Peer review of: A nanomaterials discovery robot for the Darwinian evolution of shape programmable gold nanop (2019)

Reviewers' comments: 

Reviewer #1 (Remarks to the Author): 

The authors report an autonomous platform to explore the chemical space of gold nanoparticles. They 
built a robotic system to prepare and analyse optically the performance of the particle synthesis, which 
is suggested by a genetic algorithm. The whole workflow is controlled by an in-house developed 
software written in python/C++. 

Overall the manuscript is enjoyable to read and presents an innovative approach to the discovery 
process. I would recommend publication after addressing the following suggestions and comments: 

1. The size of Space 2 and 3 is unclear to me. From Figure 1, it looks like there are four parameters to 
play with. However, later in the text - “after gentle mixing, ascorbic acid (70µl, 0.0788 M) was added” - 
and based on Table 2, it looks like Space 2 and 3 have three free parameters to play with. Could you 
please confirm and clarify? 

2. There is a total of 100 (10x10) experiments in Space 1, 150 (10x15) experiments in Space 2, and 150 
(10x15) experiments in Space 3. That sounds like a lot of experiment in order to have a good fitness, i.e., 
minimise the loss function. In your opinion, how would that compare with other approaches to 
designing experiment? Does that include the initial set of randomly generated parameters? Could you 
comment and explain why Space 1 is explored in batches on 10, while Space 2 & 3 are explored in 
batches of 15? 

3. In the absorbance spectra of Space 2, how did you decide on the threshold factor of 0.75 between the 
median peak and the target peak to assign a non-zero fitness value? 

4. For Space 3, how do you make sure that all 150 experiments yield arrow-headed nanorods and not 
nanosphere? The absorption peak of the latter (572nm) seems very close to the one of the nanospheres 
at 573.61nm, and to me, spectrum S7/S8/S10/S11/S13/S14 (nanosphere) are very similar to S25 
(expanded search). 

5. Minor comment: introduce the acronym AuNPs before using it (bottom of p.2) 

6. Minor comment: In Table 1, the “Difference (mL)” for “CTAB” should read 0.56 



Reviewer #2 (Remarks to the Author): 

The authors describe a self-optimizing reactor for the production of gold nanoparticles of varying shape. 
The paper describes an interesting application of self-optimizing reactors, but is weakened by a 
significant lack of experimental/procedural detail as set out below. 

There is a sizable body of papers on self-optimizing reactors, and it is surprising that none of these has 
been cited in the manuscript. Key papers by the groups of Bourne, de Mello, Jensen, Poliakoff and 
Rueping for instance are missing. The use of genetic algorithms (GAs) distinguishes the current 
manuscript from previous papers, but the justification for their use is not well made. The authors 
comment on the suitability of GAs "for searching through large and complex data sets... when a large 
number of variables must be explored", but neither of these considerations applies to the reported 
work, which involves just "10 generations with 10 experiments per generation". There are many possible 
algorithms that could be sensibly employed for an optimization of the size described. If there is a 
genuine advantage to using a genetic algorithm for the current application, this should be described in 
detail with comparative experimental studies against other optimization procedures. 

In general the paper lacks sufficient experimental detail for the reader to understand exactly what has 
been done or reproduce the work. For instance, it is unclear what specific reaction parameters were 
used to control the optimization. What were the precise compositions of the stock solutions used? How 
were the automated syntheses carried out? Was it purely the volumes of the stock solutions that were 
altered or were the concentrations also changed? It is very difficult to infer the experimental procedure 
from the limited information given. 

The specific algorithm used for the optimization was not stated. The only description I could find was a 
statement in the supporting information, saying “The algorithm used within this system was a standard 
optimization genetic algorithm – a form of evolutionary algorithm”, which is an inadequate description. 
The exact algorithm used should be stated, together with all relevant optimization parameters. The 
paper is unclear on the number of experiments carried out for each generation. In some places, it says 
15 and in others it says 10. 

The authors state “The final UV-vis spectra shown in Figure 4d shows an upward trend in fitness factor 
over these 10 generations, and this indicates the robot platform had produced results all but identical to 
those seen on the bench using literature values.” I assume the authors mean 4e, here? If so, then it is 
inaccurate to describe the results as “all but identical”. The peak positions and ratio of peak heights 
differ substantially for the two spectra. 

The authors conclude by saying “Our system has demonstrated for the first time, seed mediated 
nanoparticle synthesis assisted by an evolutionary algorithm in a controlled and reproducible manner” 
but no data is provided on the reproducibility of the system to support this statement. 

The labeling of the raw spectra in the SI is uninformative. For instance, the description “Seed 128, 
Generation 0000, Experiment 0003” provides little information of value. What specific reaction 
conditions were used to obtain each spectrum? 

Overall, the manuscript lacks the experimental detail required to support the claims made. Therefore I 
do not recommend the paper for publication in Nature Communications, and suggest it is submitted 
elsewhere once the above issues have been addressed. 




Reviewer #3 (Remarks to the Author): 

In this manuscript, the authors demonstrate a semi-batch liquid handling robotic platform for optimize 
the synthetic conditions of Au nanomaterials of different morphologies. Au spherical nanoparticles, 
rods, and anisotropic arrow-faceted nanoparticles were chosen as the demonstrated targets of the 
robot. While the Au nanoparticles were used as the seeds for the preparation of Au nanorods, the rods 
were used as the seeds to synthesis the arrow-faceted nanoparticles. UV-Vis spectra were measured 
and used as feedback signals to evaluate how the products’ shapes were close to the desired target. 
Overall, the work should be interesting for publication in Nat. Commun. I would like to recommend its 
publication after the following issues are addressed: 
1. More TEM images should be provided to show how the quality of three different nanomaterials was 
getting improved with increased generation. As for the Au nanorods and arrows, the TEM images should 
be of lower magnifications to display large-area samples instead of just several particles in the images. 
2. As for the sample of Au nanorods, both the UV-Vis (absorption at ~510nm) and TEM data indicated 
that there should be a large amount of spherical nanoparticles. So the authors need to make comments 
on this. The information on the ratio of different-shaped particles should be given. 
3. The robot optimized the protocols for the three different-shaped nanomaterials whose syntheses 
have been already optimized by batch reactions. I would encourage the authors to optimize conditions 
for some structures whose syntheses have not been optimized. Typically, in every batch of synthesizing 
anisotropic nanoparticles, there are always some minor products with weird morphologies. It would be 
very interesting to see the robot can be used for the optimization of those minor products.