No abstract

No abstract

Encoderless combinatorial chemistry requires high-throughput product identification without the use of chemical or other tags. We developed a novel nano-layered substrate plate and combined it with a microarraying robot, matrix-assisted laser desorption/ionization (MALDI) mass spectrometry, and custom software to produce a high-throughput small molecule identification system. To optimize system performance, we spotted 5 different chemical entities, spanning a m/z range of 195 to 1338, in 20,304 spots for a total of 101,520 molecules. The initial spot identification rate was 99.85% (20,273 spots), and after a proofreading algorithm was added, 100% of 20,304 spots and 101,520 molecules were identified. An internal recalibration algorithm also significantly improved mass accuracy to as low as 45 ppm. Using this optimized system, 47 different chemical entities, spanning a m/z range of 138 to 1,592, were spotted over 5,076 spots and could be identified with 100% accuracy. Our study lays the foundation for improved encoderless combinatorial chemistry.

We have developed low voltage driving organic light-emitting devices using triphenylphosphine oxide (Ph3PO) layers. The devices with a Ph3PO layer show high current density at a low voltage. For example, the current density of 20mA∕cm2 is achieved at a low voltage of 2.9V for the device consisted of 4,4′,4″-tris[N-(2-naphthyl)-N-phenyl-amino]-triphenylamine (2-TNATA), 4,4′-bis(2,2′-diphenylvinyl)-1,1′-biphenyl (DPVBi), and Ph3PO layers. Due to the good electron conduction property of Ph3PO, a luminance of 1017cd∕m2 is achieved at a low voltage of 3.0V in a device with a structure of ITO/2-TNATA/DPVBi:rubrene (1%,10nm)∕DPVBi (30nm)∕Ph3PO (60nm)∕LiF∕Al.

Arctium lappa L. (Asteraceae), also known as burdock, has a long history of cultivation as a dietary vegetable worldwide. Stress in plants disrupts metabolic homeostasis and requires adjustment of metabolic pathways. Exposure to heavy metals is one of the most prevalent environmental stresses encountered by plants. In this study, metabolite profiling based on H NMR and GC-MS was used to obtain a holistic view of the response of burdock roots to copper stress. The principal component analysis model generated from the NMR data showed significant separation between groups. Copper-treated burdock roots were characterized by increased levels of phenols and decreased levels of primary metabolites. These results suggest that copper stress leads to activation of the phenylpropanoid pathway and growth inhibition. GC-MS analyses revealed increased levels of unsaturated fatty acids and decreased levels of sterols in the copper-treated group. Changes in metabolite concentrations were analyzed by UPLC/QTRAP-MS, and the significances were confirmed by two-way analysis of variance and Bonferroni's test. Interestingly, linoleic acid was increased about 2.7-fold, from 316 ± 64.5 to 855 ± 111 ppm, in the group treated with copper for 6 days. This study demonstrates that metabolomic profiling is an effective analytical approach to understanding the metabolic pathway(s) associated with copper stress in burdock roots.