Volume 14, Number 3, March 2011
Hot Topic
Nano-Combinatorial Chemistry and Associated Technologies
Guest Editor: Bing Yan
Contents
Editorial Pp. 146
High Speed Screening Technologies in Heterogeneous Catalysis Pp. 147-159
Zhuo Qun Zheng and Xiao Ping Zhou
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Application of Parallel Synthesis and High Throughput Characterization in Photocatalyst Discovery Pp. 160-172
Song Sun, Jianjun Ding, Jun Bao, Zhenlin Luo and Chen Gao
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Glyconanoparticles for Biomedical Applications Pp. 173-181
Chang-Ming Dong
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Impact of Nanomaterials on High Throughput Separation Methodologies Pp. 182-190
Nana Liang and Bin Zhang
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Characterizing the Surface Chemistry of Nanoparticles: An Analogy to Solid-Phase Synthesis SamplesPp. 191-197
Yin Liu and Bing Yan
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Atom Probe Tomography – A High Throughput Screening Tool for Atomic Scale Chemistry Pp. 198-205
Krishna Rajan
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High Throughput Tools for the Study of Protein-Nanostructured Surface Interaction Pp. 206-216
Pasquale Emanuele Scopelliti, Gero Bongiorno and Paolo Milani
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Exploring Quantitative Nanostructure-Activity Relationships (QNAR) Modeling as a Tool for Predicting Biological Effects of Manufactured Nanoparticles Pp. 217-225
Denis Fourches, Dongqiuye Pu and Alexander Tropsha
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Abstracts
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High Speed Screening Technologies in Heterogeneous Catalysis
Zhuo Qun Zheng and Xiao Ping Zhou
Catalyst investigation is a typical “trial and error” process. In order to discover a good catalyst, people usually test a huge number of potential catalyst candidates. High throughput experimentation (HTE) is an efficient methodology for catalyst discovery and optimization. HTE is capable of synthesizing and testing hundreds of catalysts in a short period of time. And it is now becoming a widely applied tool in catalyst discovery and catalyst composition optimization. The development of high throughput experimentation in catalysis includes three steps, which are 1) design and synthesis of catalyst libraries, 2) design of the reactor system for catalytic reactions, and 3) product analysis and data reduction in a high throughput manner. Hence, in this mini-review, the recent developments of high throughput experimentation and these three steps are discussed. The review is focused on the research progress over the last few years.
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Application of Parallel Synthesis and High Throughput Characterization in Photocatalyst Discovery
Song Sun, Jianjun Ding, Jun Bao, Zhenlin Luo and Chen Gao
The last decade has seen significant progresses in the application of combinatorial approaches and high throughput screening in photocatalyst discovery. This paper aims at providing a comprehensive review on the parallel synthesis and high throughput characterization of photocatalysts, including the development of instrumentation, strategy of experiment, preparation of libraries, high throughput screening technique and data analysis. The review ends with a summary of the remaining challenges and prospects on combinatorial photocatalyst discovery.
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Glyconanoparticles for Biomedical Applications
Chang-Ming Dong
Over the past two decades, glycosylated nanoparticles (i.e., glyconanoparticles having sugar residues on the surface) received much attention for biomedical applications such as bioassays and targeted drug delivery. This minireview focuses on three aspects: (1) glycosylated gold nanoparticles, (2) glycosylated quantum dots, and (3) glyconanoparticles self-assembled from amphiphilic glycopolymers. The synthetic methods and the multivalent interactions between glyconanoparticles and lectins is shortly illustrated.
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Impact of Nanomaterials on High Throughput Separation Methodologies
Nana Liang and Bin Zhang
Due to the unique properties, such as their large surface to volume ratio and easy modification, nanomaterials have recently been studied as effective sorbents in the field of separation science. It has proven to be more effective and efficient to use nanoparticles (NPs) as a stationary phase in solid-phase extraction separation. In addition, NPs can be also used as buffer additives in capillary electrophoresis separation. This review highlights recent develop ments in High throughput separation methodologies employing nanomaterials such as carbon nanotubes, gold nanoparticles and magnetic NPs etc.
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Characterizing the Surface Chemistry of Nanoparticles: An Analogy to Solid-Phase Synthesis Samples
Yin Liu and Bing Yan
Chemical modifications of nanoparticle’s (NP’s) surfaces can be used to regulate their activities, remove their toxic effects, and enable them to perform desired functions. Similar to SPS samples, modified NPs also have small–molecules on the surface of a solid support. The need to monitor synthesis, optimize reaction conditions, and characterize the products is quite similar in both situations. FTIR, NMR, MS and other analytical methods have been used as effective methods to analyze surface bound molecules and monitor organic reactions directly, or indirectly, on a solid phase of a resin or a NPs.
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Atom Probe Tomography – A High Throughput Screening Tool for Atomic Scale Chemistry
Krishna Rajan
The objective of this paper is to examine the challenges and opportunities in high throughput screening of atomic scale chemistry via a technique known as atom probe tomography. While there are numerous methods that exist in the field of throughput screening, even at the nanoscale, most of the effort is on screening properties, rather than chemistry and/or structure. In this overview, we discuss the role atom probe tomography can have as a high throughput screening tool of atomic scale chemistry. Particular emphasis on the study of organic/biological materials is given along with the needs and challenges to make atom probe tomography more widespread in the field of combinatorial chemistry.
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High Throughput Tools for the Study of Protein-Nanostructured Surface Interaction
Pasquale Emanuele Scopelliti, Gero Bongiorno and Paolo Milani
The aim of this review is to describe and to analyze the ingredients that are necessary in order to develop a robust and effective experimental approach for the high throughput characterization of protein-nanostructured surface interaction. In the first part of this paper we review the nanostructured surface synthesis methods that are potentially able to create nanostructured inorganic surface libraries. In the second part, we address another fundamental aspect consisting in the availability of high throughput proteins detection methods. We describe in details new emerging analytical tools compatible with nanostructured surfaces, analyzing different possible strategies, depending on the objective of the experiment and on the library format.
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Exploring Quantitative Nanostructure-Activity Relationships (QNAR) Modeling as a Tool for Predicting Biological Effects of Manufactured Nanoparticles
Denis Fourches, Dongqiuye Pu and Alexander Tropsha
Evaluation of desired and undesired, biological effects of Manufactured NanoParticles (MNPs) is of critical importance for the future of nanotechnology. Experimental studies, especially toxicological, are time-consuming and costly, calling for the development of efficient computational tools capable of predicting biological events caused by MNPs from their structures and physical chemical properties. This mini-review assesses the potential of modern cheminformatics methods such as Quantitative Structure – Activity Relationship modeling to develop statistically significant and externally predictive models that can accurately forecast biological effects of MNPs from the knowledge of their physical, chemical, and geometrical properties. We discuss major approaches for model building and validation using both experimental and computed properties of nanomaterials. We consider two different categories of MNP datasets: (i) those comprising MNPs with diverse metal cores and organic decorations, for which experimentally measured properties can be used as particle’s descriptors, and (ii) those involving MNPs possessing the same core (e.g., carbon nanotubes), but different surface-modifying organic molecules, for which computational descriptors can be calculated for a single representative of the decorative molecule. We illustrate those concepts with three case studies for which we successfully built and validated predictive models. In summary, this mini-review demonstrates that, analogous to conventional applications of QSAR modeling for the analysis of datasets of bioactive organic molecules, its application to modeling MNPs that we term Quantitative Nanostructure Activity Relationship (QNAR) modeling can be useful for (i) predicting activity profiles of novel MNPs solely from their representative descriptors and (ii) designing and manufacturing safer nanomaterials with desired properties.