Fermi-Level Engineering of Nitrogen Core-Doped Armchair Graphene Nanoribbons.

Substitutional heteroatom doping of bottom-up engineered 1D graphene nanoribbons (GNRs) is a versatile tool for realizing low-dimensional functional materials for nanoelectronics and sensing. Previous efforts have largely relied on replacing C-H groups lining the edges of GNRs with trigonal planar N atoms. This type of atomically precise doping, however, only results in a modest realignment of the valence band (VB) and conduction band (CB) energies. Here, we report the design, bottom-up synthesis, and spectroscopic characterization of nitrogen core-doped 5-atom-wide armchair GNRs (N2-5-AGNRs) that yield much greater energy-level shifting of the GNR electronic structure. Here, the substitution of C atoms with N atoms along the backbone of the GNR introduces a single surplus π-electron per dopant that populates the electronic states associated with previously unoccupied bands. First-principles DFT-LDA calculations confirm that a sizable shift in Fermi energy (∼1.0 eV) is accompanied by a broad reconfiguration of the band structure, including the opening of a new band gap and the transition from a direct to an indirect semiconducting band gap. Scanning tunneling spectroscopy (STS) lift-off charge transport experiments corroborate the theoretical results and reveal the relationship among substitutional heteroatom doping, Fermi-level shifting, electronic band structure, and topological engineering for this new N-doped GNR.

Engineering Robust Metallic Zero-Mode States in Olympicene Graphene Nanoribbons.

Metallic graphene nanoribbons (GNRs) represent a critical component in the toolbox of low-dimensional functional materials technology serving as 1D interconnects capable of both electronic and quantum information transport. The structural constraints imposed by on-surface bottom-up GNR synthesis protocols along with the limited control over orientation and sequence of asymmetric monomer building blocks during the radical step-growth polymerization have plagued the design and assembly of metallic GNRs. Here, we report the regioregular synthesis of GNRs hosting robust metallic states by embedding a symmetric zero-mode (ZM) superlattice along the backbone of a GNR. Tight-binding electronic structure models predict a strong nearest-neighbor electron hopping interaction between adjacent ZM states, resulting in a dispersive metallic band. First-principles density functional theory-local density approximation calculations confirm this prediction, and the robust, metallic ZM band of olympicene GNRs is experimentally corroborated by scanning tunneling spectroscopy.

Magnetic Interactions in Substitutional Core-Doped Graphene Nanoribbons.

The design of a spin imbalance within the crystallographic unit cell of bottom-up engineered 1D graphene nanoribbons (GNRs) gives rise to nonzero magnetic moments within each cell. Here, we demonstrate the bottom-up assembly and spectroscopic characterization of a one-dimensional Kondo spin chain formed by a chevron-type GNR (cGNR) physisorbed on Au(111). Substitutional nitrogen core doping introduces a pair of low-lying occupied states per monomer within the semiconducting gap of cGNRs. Charging resulting from the interaction with the gold substrate quenches one electronic state for each monomer, leaving behind a 1D chain of radical cations commensurate with the unit cell of the ribbon. Scanning tunneling microscopy (STM) and spectroscopy (STS) reveal the signature of a Kondo resonance emerging from the interaction of S = 1/2 spin centers in each monomer core with itinerant electrons in the Au substrate. STM tip lift-off experiments locally reduce the effective screening of the unpaired radical cation being lifted, revealing a robust exchange coupling between neighboring spin centers. First-principles DFT-LSDA calculations support the presence of magnetic moments in the core of this GNR when it is placed on Au.

Artificial intelligence and imaging: Opportunities in cardio-oncology.

Cardiovascular disease is a leading cause of death in cancer survivors. It is critical to apply new predictive and early diagnostic methods in this population, as this can potentially inform cardiovascular treatment and surveillance decision-making. We discuss the application of artificial intelligence (AI) technologies to cardiovascular imaging in cardio-oncology, with a particular emphasis on prevention and targeted treatment of a variety of cardiovascular conditions in cancer patients. Recently, the use of AI-augmented cardiac imaging in cardio-oncology is gaining traction. A large proportion of cardio-oncology patients are screened and followed using left ventricular ejection fraction (LVEF) and global longitudinal strain (GLS), currently obtained using echocardiography. This use will continue to increase with new cardiotoxic cancer treatments. AI is being tested to increase precision, throughput, and accuracy of LVEF and GLS, guide point-of-care image acquisition, and integrate imaging and clinical data to optimize the prediction and detection of cardiac dysfunction. The application of AI to cardiovascular magnetic resonance imaging (CMR), computed tomography (CT; especially coronary artery calcium or CAC scans), single proton emission computed tomography (SPECT) and positron emission tomography (PET) imaging acquisition is also in early stages of analysis for prediction and assessment of cardiac tumors and cardiovascular adverse events in patients treated for childhood or adult cancer. The opportunities for application of AI in cardio-oncology imaging are promising, and if availed, will improve clinical practice and benefit patient care.