Skip to content

Nanosheets and Capsids from Precise Gold Nanoclusters


Assembling of colloidal particulate building blocks into 2D and 3D structures, referred to as superlattices (SLs),1-3 has been contributing to many practical applications.4-7 The essential criterion for constructing these structures is the synthesis of monodisperse nanoparticles. Although there have been enormous efforts,8-9 controlling the size and homogeneity of nanoparticles is still a challenging task due to the difficulty originated from the huge number of atoms as well as lacking a strong structure.

Recently, a collaborative work10 between research groups from University of Jyväskylä and Aalto University published on Angewandte Chemie International Edition revealed a novel type of supracolloidal self-assembly structure made possible by gold nanoclusters, resulting in 2D crystals and 3D capsids. Different from generally accepted pathway which normally use nanoparticles as building blocks, this work started with atomically precise nanoclusters, i.e. Au102(pMBA)44, (pMBA = p-mercaptobenzoic acid) which guaranteed the unparalleled monodispersity towards successful assembling. The gold clusters linked with each other efficiently with the aid of pMBA-based hydrogen bonding, and formed template-free 2D nanosheets and closed spherical capsids.

There are two key points for the assembly in this work. 1) The cluster is not spherical and the ligands point towards the equatorial plane, favoring the inter-cluster hydrogen bonding; 2) It is easy to control the carboxylic acid deprotonation by adjusting solvent condition. The gold clusters were firstly synthesized based on a previous method in which tetrachloroauric(III) acid was reduced by sodium borohydride in the presence of ligands in aqueous solution.11-12 Sodium hydroxide was then used to partially deprotonate the ligands. With coexistence of neutral carboxylic acid groups and deprotonated carboxylate groups, clusters were dialyzed against methanol to generate template-free nanosheets, which was a great advancement compared to conventional solvent casting method requiring a substrate.

Spherical capsids constructed by monolayer of clusters were efficiently prepared by mixing aqueous cluster solution with methanol. The capsid structures were preserved by hydrogen bonding dimerization as well as the electrostatic interactions between cations and negatively charged carboxylates. It was noteworthy that the hydrogen bonding did not only serve as inter-cluster connections, but also enabled joining of different capsids, shedding light on the possibility to design lightweight framework materials.

Looking into the future, the reported assembling method could be used for various number of gold atoms such as the popular Ag25 and Au38 etc.13 As different clusters possess quite distinctive properties, it is feasible to realize adjustment of size, specific structures, and stability of assembled 2D and 3D structures. As the closest counterpart of gold among other noble metal systems, atomically precise silver nanoclusters have also been successfully synthesized in recent years.14-20 With broad applications in many areas such as water filtration,21 sensing,22 catalysis,23-24 textile industry,25 and biology,26-27 it can be expected that assembling silver nanoclusters into higher dimensional structures will gain increasing attention and achieve fast development soon after.


  1. Collier, C. P.; Vossmeyer, T.; Heath, J. R., Nanocrystal superlattices. Annual Review of Physical Chemistry 1998, 49, 371-404.
  2. Prasad, B. L. V.; Sorensen, C. M.; Klabunde, K. J., Gold nanoparticle superlattices. Chemical Society Reviews 2008, 37 (9), 1871-1883.
  3. Goubet, N.; Pileni, M. P., Analogy Between Atoms in a Nanocrystal and Nanocrystals in a Supracrystal: Is It Real or Just a Highly Probable Speculation? The Journal of Physical Chemistry Letters 2011, 2 (9), 1024-1031.
  4. Sperling, R. A.; Rivera Gil, P.; Zhang, F.; Zanella, M.; Parak, W. J., Biological applications of gold nanoparticles. Chemical Society Reviews 2008, 37 (9), 1896-1908.
  5. De, M.; Ghosh, P. S.; Rotello, V. M., Applications of Nanoparticles in Biology. Advanced Materials 2008, 20 (22), 4225-4241.
  6. Mayer, K. M.; Hafner, J. H., Localized Surface Plasmon Resonance Sensors. Chemical Reviews 2011, 111 (6), 3828-3857.
  7. Astruc, D.; Lu, F.; Aranzaes, J. R., Nanoparticles as Recyclable Catalysts: The Frontier between Homogeneous and Heterogeneous Catalysis. Angewandte Chemie International Edition 2005, 44 (48), 7852-7872.
  8. Kwon, S. G.; Hyeon, T., Colloidal Chemical Synthesis and Formation Kinetics of Uniformly Sized Nanocrystals of Metals, Oxides, and Chalcogenides. Accounts of Chemical Research 2008, 41 (12), 1696-1709.
  9. Kwon, S. G.; Hyeon, T., Formation Mechanisms of Uniform Nanocrystals via Hot-Injection and Heat-Up Methods. Small 2011, 7 (19), 2685-2702.
  10. Nonappa; Lahtinen, T.; Haataja, J. S.; Tero, T.-R.; Häkkinen, H.; Ikkala, O., Template-Free Supracolloidal Self-Assembly of Atomically Precise Gold Nanoclusters: From 2D Colloidal Crystals to Spherical Capsids. Angewandte Chemie International Edition 2016, n/a-n/a.
  11. Salorinne, K.; Lahtinen, T.; Malola, S.; Koivisto, J.; Hakkinen, H., Solvation chemistry of water-soluble thiol-protected gold nanocluster Au102 from DOSY NMR spectroscopy and DFT calculations. Nanoscale 2014, 6 (14), 7823-7826.
  12. Lahtinen, T.; Hulkko, E.; Sokolowska, K.; Tero, T.-R.; Saarnio, V.; Lindgren, J.; Pettersson, M.; Hakkinen, H.; Lehtovaara, L., Covalently linked multimers of gold nanoclusters Au102(p-MBA)44 and Au[similar]250(p-MBA)n. Nanoscale 2016, 8 (44), 18665-18674.
  13. Jin, R., Quantum sized, thiolate-protected gold nanoclusters. Nanoscale 2010, 2 (3), 343-362.
  14. Bakr, O. M.; Amendola, V.; Aikens, C. M.; Wenseleers, W.; Li, R.; Dal Negro, L.; Schatz, G. C.; Stellacci, F., Silver Nanoparticles with Broad Multiband Linear Optical Absorption. Angewandte Chemie International Edition 2009, 48 (32), 5921-5926.
  15. AbdulHalim, L. G.; Ashraf, S.; Katsiev, K.; Kirmani, A. R.; Kothalawala, N.; Anjum, D. H.; Abbas, S.; Amassian, A.; Stellacci, F.; Dass, A.; Hussain, I.; Bakr, O. M., A scalable synthesis of highly stable and water dispersible Ag44(SR)30 nanoclusters. Journal of Materials Chemistry A 2013, 1 (35), 10148.
  16. Desireddy, A.; Conn, B. E.; Guo, J.; Yoon, B.; Barnett, R. N.; Monahan, B. M.; Kirschbaum, K.; Griffith, W. P.; Whetten, R. L.; Landman, U.; Bigioni, T. P., Ultrastable silver nanoparticles. Nature 2013, 501 (7467), 399-402.
  17. Joshi, C. P.; Bootharaju, M. S.; Alhilaly, M. J.; Bakr, O. M., [Ag25(SR)18]−: The “Golden” Silver Nanoparticle. Journal of the American Chemical Society 2015, 137 (36), 11578-11581.
  18. Dhayal, R. S.; Liao, J.-H.; Liu, Y.-C.; Chiang, M.-H.; Kahlal, S.; Saillard, J.-Y.; Liu, C. W., [Ag21{S2P(OiPr)2}12]+: An Eight-Electron Superatom. Angewandte Chemie International Edition 2015, 54 (12), 3702-3706.
  19. Yang, H.; Wang, Y.; Chen, X.; Zhao, X.; Gu, L.; Huang, H.; Yan, J.; Xu, C.; Li, G.; Wu, J.; Edwards, A. J.; Dittrich, B.; Tang, Z.; Wang, D.; Lehtovaara, L.; Hakkinen, H.; Zheng, N., Plasmonic twinned silver nanoparticles with molecular precision. Nat Commun 2016, 7.
  20. Russier-Antoine, I.; Bertorelle, F.; Hamouda, R.; Rayane, D.; Dugourd, P.; Sanader, Z.; Bonacic-Koutecky, V.; Brevet, P.-F.; Antoine, R., Tuning Ag29 nanocluster light emission from red to blue with one and two-photon excitation. Nanoscale 2016, 8 (5), 2892-2898.
  21. Jain, P.; Pradeep, T., Potential of silver nanoparticle-coated polyurethane foam as an antibacterial water filter. Biotechnology and Bioengineering 2005, 90 (1), 59-63.
  22. Kumar, V. V.; Anthony, S. P., Coordinating ligand functionalized AgNPs for colorimetric sensing: effect of subtle structural and conformational change of ligand on the selectivity. RSC Advances 2014, 4 (110), 64717-64724.
  23. Zhong, L.; Yang, T.; Wang, J.; Huang, C. Z., Study of Catalytic Ability of in situ Prepared AgNPs-PMAA-PVP Electrospun Nano?bers. New Journal of Chemistry 2015.
  24. Mortazavi, S. S.; Farmany, A., Catalytic-oxidation of Janus green in the presence of AgNPs: Application to the determination of iodate. Journal of Industrial and Engineering Chemistry 2014, 20 (6), 4224-4226.
  25. Yeo, S.; Lee, H.; Jeong, S., Preparation of nanocomposite fibers for permanent antibacterial effect. J Mater Sci 2003, 38 (10), 2143-2147.
  26. Devi, L. B.; Das, S. K.; Mandal, A. B., Impact of Surface Functionalization of AgNPs on Binding and Conformational Change of Hemoglobin (Hb) and Hemolytic Behavior. The Journal of Physical Chemistry C 2014, 118 (51), 29739-29749.
  27. Loza, K.; Diendorf, J.; Sengstock, C.; Ruiz-Gonzalez, L.; Gonzalez-Calbet, J. M.; Vallet-Regi, M.; Koller, M.; Epple, M., The dissolution and biological effects of silver nanoparticles in biological media. Journal of Materials Chemistry B 2014, 2 (12), 1634-1643.

News & Views: Ag136 and Ag374 Plasmonic Nanoparticles with Atomically Precise Composition

On September 9, a collaborative study led by Professors Nanfeng Zheng at Xiamen University, Hannu Hakkinen at University of Jyvaskyla, and Alison Edwards at Australian Centre for Neutron Scattering entitled “Plasmonic Twinned Silver Nanoparticles with Molecular Precision“1 was published on Nature Communications (DOI: 10.1038/ncomms12809).

In this work, a new type of Ag136 and Ag374 species protected by 4-tert-butylbenzenethiolate were chemically synthesized and structurally resolved by X-ray crystallography. It is noteworthy that, although these silver nanoparticles (Ag NPs) were identified with atomically precise composition, these species showed plasmonic optical characteristics (Figure 1).1 Atomically precise silver species, taking Ag44 as an example, typically possess characteristic optical absorption peaks and thus were initially described as intensely and broadly absorbing nanoparticles (IBANs)2 before structurally ressolved.3 The ultraviolet–visible absorption (UV-vis) of Ag136 and Ag374 reported in this work was nowhere close to molecular species and showed clear metallic features.


Figure 1. UV-vis spectra of experimental and computed Ag136 (a) and experimental Ag374 (b) NPs.1 The figure is used under a Creative Commons CC-BY license and the corresponding authors were fully acknowledged. Copyright 2016, Nature Publishing Group.

These Ag NPs were observed to have diameters around 2-3 nm which did not come from the irradiation of electron beam as the case of many Ag or Au nanoclusters. It is known that electron microscopy images are not representative for cluster size characterization due to metal growth and agglomeration under electron beam. However, the scanning transmission electron microscope (STEM) and high-resolution TEM (HRTEM) images (Figure 2) showed that the lattices of these particles were nicely ordered into fivefold twinning, which was indicative for nanoparticles of face-centered cubic (fcc) structures.4


Figure 2. STEM and HRTEM (inset) images of small (a) and large (b) 4-tert-butylbenzenethiolate-protected Ag NPs. Scale bars, 2 nm.1 The figure is used under a Creative Commons CC-BY license and the corresponding authors were fully acknowledged. Copyright 2016, Nature Publishing Group.

On the other hand, these particles were distinctive from conventional nanoparticles in the sense that they only contained one size rather than mixture of a broad range of particles. The uniformity of the particle size and composition made them unique and interesting to further investigate.

This study opened a door to a more vaguely defined area between nanoparticles and nanoclusters. It showed that atomically precise silver could be prepared with larger size that turned their properties from molecular into metallic type. It could lead to new insights that are helpful for mechanistic investigations and better understanding of the particle formation process.

For more background and details, see Opinion paper: Yangwei Liu, Plasmonic Silver Nanoparticles with Atomically Precise Composition. Journal of Nanomedicine Research, 2016 4(2): 00083. DOI: 10.15406/jnmr.2016.04.00083

1. Yang, H., et al., Plasmonic Twinned Silver Nanoparticles with Molecular Precision. Nat Commun 2016, 7:12809.
2. Bakr, O. M., et al., Silver Nanoparticles with Broad Multiband Linear Optical Absorption. Angew Chem Int Ed 2009, 48, 5921-5926.
3. Harkness, K. M., et al., Ag-44(Sr)(30)(4-): A Silver-Thiolate Superatom Complex. Nanoscale 2012, 4, 4269-4274.
4. Xia, Y., et al., Shape-Controlled Synthesis of Metal Nanocrystals: Simple Chemistry Meets Complex Physics? Angew Chem Int Ed 2009, 48, 60-103.

Automatic Data Analysis in MATLAB for MRI and NMR T1/T2 Measurements

MATLAB for NMR MRI T1 T2 Data Analysis600


Relaxation is one of the most important information in magnetic resonance imaging (MRI) and nuclear magnetic resonance (NMR) spectroscopy for medical applications, material characterizations, and reaction mechanism studies. Relaxation can be measured by using two separate processes which generate longitudinal relaxation time, T1, and the transverse relaxation time, T2, respectively. Both T1 and T2 measurements involve carrying out 2D NMR/MRI experiments during which a series of spectra are measured using different time delays (tau) for each spectrum.


The procedures to get T1 and T2 values are not so straightforward. Even worse, these procedures are often extremely time consuming, tedius, and requiring use of multiple software. Conventional data processing for T1 and T2 includes following steps.

1. Baseline correction for each spectrum with changing time delay;
2. Phase adjustment for each spectrum;
3. For T2, find peak position for each spectrum (fixed point for T1);
4. Read peak height for real signal and write down the values;
5. Input tau & peak height into scientific software such as Igor or Origin to plot figures;
6. Need to manually choose initial values for fitting to get T1/T2.

Doing all these steps could take as long as 30-40 min for a single T1/T2 experiment depending on how experienced the person is with this process.

Programming in MATLAB to Automatically Process T1/T2 Data

A typical experiment contains ten to twenty spectra. The first thing in processing is exporting raw data as a single text file. Then copy the table of tau values with data file name into a .txt file. The raw data and this text file will be loaded by MATLAB using “textread” function. Remember to look into data file to find how many header lines need to be skipped.

The next step is baseline correction. As discussed in a previous article, data acquisition software that come with the instrument could have incorrect baseline correction results in some cases. So we can use our own algorithm as the first part of T1/T2 data processing. We can plot the spectra before and after correction for a brief review. Then adjust phase or alternatively use magnitude values by calculating square root of the sum of real square and imaginary square. Using the tau values from the text file to read the signal at tau position for T2 or at fixed point for T1. When we got the values, generate Excel file containing tau and (echo max) values we just read using “writetable” function which can assign file name and the starting cell to write. In order to get T1/T2, we need to plot Echo vs tau graph and fit the curve by following equations.


Scripts for fitting

Please note that for saturation recovery, the fitting function for T1 is y = a*(1-exp(-x/T1)). However, if you do inversion recovery where the initial magnetization is inverted, then you will have a coefficient 2 in front of the “exp”. In order to combine these cases, I used a “b” in a generalized function as shown above.

y=a*exp(b*x), coefficient b=-1/T2

Scripts for fitting

Then use “coeffvalues” to get the fitted parameters from “T1f“. One can also give some initial values to the fitting fuction for better results. Lastly, save the T1/T2 graphs with fitting curves as .png or other type of image files and write the T1/T2 values in the same Excel file that generated previously.

A summary of the processing steps in my script

1. Read .txt parameter file and data file;
2. Baseline correction for real and imag data in each experiment;
3. Draw graphs for spectra before and after correction for review;
4. Phase adjustment or calculate magnitude values from baseline corrected real and imaginary data;
5. Read tau values from parameter file and read Mag signal at fixed point (for T1) or Echo Max (for T2);
6. Generate a table of tau and Echo and save as a Excel file;
7. Draw Echo vs tau graph and fit the curve to get T1/T2 values;
8. Save the T1/T2 graph with fitting curve as a .png file;
9. Write the T1/T2 values in the same Excel file.
By programming in MATLAB, we can minimize steps that need manual operations. The new procedure only contains 3 steps.

1. Export 2D experiments as a text file as usual;
2. Copy tau table into tau.txt;
3. Open MATLAB and hit Run.


T2 export  tau tableExport data from TNMR software and copy tau table and file name into text file (I use TNMR software as an example since it’s the one that works with my NMR spectrometer. There are a bunch of other choices.)

Besides, as a fun part, one can add a dialog using “msgbox” to show summary of performed tasks and calculate time for processing. As we can see, the whole procedure only takes 7 seconds to finish. T1/T2 data processing becomes a breeze.


Baseline and MATLAB Summary dialogBaseline corrected spectra and summary dialog

T1 T2 fit result table ExcelGenerated Excel files containing T1/T2 fitting results

T1 Graph with Fitting CurveGenerated T1 graph with fitting curve (inset figure: manually done whole process for comparison)

T2 Graph with Fitting CurveGenerated T2 graph with fitting curve (inset figure: manually done whole process for comparison)

If my experience can be of any help to your research or inspire your great idea, I will be very happy. Please don’t hesitate to share your thoughts if you have criticisms or suggestions.
Related Articles

Programming in MATLAB for Data Analysis – Baseline Correction for NMR Spectra

Programming in MATLAB for Data Analysis – Baseline Correction for NMR Spectra

MATLAB for NMR Baseline Data Analysis title

In my research, doing Nuclear Magnetic Resonance (NMR) for material characterization and mechanism study is an important part of my work. Although every scientific instrument has its own data processing software, there are some clear disadvantages which may lead to incorrect results or low efficiency. Unfortunately, the processing tools that come with instruments are normally proprietary software, meaning you would never know what the software is actually doing when you click each button, even for simplest processing such as baseline correction, curve fitting, integration, and Fourier Transform. This situation is particularly inconvenient when there is an unexpected output since one cannot determine whether it’s due to a flaw in the algorithm or the experiment itself.

In this blog article, I will share how I take initiative to solve the problem by writing scripts in MATLAB and use its tools to perform automatic NMR data processing which would save countless hours for people who intensively work with NMR. Due to the length, I am planning to write another article talking about how to programming in MATLAB to turn time-consuming and tedious T1/T2 data analysis process into a breeze.


Problem that needs to be solved

Baseline correction is a basic yet extremely important step of NMR data analysis. The raw data that is acquired by the spectrometer should (but in most cases does not) have a baseline at zero. When the spectrum has an offset from zero, it will give rise to many problems such as incorrect integral values, additional peaks in Fourier space, and being unable to do parallel comparison between experiments etc. Thus, it is usually the first thing one needs to do before further processing. Every NMR software has that function.

However, not all of the commercial software does that as we expect every time. Following screenshot shows a problem that I encountered when using a very popular NMR software to do baseline correction. The red line indicates zero where the base should be. It is obvious that the processed spectrum (lower figure) has a shape that differs from the original one (upper figure).

NMR Baseline Correction Problem from Commercial Software
Screenshot showing baseline correction error from using a NMR software (time domain)



I don’t know how does that strange shape come from since it is a proprietary software. But if we look into the principle of baseline correction, we may have a few ideas. As in the following schematic, raw data normally has an offset from zero and sometimes a slope, too. Zero order baseline correction only subtracts raw data with its mean value and moves the line to x axis. First order correction fits with a straight line (green dashed line) and subtracts the raw data by that to get rid of the slope and intercept. If the baseline has a curvature, a second or higher order treatment can also be used, but that is rarely the case in NMR.

The ideal correction should only consider the “baseline” part while ignoring the actual signal. But if the actual signal is taken into the correction, as shown in the last figure of the schematic, the corrected baseline as well as the signal will be distorted.

Principle of Baseline Correction and Problem
Principle of baseline correction and possible reason for the problem

By looking at the spectrum, one can see that the signal is always at the beginning of the time domain and decays to zero gradually. Thus, we can deal with this problem by carrying out linear fitting using only the last part of the data. I use a text file to store file names that will be processed, and use “for” loops to process as many files as necessary one after the other. When dealing with lots of raw data, this may greatly increase the efficiency of data processing. For the linear fitting, one can write one’s own algorithm according to the following linear equation.

f(x) = a*x + b

A second and more convenient way is to use MATLAB’s “polyfit” function, which generates a two-value array as its result. The first value is parameter a and the second is b in the above function.


A summary of the processing steps in my script
1. Read baseline.txt to get all file names of raw data, then load data into matrices;
2. Use last 1/5 or 1/4 of the data points, depending on experiment, for fitting;
3. Subtract raw data by fitted line;
4. Baseline correction for the next file til all are done;
5. Plot all experiments comparing before and after baseline correction for quick review;
6. Save baseline corrected .txt files & use original file names adding “_baseline” to the end.

The figure below is the output for the same data that we just see in the error screenshot. It’s done perfectly and the signals are well preserved.

Baseline correction results by programming in MATLAB

Improved baseline correction results by using MATLAB

A friendly reminder: pay attention to the end of the raw data because some experimental software may add additional zeros or other values after acquisition. So you may want to modify algorithm accordingly.

If my experience can be of any help to your research or inspire your great idea, I will be very happy. Please don’t hesitate to share your thoughts if you have criticisms or suggestions.


Related articles

Automatic Data Analysis in MATLAB for MRI and NMR T1/T2 Measurements

Nanoparticle Synthesis and Assembly Faraday Discussion: Recap and My Work

Nanoparticle_Synthesis_Assembly_Faraday_Discussion1Advanced Photon Source at Argonne National Laboratory, site for Faraday Discussion

Faraday Discussion, organized by Faraday Division of Royal Society of Chemistry in UK, is one of the most important international scientific conferences focused on physical chemistry and related fields. The Discussion is named after 18th century English scientist Michael Faraday who discovered electromagnetic induction, and it has been a high impact conference for over 100 years.

Faraday Discussion has a special form that all the presentations are full length research papers which are distributed to all participants before the meeting. The majority of the meeting time is to discuss the papers. Everyone contributes to the discussion by challenging or commenting on the authors’ work and can present their own research results. The presented papers and an organized written form of the discussion are published in the journal Faraday Discussions (Impact Factor 4.606). The discussions may contain original ideas, valuable suggestions, and new results, which are as important as the presented papers, and thus they are formatted as articles and can be cited independently.

Nanoparticle Synthesis and Assembly Faraday Discussion was held at Argonne National Laboratory on April 20-22, 2015. This was the 3rd time that the conference took place in the United States. The topics of this Faraday Discussion were focused on nanoparticle synthetic methods, theoretical insights, self-assembly and directed assembly.

Relevant Talks and Posters

The introductory lecture was given by Dr. Paul Alivisatos (Lawrence Berkeley National Laboratory) who received Spiers Memorial Award from the conference. Dr. Alivisatos showed a series of fascinating studies using in situ Transmission Electron Microscope (TEM).

Dr. Christophe Petit (Pierre and Marie Curie University, France) introduced his achievement in synthesis of shape-controlled Pt, PtCo, and PtPd nanoparticles, which could be used as high performance catalysts for fuel cells. They investigated reaction process by changing parameters such as elemental composition in the alloy etc.

Dr. Christina Graf (Freie Universitaet Berlin, Germany) presented interesting results of their kinetic study on the aggregation and growth of PEG-protected gold nanoparticles (Au NPs) in halide solutions.

Dr. Brian Korgel (University of Texas at Austin) discussed the heating effect on the nanocrystal superlattices. They found that upon increasing temperature, the C18SH protected Au NPs switched from disordered state into ordered BCC packing structure. Interestingly, this process is reversible when cooling down.

Dr. Matthew Martin (Khalifa University, United Arab Emirates) presented his poster about synthesis, self-assembly, and dis-assembly of thiolate protected nanoparticles. The highlight of their research is that they could synthesize homogeneous particle lattice membrane as large as several inches long.

My Work

Yangwei_Liu_Poster_Faraday_Discussion2015I presented my poster entitled “Synthesis of Highly Monodisperse Alkanethiolate Protected Silver Nanoparticles by Modified Aging Process”. In recent years, a series of progress has been achieved to understand the synthetic process for noble metal nanoparticles smaller than 5 nanometers. Great emphasis has been placed on resolving the precursor states in the existed studies. However, relatively less efforts have been taken to explore the post-synthesis aging process. In this presentation, we report our recent progress on the fine control of alkanethiolate-protected silver nanoparticles (Ag NPs) by using a modified method combining with digestive ripening under a range of conditions. The Ag NPs of 3.4 nm have been successfully synthesized with very high homogeneity (7%). A series of influential factors including Ag : ligand ratio, carbon chain length, and temperature have been systematically investigated. The chemical explanation has also been proposed. This study will lead to deeper understanding of the particle growth, and provide better control over the size and homogeneity of the Ag NPs for further applications.

Nanoparticle_Faraday_Discussion_David_Schiffrin_Yangwei_LiuPhoto with Dr. David Schiffrin during Faraday Discussion at Argonne National Lab

It was an honor to meet with Dr. David Schiffrin (University of Liverpool), the inventor of the widely used nanoparticle synthetic route Brust-Schiffrin Method (BSM), during the break of the meeting. I talked with him about my silver nanoparticle synthesis and my research of improving this method to better fit silver system, which is much more difficult to deal with than the original gold system. Dr. Schiffrin was kind enough to share his insight into the development and encouraged my efforts.

Award and Honor

I am so delighted to be awarded with Registration Fee Waiver as well as a Bursary from the Faraday Division of Royal Society of Chemistry.

Related Links

1. Nanoparticle Synthesis and Assembly Faraday Discussion
2. The journal Faraday Discussions, Volume 181, Aug 2015
3. My publications in this volume
Faraday Discussions, 2015, 181, 147-179.
Faraday Discussions, 2015, 181, 299-323.
Faraday Discussions, 2015, 181, 365-381.

My Blog Picture Used in IACIS Newsletter

Screen shot for IACIS newsletter 58 December 2014

Screen shot for IACIS newsletter 58 December 2014

I wrote a blog article in August 2014 as a recap for the 88th American Chemical Society Colloid & Surface Science Symposium that was held at the University of Pennsylvania. The article introduced the symposium in general, interesting talks, and my own work. I shot some pictures on campus and treated one of them with artistic effects.

A few month later, Professor Ger Koper from Delft University of Technology who is newsletter editor for International Association of Colloid and Interface Scientists (IACIS), sent me an email asking if I’d like to have my blog picture used in IACIS newsletter 58. I was thrilled to hear that and certainly agreed to the usage and appreciated his asking.

I am enthusiastic about contributing my expertise to the scientific community as well as connecting the latter with general public. This is why I started my research blog. The appearance of the picture on IACIS newsletter would undoubtedly increase the visibility of my blog. This encouraged me to continue writing and sharing more of my research experience, insights, news, and non-confidential achievements no matter how crazy my schedule is. If any person who read my articles feels like they are useful or inspirational, that would be the greatest payoff to my hard work.

Related links: 

IACIS Newsletter 58 (December 2014)

International Association of Colloid and Interface Scientists (IACIS)

My blog “Recap for 88th ACS Colloid & Surface Science Symposium”

88th ACS Colloid & Surface Science Symposium

Similar blog articles:

2014 DOE Annual Merit Review Meeting (Fuel Cell Topics Recap)
Highlights of 58th EIPBN Conference
A Brief Note for ISMPC13
2013 DOE Catalysis Working Group Meeting

How a Damaged Smart Phone Increased My Efficiency

Time chart

Several months ago, my cell phone got a water incident and was seriously damaged. After that, the phone frequently jumped out of apps and went back to the home screen. The only function that was relatively less affected was making calls.

I used to be a heavy user of apps and spent quite some time everyday using my phone writing emails, checking Facebook, looking through tweets, watching news, reading blog posts, update apps and so on. It had become a part of my life. Now that it turned into a “traditional phone”, and I did not plan to buy a replaced one before the release of a new version, so I decided to live with it and see how my daily life would change.

It was difficult at the beginning. Every time I took out my phone when I was walking, dining, taking breaks from work, feeling bored, before sleeping, and after waking up, all I could do is to (uncomfortably) put it back again. A week later, I was kind of getting used to it. Checking emails and social media and things alike seemed to be not so urgent as before – and they are actually not. There were no more interrupting emails, chat messages, or alerts during work. I had a feeling of being freed from a prison made of an unnecessary routine. And I had more time and peace of mind to focus on what is important.

Now, although I have bought a new smart phone already, I am more cautious than ever about the frequency that I use and the time spent on it. In order to better control my time, I set a 15-min restriction per day for each social media, and a limit of email checking for no more than once every hour. The key supporting idea is that, there are no such things that are so urgent for me to check my phone every so often. Otherwise, they would come to me through more direct ways such as a call or face-to-face talk.Time chart words

The following is a comparison of the time I spent before and after the change. Note that there is a total of amazing 2-hour difference in one day.


Bottom line: I am not neglecting the convenience that smart phones have brought to us. However, when properly used and well controlled, they can make our life much better.

%d bloggers like this: