Graphene nanoribbons (GNRs) are of enormous research interest as a promising active component in electronic devices, for example, field-effect transistors (FET). The recently developed "bottomup" on-surface synthesis provides an unprecedented approach for the generation of GNRs on metal surfaces with atomic precision. In order to fabricate well-defined GNRs on surfaces, numerous previous works have been focused on the delicate engineering of building blocks. Lateral fusion of polyphenylene chains into GNRs, as a more flexible method, now has received an increasing attention. However, the lateral fusion into GNRs reported to date is merely limited to the straight GNRs. The GNRs with other topologies potentially displaying distinctive electronic properties are rarely reported. In this work, we report the synthesis of armchair-edged graphene nanoribbons (AGNRs) with zigzag topology for the first time via a stepwise polymerization reaction starting from 4,4″-dibromo-m-terphenyl (DMTP) precursor on Au(111). Self-assembled unreacted monomers, covalent dimers, and zigzag polyphenylene chains are observed at different temperatures. Various GNRs with zigzag topology, including 6-AGNRs, 9-AGNRs, and nanoporous AGNRs are eventually produced through lateral fusion of polyphenylene chains. This study further diversifies the GNR family. Confining the zigzag polyphenylene chains in an ideal arrangement for subsequent lateral fusion can be explored in the future.

Untargeted analysis performed using full-scan mass spectrometry (MS) coupled with liquid chromatography (LC) is commonly used in metabolomics. Although they are commonly employed, full-scan MS methods such as quadrupole-time-of-flight (Q-TOF) MS have been restricted by various factors including their limited linear range and complicated data processing. LC coupled with triple quadrupole (QQQ) MS operated in the multiple reaction monitoring (MRM) mode is the gold standard for metabolite quantification; however, only known metabolites are generally quantified, limiting its applications in metabolomic analysis. In this study, a pseudotargeted approach was proposed to perform serum metabolomic analysis using an ultra high-performance liquid chromatography (UHPLC)/QQQ MS system operated in the MRM mode, for which the MRM ion pairs were acquired from the serum samples through untargeted tandem MS using UHPLC/Q-TOF MS. The UHPLC/QQQ MRM MS-based pseudotargeted method displayed better repeatability and wider linear range than the traditional UHPLC/Q-TOF MS-based untargeted metabolomics method, and no complicated peak alignment was required. The developed method was applied to discover serum biomarkers for patients with hepatocellular carcinoma (HCC). Patients with HCC had decreased lysophosphatidylcholine, increased long-chain and decreased medium-chain acylcarnitines, and increased aromatic and decreased branched-chain amino acid levels compared to healthy controls. The novelty of this work is that it provides an approach to acquire MRM ion pairs from real samples, is not limited to metabolite standards, and it provides a foundation to achieve pseudotargeted metabolomic analysis on the widely used LC/QQQ MS platform.

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Pseudotargeted metabolic profiling is a novel strategy combining the advantages of both targeted and untargeted methods. The strategy obtains metabolites and their product ions from quadrupole time-of-flight (Q-TOF) MS by information-dependent acquisition (IDA) and then picks targeted ion pairs and measures them on a triple-quadrupole MS by multiple reaction monitoring (MRM). The picking of ion pairs from thousands of candidates is the most time-consuming step of the pseudotargeted strategy. Herein, a systematic and automated approach and software (MRM-Ion Pair Finder) were developed to acquire characteristic MRM ion pairs by precursor ions alignment, MS(2) spectrum extraction and reduction, characteristic product ion selection, and ion fusion. To test the reliability of the approach, a mixture of 15 metabolite standards was first analyzed; the representative ion pairs were correctly picked out. Then, pooled serum samples were further studied, and the results were confirmed by the manual selection. Finally, a comparison with a commercial peak alignment software was performed, and a good characteristic ion coverage of metabolites was obtained. As a proof of concept, the proposed approach was applied to a metabolomics study of liver cancer; 854 metabolite ion pairs were defined in the positive ion mode from serum. Our approach provides a high throughput method which is reliable to acquire MRM ion pairs for pseudotargeted metabolomics with improved metabolite coverage and facilitate more reliable biomarkers discoveries.