Single-molecule Break Junction Research Articles

Distinct armchair and zigzag charge transport through single polycyclic aromatics.

In aromatic systems with large π-conjugated structures, armchair and zigzag configurations can affect each material's electronic properties, determining their performance and generating certain quantum effects. Here, we explore the intrinsic effect of armchair and zigzag pathways on charge transport through single hexabenzocoronene molecules. Theoretical calculations and systematic experimental results from static carbon-based single-molecule junctions and dynamic scanning tunneling microscope break junctions show that charge carriers are preferentially transported along the hexabenzocoronene armchair pathway, and thus, the corresponding current through this pathway is approximately one order of magnitude higher than that through the zigzag pathway. In addition, the molecule with the zigzag pathway has a smaller energy gap. In combination with its lower off-state conductance, it shows a better field-effect performance because of its higher on-off ratio in electrical measurements. This study on charge transport pathways offers a useful perspective for understanding the electronic properties of π-conjugated systems and realizing high-performance molecular nanocircuits toward practical applications.

Configuration-Specific Insight into Single-Molecule Conductance and Noise Data Revealed by the Principal Component Projection Method.

We explore the merits of neural network boosted, principal-component-projection-based, unsupervised data classification in single-molecule break junction measurements, demonstrating that this method identifies highly relevant trace classes according to the well-defined and well-visualized internal correlations of the data set. To this end, we investigate single-molecule structures exhibiting double molecular configurations, exploring the role of the leading principal components in the identification of alternative junction evolution trajectories. We show how the proper principal component projections can be applied to separately analyze the high- or low-conductance molecular configurations, which we exploit in 1/f-type noise measurements on bipyridine molecules. This approach untangles the unclear noise evolution of the entire data set, identifying the coupling of the aromatic ring to the electrodes through the π orbitals in two distinct conductance regions, and its subsequent uncoupling as these configurations are stretched.

Stereoelectronic Modulation of a Single-Molecule Junction through a Tunable Metal–Carbon dπ–pπ Hyperconjugation

Conjugated molecules play a critical role in the construction of single-molecule devices. However, most conventional conjugated molecules, such as hydrocarbons, involve only a pπ-pπ conjugation of light elements. While the metal d-orbitals can introduce abundant electronic effects to achieve novel electronic properties, it is very scarce for the charge transport study of dπ-pπ conjugated pathways with a metal involved. Here, we employed the single-molecule break junction technique to investigate the charge transport through dπ-pπ conjugated backbones with metal-carbon multiple bonds integrated into the alternative conjugated pathways. The involved dπ-pπ conjugation not only supports high conductivity comparable to that of conjugated hydrocarbons but also significantly enhances the tunable diversity in electronic properties through the metal-induced secondary interaction. Specifically, the introduction of the metal brings an unconventionally stereoelectronic effect triggered by metal-carbon dπ-pπ hyperconjugation, which can be tuned by protonation taking place on the metal-carbon multiple bonds, collectively modulating the single-molecule rectification feature and transmission mechanism. This work demonstrates the promise of utilizing the diverse electronic effect of metals to design molecular devices.

Electric field-driven folding of single molecules

Folding of molecules is an essential process in nature, and various molecular machines achieve their chemical and mechanical function via controlled folding of molecular conformations. The electric field offers a unique strategy to drive the folding of molecular conformation and to control charge transport through single molecules but remains unexplored. The single-molecule break junction technique provides access to detect the conformational changes via the monitoring of single-molecule conductance, and the electric field between two metal electrodes with nanoscale spacing can provide an extremely strong to achieve in-situ control and detection of molecular folding at the single-molecule level. Here, we use the electric field to control the single-molecule folding using the scanning tunneling microscope break junction (STM-BJ) technique. The electric fields induced folding could lead to a ∼1400% conductance change of the single-molecule junctions, and the folding/unfolding process can be in-situ switched at the scale of milliseconds. DFT calculations suggest the conformational control originates from the electric field-induced charge injection, and the formation of homoconjugated conformation with the overlapped orbitals. This work provides the first demonstration of electric field-driven molecular folding, which is essential for the understanding of molecular machines in nature and for the design of artificial molecular machines.

Mapping DNA Conformations Using Single-Molecule Conductance Measurements.

DNA is an attractive material for a range of applications in nanoscience and nanotechnology, and it has recently been demonstrated that the electronic properties of DNA are uniquely sensitive to its sequence and structure, opening new opportunities for the development of electronic DNA biosensors. In this report, we examine the origin of multiple conductance peaks that can occur during single-molecule break-junction (SMBJ)-based conductance measurements on DNA. We demonstrate that these peaks originate from the presence of multiple DNA conformations within the solutions, in particular, double-stranded B-form DNA (dsDNA) and G-quadruplex structures. Using a combination of circular dichroism (CD) spectroscopy, computational approaches, sequence and environmental controls, and single-molecule conductance measurements, we disentangle the conductance information and demonstrate that specific conductance values come from specific conformations of the DNA and that the occurrence of these peaks can be controlled by controlling the local environment. In addition, we demonstrate that conductance measurements are uniquely sensitive to identifying these conformations in solutions and that multiple configurations can be detected in solutions over an extremely large concentration range, opening new possibilities for examining low-probability DNA conformations in solutions.

SARS-CoV-2 spike proteins react with Au and Si, are electrically conductive and denature at 3 × 108 V m-1: a surface bonding and a single-protein circuit study.

Developing means to characterise SARS-CoV-2 and its new variants is critical for future outbreaks. SARS-CoV-2 spike proteins have peripheral disulfide bonds (S-S), which are common in all spike proteins of SARS-CoV-2 variants, in other types of coronaviruses (e.g., SARS-CoV and MERS-CoV) and are likely to be present in future coronaviruses. Here, we demonstrate that S-S bonds in the spike S1 protein of SARS-CoV-2 react with gold (Au) and silicon (Si) electrodes. Bonding to Si is induced by a spontaneous electrochemical reaction that involves oxidation of Si-H and the reduction of the S-S bonds. The reaction of the spike protein with Au enabled single-molecule protein circuits, by connecting the spike S1 protein between two Au nano-electrodes using the scanning tunnelling microscopy-break junction (STM-BJ) technique. The conductance of a single spike S1 protein was surprisingly high and ranged between two states of 3 × 10-4 G 0 and 4 × 10-6 G 0 (1G 0 = 77.5 μS). The two conductance states are governed by the S-S bonds reaction with Au which controls the orientation of the protein in the circuit, and via which different electron pathways are created. The 3 × 10-4 G 0 level is attributed to a single SARS-CoV-2 protein connecting to the two STM Au nano-electrodes from the receptor binding domain (RBD) subunit and the S1/S2 cleavage site. A lower 4 × 10-6 G 0 conductance is attributed to the spike protein connecting to the STM electrodes from the RBD subunit and the N-terminal domain (NTD). These conductance signals are only observed at electric fields equal to or lower than 7.5 × 107 V m-1. At an electric field of 1.5 × 108 V m-1, the original conductance magnitude decreases accompanied by a lower junction yield, suggesting a change in the structure of the spike protein in the electrified junction. Above an electric field of 3 × 108 V m-1, the conducting channels are blocked and this is attributed to the spike protein denaturing in the nano-gap. These findings open new venues for developing coronavirus-capturing materials and offer an electrical method for analysing, detecting and potentially electrically deactivating coronaviruses and their future variants.

Scaling of quantum interference from single molecules to molecular cages and their monolayers

The discovery of quantum interference (QI) is widely considered as an important advance in molecular electronics since it provides unique opportunities for achieving single-molecule devices with unprecedented performance. Although some pioneering studies suggested the presence of spin qubit coherence and QI in collective systems such as thin films, it remains unclear whether the QI can be transferred step-by-step from single molecules to different length scales, which hinders the application of QI in fabricating active molecular devices. Here, we found that QI can be transferred from a single molecule to their assemblies. We synthesized and investigated the charge transport through the molecular cages using 1,3-dipyridylbenzene (DPB) as a ligand block with a destructive quantum interference (DQI) effect and 2,5-dipyridylfuran (DPF) as a control building block with a constructive quantum interference (CQI) effect using both single-molecule break junction and large area junction techniques. Combined experiments and calculations revealed that both DQI and CQI had been transferred from the ligand blocks to the molecular cages and the monolayer thin film of the cages. Our work introduced QI effects from a ligand to the molecular cage comprising 732 atoms and even their monolayers, suggesting that the quantum interference could be scaled up within the phase-coherent distance.

Structure Identification for Force-Induced Reaction Using Single-Molecule Conductance Measurement

Structure Identification for Force-Induced Reaction Using Single-Molecule Conductance Measurement

The Evolution of the Charge Transport Mechanism in Single-Molecule Break Junctions Revealed by Flicker Noise Analysis.

The electronic noise characterization of single-molecule devices provides insights into the mechanisms of charge transport. In this work, it is reported that flicker noise can serve as an indicator of the time-dependent evolution of charge transport mechanisms in the single-molecule break junction process. By introducing time-frequency analysis, the authors find that flicker noise components of the molecule junction show time evolution behavior in the dynamic break junction process. A further investigation of the power-law dependence of flicker with conductance during the dynamic break junction process reveals that the mechanism of charge transport transits from the through-space transport to the through-bond transport, and is dominated by through-space transport again when the junction is about to rupture. The authors' results provide a flicker noise-based way to characterize the time-dependent evolution of charge transport mechanisms in single-molecule break junctions.

Grid-Based Correlation Analysis to Identify Rare Quantum Transport Behaviors

Most single-molecule transport experiments produce large and stochastic datasets containing a wide range of behaviors, presenting both a challenge to their analysis, but also an opportunity for discovering new physical insights. Recently, several unsupervised clustering algorithms have been developed to help extract and separate distinct features from single-molecule transport data. However, these clustering approaches have been primarily designed and used to extract major dataset components, and are consequently likely to struggle with identifying very rare features and behaviors which may nonetheless contain physically meaningful information. In this work, we thus introduce a completely new analysis framework specifically designed for rare event detection in single-molecule break junction data to help unlock such information and provide a new perspective with different implicit assumptions than clustering. Our approach leverages the concept of correlations of breaking traces with their own history to robustly identify paths through distance-conductance space that correspond to reproducible rare behaviors. As both a demonstrative and important example, we focus on rare conductance plateaus for short molecules, which can be essentially invisible when examining raw data. We show that our grid-based correlation tools successfully and reproducibly locate these rare plateaus in real experimental datasets, including in situations that traditional clustering approaches find challenging. This result enables a broader variety of molecules to be considered in the future, and suggests that our new approach is a useful tool for detecting rare yet meaningful behaviors in single molecule transport data more generally.

Using Weakly Supervised Deep Learning to Classify and Segment Single-Molecule Break-Junction Conductance Traces.

In order to design molecular electronic devices with high performance and stability, it is crucial to understand their structure-to-property relationships. Single-molecule break junction measurements yield a large number of conductance-distance traces, which are inherently highly stochastic. Here we propose a weakly supervised deep learning algorithm to classify and segment these conductance traces, a method that is mainly based on transfer learning with the pretrain-finetune technique. By exploiting the powerful feature extraction capabilities of the pretrained VGG-16 network, our convolutional neural network model not only achieves high accuracy in the classification of the conductance traces, but also segments precisely the conductance plateau from an entire trace with very few manually labeled traces. Thus, we can produce a more reliable estimation of the junction conductance and quantify the junction stability. These findings show that our model has achieved a better accuracy-to-manpower efficiency balance, opening up the possibility of using weakly supervised deep learning approaches in the studies of single-molecule junctions.

A machine learning approach for accurate and real-time DNA sequence identification

BackgroundThe all-electronic Single Molecule Break Junction (SMBJ) method is an emerging alternative to traditional polymerase chain reaction (PCR) techniques for genetic sequencing and identification. Existing work indicates that the current spectra recorded from SMBJ experimentations contain unique signatures to identify known sequences from a dataset. However, the spectra are typically extremely noisy due to the stochastic and complex interactions between the substrate, sample, environment, and the measuring system, necessitating hundreds or thousands of experimentations to obtain reliable and accurate results.ResultsThis article presents a DNA sequence identification system based on the current spectra of ten short strand sequences, including a pair that differs by a single mismatch. By employing a gradient boosted tree classifier model trained on conductance histograms, we demonstrate that extremely high accuracy, ranging from approximately 96 % for molecules differing by a single mismatch to 99.5 % otherwise, is possible. Further, such accuracy metrics are achievable in near real-time with just twenty or thirty SMBJ measurements instead of hundreds or thousands. We also demonstrate that a tandem classifier architecture, where the first stage is a multiclass classifier and the second stage is a binary classifier, can be employed to boost the single mismatched pair’s identification accuracy to 99.5 %.ConclusionsA monolithic classifier, or more generally, a multistage classifier with model specific parameters that depend on experimental current spectra can be used to successfully identify DNA strands.

Spectral Clustering to Analyze the Hidden Events in Single-Molecule Break Junctions

The single-molecule break junction technique provides a high-throughput method to explore the charge transport phenomena through a molecular junction at the ultimate scale of a single molecule. The...

Selective Fabrication of Single-Molecule Junctions by Interface Engineering.

Recent progress in addressing electrically driven single-molecule behaviors has opened up a path toward the controllable fabrication of molecular devices. Herein, the selective fabrication of single-molecule junctions is achieved by employing the external electric field. For molecular junctions with methylthio (-SMe), thioacetate (-SAc), amine (-NH2 ), and pyridyl (-PY), the evolution of their formation probabilities along with the electric field is extracted from the plateau analysis of individual single-molecule break junction traces. With the increase of the electric field, the SMe-anchored molecules show a different trend in the formation probability compared to the other molecular junctions, which is consistent with the density functional theory calculations. Furthermore, switching from an SMe-anchored junction to an SAc-anchored junction is realized by altering the electric field in a mixed solution. The results in this work provide a new approach to the controllable fabrication and modulation of single-molecule junctions and other bottom-up nanodevices at molecular scales.

Correction to “Unsupervised Segmentation-Based Machine Learning as an Advanced Analysis Tool for Single Molecule Break Junction Data”

ADVERTISEMENT RETURN TO ISSUEPREVAddition/CorrectionNEXTORIGINAL ARTICLEThis notice is a correctionCorrection to “Unsupervised Segmentation-Based Machine Learning as an Advanced Analysis Tool for Single Molecule Break Junction Data”Nathan D. BambergerNathan D. BambergerMore by Nathan D. Bamberger, Jeffrey A. IvieJeffrey A. IvieMore by Jeffrey A. Ivie, Keshaba N. ParidaKeshaba N. ParidaMore by Keshaba N. Parida, Dominic V. McGrathDominic V. McGrathMore by Dominic V. McGrathhttp://orcid.org/0000-0001-9605-2224, and Oliver L. A. Monti*Oliver L. A. MontiMore by Oliver L. A. Montihttp://orcid.org/0000-0002-0974-7253Cite this: J. Phys. Chem. C 2020, 124, 43, 24029–24031Publication Date (Web):October 14, 2020Publication History Published online14 October 2020Published inissue 29 October 2020https://doi.org/10.1021/acs.jpcc.0c08721Copyright © 2020 American Chemical SocietyRIGHTS & PERMISSIONSArticle Views705Altmetric-Citations-LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit PDF (507 KB) Get e-Alerts Get e-Alerts

Unsupervised Segmentation-Based Machine Learning as an Advanced Analysis Tool for Single Molecule Break Junction Data

Improved understanding of charge-transport in single molecules is essential for harnessing the potential of molecules e.g. as circuit components at the ultimate size limit. However, interpretation and analysis of the large, stochastic datasets produced by most quantum transport experiments remains an ongoing challenge to discovering much-needed structure-property relationships. Here, we introduce Segment Clustering, a novel unsupervised hypothesis generation tool for investigating single molecule break junction distance-conductance traces. In contrast to previous machine learning approaches for single molecule data, Segment Clustering identifies groupings of similar pieces of traces instead of entire traces. This offers a new and advantageous perspective into dataset structure because it facilitates the identification of meaningful local trace behaviors that may otherwise be obscured by random fluctuations over longer distance scales. We illustrate the power and broad applicability of this approach with two case studies that address common challenges encountered in single molecule studies: First, Segment Clustering is used to extract primary molecular features from a varying background to increase the precision and robustness of conductance measurements, enabling small changes in conductance in response to molecular design to be identified with confidence. Second, Segment Clustering is applied to a known data mixture to qualitatively separate distinct molecular features in a rigorous and unbiased manner. These examples demonstrate two powerful ways in which Segment Clustering can aid in the development of structure-property relationships in molecular quantum transport, an outstanding challenge in the field of molecular electronics.

Single‐Molecule Junctions: An On‐Chip Break Junction System for Combined Single‐Molecule Conductance and Raman Spectroscopies (Adv. Funct. Mater. 28/2020)

In article number 2000615, Joshua Hihath and co-workers present a novel on-chip single-molecule break junction device utilizing a micro-electromechanical system fabricated using conventional semiconductor manufacturing processes. The system provides improved optical accessibility and scalability over traditional break-junction systems and is fully electrically controllable. These features make it a reliable platform for the convenient characterization and practical application of single-molecule electronic systems.

Coenzyme Coupling Boosts Charge Transport through Single Bioactive Enzyme Junctions.

SummaryOxidation of formate to CO2 is catalyzed via the donation of electrons from formate dehydrogenase (FDH) to nicotinamide adenine dinucleotide (NAD+), and thus the charge transport characteristics of FDH become essential but remain unexplored. Here, we investigated the charge transport through single-enzyme junctions of FDH using the scanning tunneling microscope break junction technique (STM-BJ). We found that the coupling of NAD+ with FDH boosts the charge transport by ∼2,100%, and the single-enzyme conductance highly correlates with the enzyme activity. The combined flicker noise analysis demonstrated the switching of the coenzyme-mediated charge transport pathway and supported by the significantly reduced HOMO-LUMO gap from calculations. Site-specific mutagenesis analysis demonstrated that FDH-NAD+ stably combined own higher bioactivity and boosts charge transport, and the coupling has been optimized via the natural selection. Our work provides evidence of hydrogen bond coupling in bioactivity but also bridges the charge transport through single-enzyme junctions and enzyme activities.

Unsupervised feature recognition in single-molecule break junction data.

Single-molecule break junction measurements deliver a huge number of conductance vs. electrode separation traces. During such measurements, the target molecules may bind to the electrodes in different geometries, and the evolution and rupture of the single-molecule junction may also follow distinct trajectories. The unraveling of the various typical trace classes is a prerequisite to the proper physical interpretation of the data. Here we exploit the efficient feature recognition properties of neural networks to automatically find the relevant trace classes. To eliminate the need for manually labeled training data we apply a combined method, which automatically selects training traces according to the extreme values of principal component projections or some auxiliary measured quantities. Then the network captures the features of these characteristic traces and generalizes its inference to the entire dataset. The use of a simple neural network structure also enables a direct insight into the decision-making mechanism. We demonstrate that this combined machine learning method is efficient in the unsupervised recognition of unobvious, but highly relevant trace classes within low and room temperature gold-4,4' bipyridine-gold single-molecule break junction data.

Single-Molecule Measurement of Adsorption Free Energy at the Solid-Liquid Interface.

Adsorption plays a critical role in surface and interface processes. Fractional surface coverage and adsorption free energy are two essential parameters of molecular adsorption. However, although adsorption at the solid-gas interface has been well-studied, and some adsorption models were proposed more than a century ago, challenges remain for the experimental investigation of molecular adsorption at the solid-liquid interface. Herein, we report the statistical and quantitative single-molecule measurement of adsorption at the solid-liquid interface by using the single-molecule break junction technique. The fractional surface coverage was extracted from the analysis of junction formation probability so that the adsorption free energy could be calculated by referring to the Langmuir isotherm. In the case of three prototypical molecules with terminal methylthio, pyridyl, and amino groups, the adsorption free energies were found to be 32.5, 33.9, and 28.3 kJ mol-1 , respectively, which are consistent with DFT calculations.