Exploring Aromaticity Effects on Electronic Transport in Cyclo[n]carbon Single-Molecule Junctions.

Cyclo[n]carbon (Cn) is one member of the all-carbon allotrope family with potential applications in next-generation electronic devices. By employing first-principles quantum transport calculations, we have investigated the electronic transport properties of single-molecule junctions of Cn, with n = 14, 16, 18, and 20, connected to two bulk gold electrodes, uncovering notable distinctions arising from the varying aromaticities. For the doubly aromatic C14 and C18 molecules, slightly deformed complexes at the singlet state arise after bonding with one Au atom at each side; in contrast, the reduced energy gaps between the highest occupied and the lowest unoccupied molecular orbitals due to the orbital reordering observed in the doubly anti-aromatic C16 and C20 molecules lead to heavily deformed asymmetric complexes at the triplet state. Consequently, spin-unpolarized transmission functions are obtained for the Au-C14/18-Au junctions, while spin-polarized transmission appears in the Au-C16/20-Au junctions. Furthermore, the asymmetric in-plane π-type hybrid molecular orbitals of the Au-C16/20-Au junctions contribute to two broad but low transmission peaks far away from the Fermi level (Ef), while the out-of-plane π-type hybrid molecular orbitals dominate two sharp transmission peaks that are adjacent to Ef, thus resulting in much lower transmission coefficients at Ef compared to those of the Au-C14/18-Au junctions. Our findings are helpful for the design and application of future cyclo[n]carbon-based molecular electronic devices.

Robust covalent pyrazine anchors forming highly conductive and polarity-tunable molecular junctions with carbon electrodes.

In molecular electronics, electrode-molecule anchoring strategies play a crucial role in the design of stable and high-performance functional single-molecule devices. Herein, we employ aromatic pyrazine as anchors to connect a central anthracene molecule to carbon electrodes including graphene and armchair single-walled carbon nanotubes (SWCNTs), and theoretically investigate their atomic structures and electronic transport properties. These molecular junctions can be constructed via condensation reactions of the central molecules terminated with ortho-phenylenediamines with ortho-quinone-functionalized nanogaps of graphene and SWCNT electrodes. With two direct C-N covalent bonds connecting the central molecule site-selectively to carbon electrodes in a coplanar way, pyrazine anchors are advantageous for forming stable and structurally well-defined molecular junctions, being expected to reduce the uncertainty about the electrode-molecule linkage motifs. The junction transport is highly efficient due to the coplanar geometry and the ensuing strong π-type molecule-electrode electronic coupling. Furthermore, our calculations show that molecular junctions with pyrazine anchors and carbon electrodes are usually n-type electronic devices; upon hydrogenation of pyridinic nitrogen atoms, the device polarity can be tuned to p-type, indicating that the pyrazine anchors can also serve as a powerful platform for tailoring in situ the polarity of charge carriers in carbon-electrode molecular electronic devices.

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.

Tuning Surface Organic Structures by Small Gas Molecules through Catassembly and Coassembly.

The field of molecular assembly has seen remarkable advancements across various domains, such as materials science, nanotechnology, and biomedicine. Small gas molecules serve as pivotal modulators, capable of altering the architecture of assemblies via tuning a spectrum of intermolecular forces including hydrogen bonding, dipole-dipole interactions, and metal coordination. Surface techniques, notably scanning tunneling microscopy and atomic force microscopy, have proven instrumental in dissecting the structural metamorphosis and characteristic features of these assemblies at an unparalleled single-molecule resolution. Recent research has spotlighted two innovative approaches for modulating surface molecular assemblies with the aid of small gas molecules: "catassembly" and "coassembly". This Perspective delves into these methodologies through the lens of varying molecular interaction types. The strategies discussed here for regulating molecular assembly structures using small gas molecules can aid in understanding various complex assembly processes and structures and provide guidance for the further fabrication of complex surface structures.