Cysteine‐capped ZnSe quantum dots as affinity and accelerating probes for microwave enzymatic digestion of proteins via direct matrix‐assisted laser desorption/ionization time‐of‐flight mass spectrometric analysis

Fluorescent semiconductor quantum dots (QDs) exhibit great potential and capability for many biological and biochemical applications. We report a simple strategy for the synthesis of aqueous stable ZnSe QDs by using cysteine as the capping agent (ZnSe‐Cys QDs). The ZnSe QDs can act as affinity probes to enrich peptides and proteins via direct matrix‐assisted laser desorption/ionization time‐of‐flight mass spectrometry (MALDI‐TOFMS) analysis. This nanoprobe could significantly enhance protein signals (insulin, ubiquitin, cytochrome c, myoglobin and lysozyme) in MALDI‐TOFMS by 2.5–12 times compared with the traditional method. Additionally, the ZnSe‐Cys QDs can be applied as heat absorbers (as accelerating probes) to speed up microwave‐assisted enzymatic digestion reactions and also as affinity probes to enrich lysozyme‐digested products in MALDI‐TOFMS. Furthermore, after the enrichment experiments, the solutions of ZnSe‐Cys QDs mixed with proteins can be directly deposited onto the MALDI plates for rapid analysis. This approach shows a simple, rapid, efficient and straightforward method for direct analysis of proteins or peptides by MALDI‐TOFMS without the requirement for further time‐consuming separation processes, tedious washing steps or laborious purification procedures. The present study has demonstrated that ZnSe‐Cys QDs are reliable and potential materials for rapid, selective separation and enrichment of proteins as well as accelerating probes for microwave‐digested reactions for proteins than the regular MALDI‐MS tools. Additionally, we also believe that this work may also inspire investigations for applications of QDs in the field of MALDI‐MS for proteomics. Copyright © 2009 John Wiley & Sons, Ltd.     

Novel long chain unsaturated diisocyanate from fatty acid: Synthesis,characterization, and application in bio‐based polyurethane

A novel long chain linear unsaturated terminal diisocyanate, 1,16‐diisocyanatohexadec‐8‐ene (HDEDI) was synthesized from oleic acid via Curtius rearrangement. Its chemical structure was identified by FTIR, ¹H NMR, ¹³C NMR, and HRMS. This diisocyanate was used as a starting material for the preparation of entirely bio‐based polyurethanes (PUs) by reacting it with canola diol and canola polyol, respectively. The physical properties and crystalline structure of the PUs prepared from this diisocyanate were compared to their counterparts prepared from similar fatty acid‐derived diisocyanate, 1,7‐heptamethylene diisocyanate (HPMDI). The HDEDI based PUs demonstrated various different properties compared to those of HPMDI based PUs. For example, HDEDI based PUs exhibited a triclinic crystal form; whereas HPMDI based PUs exhibited a hexagonal crystal lattice. In addition, canola polyol‐HDEDI PU demonstrated a higher tensile strength at break than that of canola polyol‐HPMDI, attributed to the higher degree of hydrogen bonding associated with the former sample. Nevertheless, lower Young's modulus and higher elongation in canola polyol‐HDEDI PU were obtained because of the flexibility of the long chain introduced by the HDEDI diisocyanate. © 2010 Wiley Periodicals, Inc. J Polym Sci Part A: Polym Chem 48: 3302–3310, 2010     

Aziridination of 3-methyl-4-nitro-5-styrylisoxazoles

Herein we describe the preparation of a novel class of isoxazolyl aziridines. The products were obtained exclusively as cis diastereoisomers.     

Learning and forgetting effects on a group scheduling problem

This paper proposes to investigate learning and forgetting effects on the problem of scheduling families of jobs on a single machine to minimize total completion time of jobs. A setup time is incurred whenever the single machine transfers job processing from a family to another family. To analyze the impact of learning and forgetting on this group scheduling problem, we structure three basic models and make some comparisons through computational experiments. The three models, including no forgetting, total forgetting and partial forgetting, assume that the processing time of a job is dependent on its position in a schedule. Some scheduling rules and a lower bound are derived in order to constitute our branch-and-bound algorithm for searching an optimal sequence. In addition, an efficient and simply-structured heuristic is also built to find a near-optimal schedule.