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Introduction: Timely diagnosis of patients affected by an emerging infectious disease plays a crucial role in treating patients and avoiding disease spread. In prior research, we developed an approach by using machine learning (ML) algorithms to predict serious acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection based on clinical features of patients visiting an emergency department (ED) during the early coronavirus 2019 (COVID-19) pandemic. In this study, we aimed to externally validate this approach within a distinct ED population.Methods: To create our training/validation cohort (model development) we collected data retrospectively from suspected COVID-19 patients at a US ED from February 23–May 12, 2020. Another dataset was collected as an external validation (testing) cohort from an ED in another country from May 12–June 15, 2021. Clinical features including patient demographics and triage information were used to train and test the models. The primary outcome was the confirmed diagnosis of COVID-19, defined as a positive reverse transcription polymerase chain reaction test result for SARS-CoV-2. We employed three different ML algorithms, including gradient boosting, random forest, and extra trees classifiers, to construct the predictive model. The predictive performances were evaluated with the area under the receiver operating characteristic curve (AUC) in the testing cohort.Results: In total, 580 and 946 ED patients were included in the training and testing cohorts, respectively. Of them, 98 (16.9%) and 180 (19.0%) were diagnosed with COVID-19. All the constructed ML models showed acceptable discrimination, as indicated by the AUC. Among them, random forest (0.785, 95% confidence interval [CI] 0.747–0.822) performed better than gradient boosting (0.774, 95% CI 0.739–0.811) and extra trees classifier (0.72, 95% CI 0.677–0.762). There was no significant difference between the constructed models.Conclusion: Our study validates the use of ML for predicting COVID-19 in the ED and demonstrates its potential for predicting emerging infectious diseases based on models built by clinical features with temporal and spatial heterogeneity. This approach holds promise for scenarios where effective diagnostic tools for an emerging infectious disease may be lacking in the future.

We investigate the thermally induced cyclization of 1,2-bis(2-phenylethynyl)benzene on Au(111) using scanning tunneling microscopy and computer simulations. Cyclization of sterically hindered enediynes is known to proceed via two competing mechanisms in solution: a classic C(1)-C(6) (Bergman) or a C(1)-C(5) cyclization pathway. On Au(111), we find that the C(1)-C(5) cyclization is suppressed and that the C(1)-C(6) cyclization yields a highly strained bicyclic olefin whose surface chemistry was hitherto unknown. The C(1)-C(6) product self-assembles into discrete noncovalently bound dimers on the surface. The reaction mechanism and driving forces behind noncovalent association are discussed in light of density functional theory calculations.

Substitutional doping of graphene nanoribbons (GNRs) with heteroatoms is a principal strategy to fine-tune the electronic structure of GNRs for future device applications. Here, we report the fabrication and nanoscale characterization of atomically precise N = 13 armchair GNRs featuring regioregular edge-doping with sulfur atoms (S-13-AGNRs) on a Au(111) surface. Scanning tunneling spectroscopy and first-principle calculations reveal modification of the electronic structure of S-13-AGNRs when compared to undoped N = 13 AGNRs.

Bandgap engineering is used to create semiconductor heterostructure devices that perform processes such as resonant tunnelling and solar energy conversion. However, the performance of such devices degrades as their size is reduced. Graphene-based molecular electronics has emerged as a candidate to enable high performance down to the single-molecule scale. Graphene nanoribbons, for example, can have widths of less than 2 nm and bandgaps that are tunable via their width and symmetry. It has been predicted that bandgap engineering within a single graphene nanoribbon may be achieved by varying the width of covalently bonded segments within the nanoribbon. Here, we demonstrate the bottom-up synthesis of such width-modulated armchair graphene nanoribbon heterostructures, obtained by fusing segments made from two different molecular building blocks. We study these heterojunctions at subnanometre length scales with scanning tunnelling microscopy and spectroscopy, and identify their spatially modulated electronic structure, demonstrating molecular-scale bandgap engineering, including type I heterojunction behaviour. First-principles calculations support these findings and provide insight into the microscopic electronic structure of bandgap-engineered graphene nanoribbon heterojunctions.

Semiconducting π-conjugated polymers have attracted significant interest for applications in light-emitting diodes, field-effect transistors, photovoltaics, and nonlinear optoelectronic devices. Central to the success of these functional organic materials is the facile tunability of their electrical, optical, and magnetic properties along with easy processability and the outstanding mechanical properties associated with polymeric structures. In this work we characterize the chemical and electronic structure of individual chains of oligo-(E)-1,1'-bi(indenylidene), a polyacetylene derivative that we have obtained through cooperative C1-C5 thermal enediyne cyclizations on Au(111) surfaces followed by a step-growth polymerization of the (E)-1,1'-bi(indenylidene) diradical intermediates. We have determined the combined structural and electronic properties of this class of oligomers by characterizing the atomically precise chemical structure of individual monomer building blocks and oligomer chains (via noncontact atomic force microscopy (nc-AFM)), as well as by imaging their localized and extended molecular orbitals (via scanning tunneling microscopy and spectroscopy (STM/STS)). Our combined structural and electronic measurements reveal that the energy associated with extended π-conjugated states in these oligomers is significantly lower than the energy of the corresponding localized monomer orbitals, consistent with theoretical predictions.

Recently developed processes have enabled bottom-up chemical synthesis of graphene nanoribbons (GNRs) with precise atomic structure. These GNRs are ideal candidates for electronic devices because of their uniformity, extremely narrow width below 1 nm, atomically perfect edge structure, and desirable electronic properties. Here, we demonstrate nano-scale chemically synthesized GNR field-effect transistors, made possible by development of a reliable layer transfer process. We observe strong environmental sensitivity and unique transport behavior characteristic of sub-1 nm width GNRs. © 2013 AIP Publishing LLC.