Sequential Growth as a Mechanism of Silver‐Glutathione Monolayer‐Protected Cluster Formation
Source abstract
Abstract
Abstract Silver monolayer‐protected clusters (MPCs) are an important new class of small metal nanoparticles with discrete sizes and unique properties that are eminently tunable; however, a fundamental understanding of the mechanisms of MPC formation is still lacking. Here, the basic mechanism by which silver‐glutathione MPCs form is established by using real‐time in situ optical measurements and ex situ solution‐phase analyses to track MPC populations in the reaction mixture. These measurements identify that MPCs grow systematically, increasing in size sequentially as they transform from one known species to another, in contrast to existing models. In the new sequential growth model of MPC formation, the relative stability of each species in the series results in thermodynamic preferences for certain species as well as kinetic barriers to transformations between stable sizes. This model is shown to correctly predict the outcome of silver MPC synthetic reactions. Simple analytic expressions and simulations of rate equations are used to further validate the model and study its nature. The sequential growth model provides insights into how reactions may be directed, based on the interplay between relative MPC stabilities and reaction kinetics, providing tools for the synthesis of particular MPCs in high yield.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Nanocluster Synthesis and Applications · Gold and Silver Nanoparticles Synthesis and Applications · Quantum Dots Synthesis And Properties
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Chemistry and Biochemistry University of Toledo Toledo OH 43606 USA
Department of Chemistry Eastern Michigan University Ypsilanti MI 48197 USA
The School of Green Chemistry and Engineering University of Toledo Toledo OH 43606 USA
Funding
National Science Foundation
References (58)
Show source-provided references
1. 10.1002/adma.1996008051310.1002/adma.19960080513 ↗
2. 10.1126/science.114862410.1126/science.1148624 ↗
3. 10.1021/ja105836b10.1021/ja105836b ↗
4. 10.1038/nature1252310.1038/nature12523 ↗
5. 10.1038/ncomms342210.1038/ncomms3422 ↗
6. 10.1021/ja01167a00110.1021/ja01167a001 ↗
7. 10.1039/df951110005510.1039/df9511100055 ↗
8. 10.1021/ja00072a02510.1021/ja00072a025 ↗
9. 10.1038/nmat125110.1038/nmat1251 ↗
10. 10.1103/PhysRevLett.52.214110.1103/PhysRevLett.52.2141 ↗
11. 10.1073/pnas.080100110510.1073/pnas.0801001105 ↗
12. 10.1021/la991206k10.1021/la991206k ↗
13. 10.1021/jp983052810.1021/jp9830528 ↗
14. 10.1021/jp993229d10.1021/jp993229d ↗
15. 10.1021/jp062140m10.1021/jp062140m ↗
16. 10.1021/ja906087a10.1021/ja906087a ↗
17. 10.1021/jz200310p10.1021/jz200310p ↗
18. 10.1021/ja110507810.1021/ja1105078 ↗
19. 10.1021/ja202810210.1021/ja2028102 ↗
20. 10.1021/ja402680c10.1021/ja402680c ↗
21. 10.1021/ja505429f10.1021/ja505429f ↗
22. 10.1021/jacs.5b0946610.1021/jacs.5b09466 ↗
23. 10.1038/s41467-017-00970-110.1038/s41467-017-00970-1 ↗
24. 10.1021/acs.jpcc.8b0936010.1021/acs.jpcc.8b09360 ↗
25. 10.1021/jz100944k10.1021/jz100944k ↗
26. 10.1246/bcsj.2011022710.1246/bcsj.20110227 ↗
27. 10.1038/s41467-018-04837-x10.1038/s41467-018-04837-x ↗
28. 10.1002/smll.20060061110.1002/smll.200600611 ↗
29. 10.1021/la900596710.1021/la9005967 ↗
30. 10.1021/jp101764q10.1021/jp101764q ↗
31. 10.1021/ac300536j10.1021/ac300536j ↗
32. 10.1039/c3nr33705g10.1039/c3nr33705g ↗
33. 10.1039/c3nr00677h10.1039/c3nr00677h ↗
34. 10.1007/s12274-012-0218-110.1007/s12274-012-0218-1 ↗
35. 10.1021/jp508124b10.1021/jp508124b ↗
36. 10.1021/acs.jpca.5b0962310.1021/acs.jpca.5b09623 ↗
37. 10.1021/acs.jpcc.6b1043410.1021/acs.jpcc.6b10434 ↗
38. 10.1021/acs.jpcc.5b0573510.1021/acs.jpcc.5b05735 ↗
39. 10.1021/jz300743610.1021/jz3007436 ↗
40. 10.1021/ac303367810.1021/ac3033678 ↗
41. 10.1038/am.2013.310.1038/am.2013.3 ↗
42. 10.1016/j.nantod.2015.04.00210.1016/j.nantod.2015.04.002 ↗
43. 10.1016/j.ccr.2017.11.01910.1016/j.ccr.2017.11.019 ↗
44. 10.1021/acs.jpcc.6b1242710.1021/acs.jpcc.6b12427 ↗
45. 10.1021/jp512237b10.1021/jp512237b ↗
46. 10.1021/cm049233610.1021/cm0492336 ↗
47. 10.1039/c0nr00541j10.1039/c0nr00541j ↗
48. 10.1021/jacs.8b0678310.1021/jacs.8b06783 ↗
49. 10.1021/ja01097a05710.1021/ja01097a057 ↗
50. 10.1039/c2cc00056c10.1039/c2cc00056c ↗
51. 10.1021/acs.nanolett.5b0112210.1021/acs.nanolett.5b01122 ↗
52. 10.1021/jacs.5b0542210.1021/jacs.5b05422 ↗
53. 10.1126/sciadv.160160910.1126/sciadv.1601609 ↗
54. M. S.Bootharaju C. P.Joshi M. J.Alhilaly O. M.Bakr2016 28 3292.10.1021/acs.chemmater.5b05008 ↗
55. 10.1021/jz900035p10.1021/jz900035p ↗
56. 10.1039/c3ra40775f10.1039/c3ra40775f ↗
57. 10.1021/co500072c10.1021/co500072c ↗
58. W.Hinsberg F.Houle Kinetiscope: A Stochastic Kinetics Simulator http://hinsberg.net/kinetiscope/index.html(accessed: September2018).