillations and phase-related modulations in broadband high-frequency activity. Consistent with known perceptual enhancement effects, the visual phase reset amplifies the cortical representation of concomitant auditory speech. In contrast to this, and in line with earlier reports, visual input reduces the amplitude of evoked responses to concomitant auditory input. We interpret the combination of improved phase tracking and reduced response amplitude as evidence for more efficient and reliable stimulus processing in the presence of congruent auditory and visual speech inputs.SIGNIFICANCE STATEMENT Watching the speaker can facilitate our understanding of what is being said. The mechanisms responsible for this influence of visual cues on the processing of speech remain incompletely understood. We studied these mechanisms by recording the electrical activity of the human brain through electrodes implanted surgically inside the brain. We found that visual inputs can operate by directly activating auditory cortical areas, and also indirectly by modulating the strength of cortical responses to auditory input. Our results help to understand the mechanisms by which the brain merges auditory and visual speech into a unitary perception.

U-net model for brain extraction: Trained on humans for transfer to non-human primates.

Brain extraction (a.k.a. skull stripping) is a fundamental step in the neuroimaging pipeline as it can affect the accuracy of downstream preprocess such as image registration, tissue classification, etc. Most brain extraction tools have been designed for and applied to human data and are often challenged by non-human primates (NHP) data. Amongst recent attempts to improve performance on NHP data, deep learning models appear to outperform the traditional tools. However, given the minimal sample size of most NHP studies and notable variations in data quality, the deep learning models are very rarely applied to multi-site samples in NHP imaging. To overcome this challenge, we used a transfer-learning framework that leverages a large human imaging dataset to pretrain a convolutional neural network (i.e. U-Net Model), and then transferred this to NHP data using a small NHP training sample. The resulting transfer-learning model converged faster and achieved more accurate performance than a similar U-Net Model trained exclusively on NHP samples. We improved the generalizability of the model by upgrading the transfer-learned model using additional training datasets from multiple research sites in the Primate Data-Exchange (PRIME-DE) consortium. Our final model outperformed brain extraction routines from popular MRI packages (AFNI, FSL, and FreeSurfer) across a heterogeneous sample from multiple sites in the PRIME-DE with less computational cost (20 s~10 min). We also demonstrated the transfer-learning process enables the macaque model to be updated for use with scans from chimpanzees, marmosets, and other mammals (e.g. pig). Our model, code, and the skull-stripped mask repository of 136 macaque monkeys are publicly available for unrestricted use by the neuroimaging community at https://github.com/HumanBrainED/NHP-BrainExtraction.

Combining brain perturbation and neuroimaging in non-human primates.

Brain perturbation studies allow detailed causal inferences of behavioral and neural processes. Because the combination of brain perturbation methods and neural measurement techniques is inherently challenging, research in humans has predominantly focused on non-invasive, indirect brain perturbations, or neurological lesion studies. Non-human primates have been indispensable as a neurobiological system that is highly similar to humans while simultaneously being more experimentally tractable, allowing visualization of the functional and structural impact of systematic brain perturbation. This review considers the state of the art in non-human primate brain perturbation with a focus on approaches that can be combined with neuroimaging. We consider both non-reversible (lesions) and reversible or temporary perturbations such as electrical, pharmacological, optical, optogenetic, chemogenetic, pathway-selective, and ultrasound based interference methods. Method-specific considerations from the research and development community are offered to facilitate research in this field and support further innovations. We conclude by identifying novel avenues for further research and innovation and by highlighting the clinical translational potential of the methods.

Nonlinear Transient and Steady State Stretching of Deflated Vesicles in Flow.

Membrane-bound vesicles and organelles exhibit a wide array of nonspherical shapes at equilibrium, including biconcave and tubular morphologies. Despite recent progress, the stretching dynamics of deflated vesicles is not fully understood, particularly far from equilibrium where complex nonspherical shapes undergo large deformations in flow. Here, we directly observe the transient and steady-state nonlinear stretching dynamics of deflated vesicles in extensional flow using a Stokes trap. Automated flow control is used to observe vesicle dynamics over a wide range of flow rates, shape anisotropy, and viscosity contrast. Our results show that deflated vesicle membranes stretch into highly deformed shapes in flow above a critical capillary number Cac1. We further identify a second critical capillary number Cac2, above which vesicle stretch diverges in flow. Vesicles are robust to multiple nonlinear stretch-relax cycles, evidenced by relaxation of dumbbell-shaped vesicles containing thin lipid tethers following flow cessation. An analytical model is developed for vesicle deformation in flow, which enables comparison of nonlinear steady-state stretching results with theories for different reduced volumes. Our results show that the model captures the steady-state stretching of moderately deflated vesicles; however, it underpredicts the steady-state nonlinear stretching of highly deflated vesicles. Overall, these results provide a new understanding of the nonlinear stretching dynamics and membrane mechanics of deflated vesicles in flow.

Understanding How Coacervates Drive Reversible Small Molecule Reactions to Promote Molecular Complexity.

Liquid-liquid phase-separated coacervate droplets give rise to membraneless compartments that play an important role in the spatial organization and reactivity in cells. Due to their molecularly crowded nature and ability to sequester biomolecules, coacervate droplets create distinct environments for enzymatic reaction kinetics and reaction mechanisms that markedly differ from bulk solution. In this work, we use a combination of experiments and quantitative modeling to understand how coacervate droplets promote reversible small molecule reaction chemistry. In particular, we study a model condensation reaction generating an unstable fluorescent imine in polyacrylic acid-polyethylene glycol coacervate droplets over a range of conditions. At equilibrium, the concentration of the imine product in coacervate droplets is approximately 140-fold larger than that in bulk solution, which arises due to preferential partitioning of reactants and products into coacervate droplets and a reaction equilibrium constant that is roughly threefold larger in coacervate droplets than in solution. A reaction-diffusion model is developed to quantitatively describe how competing reaction and partitioning equilibria govern the spatial distribution of the imine product inside coacervate droplets. Overall, our results show that compartmentalization stabilizes kinetically labile reaction products, which enables larger reactant concentrations in coacervate droplets compared to bulk solution. Broadly, these results provide an improved understanding of how biomolecular condensates promote multistep reaction pathways involving unstable reaction intermediates and suggest how coacervates provide a potential abiotic mechanism to promote molecular complexity.

Covalent Ag-C Bonding Contacts from Unprotected Terminal Acetylenes for Molecular Junctions.

Robust molecule-metal linkages are essential for developing high-performance and air-stable devices for molecular and organic electronics. In this work, we report a facile method for forming robust and covalent bonding contacts between unprotected terminal acetylenes and metal (Ag) interfaces. Using this approach, we study the charge transport properties of conjugated oligophenylenes with covalent metal-carbon contacts to silver electrodes formed from unprotected terminal acetylene anchors. We performed single molecule charge transport experiments and molecular simulations on a series of arylacetylenes using gold and silver electrodes. Our results show that molecular junctions on silver electrodes spontaneously form silver-carbynyl carbon (Ag-C) contacts, resulting in a nearly 10-fold increase in conductance compared to the same molecules on gold electrodes. Overall, this work presents a simple, new electrode-anchor pair that reliably forms molecular junctions with stable and robust contacts for molecular electronics.