Peer review of: Imaging spatiotemporal evolution of molecules and active sites in zeolite catalyst during methano (2020)

Reviewers' comments: 

Reviewer #1 (Remarks to the Author): 

The manuscript by Ye and Liu et al presents an alleged deep data approach to spatiotemporal 
understanding of key species in MTO chemistry using molecular simulations and (among a plethora of 
techniques) SIM microscopy. The main focus is on understanding differences between different sized 
SAPO particles. The topic and the aim is timely, and indeed, some mysteries are really worth exploring 
in MTO and its deactivation. The data is of high quality and seemingly all techniques (MD, 
characterization) are performed in a correct manner. The paper is well written and with a quite a good 
intro for the non-expert reader. It could be shortened a little, but length on average is justified. 

There are however some major concerns (1, 2, 3): 

1. I do question the main claim though, i.e. the authors claim a synergy of multiscale reaction 
diffusion simulation and microscopy (SIM) to give insight into the spatiotemporal evolution of key MTO 
species, and say this is a deep data approach. 
The paper presents the word Deep data a lot on page 4, and in Fig 1, this is also somewhat visualized. 
However, no details on the deep data approach are found. Fig 1 shows some buzz words such as ‘deep 
mechanisms…” and ‘deep development’ but all of this remains very vague. Later in the paper, they 
state: ‘SIM images are used to verify the multi scale reaction diffusion simulation’. Is this the deep 
data? What is really deep? Is it just a comparison of the visual models with the SIM and then adjust 
some parameters? Is it with machine learning based iteration or prediction? … This is not clear. 

If I follow the thought that data of physical characterization is fed into the models, I have some 
questions (comment 2) 
2. In Fig S2, clearly samples B and C have no real homogeneous particles sizes for the crystals. Also 
for the big 50µm one in D, only one crystal is shown. How is the size distribution of these crystals 
(synthesis) and how does it affect the authors’ work where bulk physical characterization data (of a 
range of crystals with avg size e.g. + or – 10 µm) is used as input in multi scale spatiotemporal 
evolution models of acidity, species and diffusion, for one crystal (e.g. in the presented visualizations 
(e.g. Fig 3, 4, 6b and 6d). 

Similar questions for the DRIFTS, this is measured on bulk spent catalyst, but then used in single 
crystal computation (or not? This is not clear). Also, the figures give kg/m3 for acid sites: how come 
this is so high? (e.g. a zeolite with really a lot of acid sites (Si), has 1 mmol /g. Imagine this is a 
proton, weighing 1 g/mol, we get 1 mg/g zeolite. Then assuming a zeolite density of 1 g/mL (not 
correct but OK), we get 1 mg/mL zeolite. If we translate to m3, we get 1000 g/m3. But the scale bars 
in Fig. 3e for instance give around 150 kg/m3 at TOS 3; (so 150 times more than what a really acidic 
zeolite would have?). The authors should this rationalize their numbers a bit more and check them. 

So, in general, how can one transfer bulk obtained information on materials to quantitative input for 
MD or other computational models centering on one crystal? 
The link between All of this relates to the warning given for the deep data approach as explained in 
the benchmark outlook in Nature Materials (ref 23 in this work), that says “Despite simplicity, such an 
approach requires extreme care and expertise in interpreting the experimental data, to correct for 
instrumental artefacts (for example, calibration, giving systematic errors in measured structural 
parameters), partial character of information (for example, missing atoms of light elements poorly 
visible in electron microscopy), and systematic errors in theoretical models. Practically, this requires 
close interactions between theorists and experimentalists who are well aware of the strengths and 
limitations of their respective methods.” 

Can the authors briefly comment on how they made sure that they took ‘extreme care’ in 
experimental data given the partial character of information or systematic errors in theoretical models. 
These things should be addressed and the deep-data approach should be explained better (if it is 
really justified to claim it as such), i.e. more detailed and yet more understandable for the average 
reader. So, in general, how can one transfer bulk obtained information on materials to quantitative 
input for MD or other computational models centering on one crystal? 

3. How does this open an interesting method for materials synthesis as claimed in the introduction? To 
claim this, and to show real promise of their approach (deep data or not), I suggest the authors to 
make one more SAPO-34 with a critical property change from synthesis (e.g. islanding Si, Si/Al+P 
variation, zoning, or something else) and test whether the deep data approach presented can indeed 
quickly offer insights that correlate with the catalytic data. 

Minor comment 4: Along a similar line as comment 2, in terms of statistical analysis, little is found, e.g. 
were standard errors taken into consideration when measuring physical properties and is this error 
margin translated when the inputs were used for the MD models? Also some replications of key 
measurements used as input are needed with error estimation. 
Minor comment 5: (Suppl table 1, two numbers after the dot for surface areas are not meaningful), 
Suppl fig 5, error bars on conversion? Same for Fig. 5 a and b in main manuscript. 
Minor comment 6: fig 6 is in part discussed before fig 5 and this is confusing. 

Finally, I wish to complement the authors on an impressive piece of work that is certainly valuable and 
with good insights (e.g. Fig 6, even if still a bit hypothetical, is very informative!). Given 
substantiation by the authors and demonstrating what exactly is the synergy (which data) and how 
the deep data approach is really used, this paper is of very high quality and very suited for Nature 
Communications, and a nice addition to the MTO catalysis field. 



Reviewer #2 (Remarks to the Author): 

Manuscript brings rather new insight into the properties and behaviour of SAPO-34 catalyst in the MTO 
process. It is an ambitious combination of simulations and space-resolved spectroscopy that together 
offers an interpretation of differences in catalytic activity of crystals with different size (ranging form 5 
to 50 μm). Therefore, I believe it is appropriate for publication in Nature Communication. However, 
before it is published it should clarify and improve few details described below: 
Authors talk about “deep data”, however, I haven’t found what exactly is meant by deep data. There 
are details about adsorption, diffusion and reactivity simulations, there are details about spectroscopy 
but I am missing the details how those two are put together. Please clarify. 
Please provide more details about how kinetic constants summarized in SI Table 2 where obtained. 
Are they fitted to provide agreement with experimental data reported in SI Fig. 5? 
I have an objection on how the Figures are presented (2-4 and 6): concentration profile is shown as 
2D plot showing the central plane of the whole crystal. However, profiles are symmetrical (and that is 
the natural consequence of the model underneath) and the same information can be presented as a 
simple function y(x) where x is the distance from the edge to the centre of the crystal. Such figures do 
not need colour scheme and they (i) will bring clearer pictures, (ii) can combine more information in 
one figure and, thus, (iii) significantly reduce the space required without compromising clarity. 
Figure 5, on the other hand, is too small and it is difficult to read, please organize it differently and 
make sure that reader can read axis and legends. 
The level of English is visibly lower in SI compared to main text. 

Supplementary Figures 15 – I do not see a need for 2D plots when the values are constant along x 
and colour scheme is needed (and extremely small values with unnecessary number of decimal points 
are used). Please change it to simple plot where y axis shows the value. 
SI Figures 16-21 – same as above for Figures 2-4 and 6. 



Reviewer #3 (Remarks to the Author): 

The work by Gao et al. entitled Imaging spatiotemporal evolution of molecules and active sites in 
zeolite catalyst during methanol-to-olefins reaction contains the results from a fluorescence (SIM) 
microscopy study and modelling of diffusion behaviour in an attempt to understand how product and 
reactant diffusion affects the acid site availability and deactivation of catalytically relevant small pore 
zeolites for hydrocarbon conversion. The key premise in this work is that with techniques such as SIM 
it is now possible to study zeolite particles that are more relevant in size (industrially) when compared 
to what has been performed in the past and to subsequently be able to identify the species and their 
location that lead to ‘real’ deactivation. The results have been contextualised with more conventional 
lab-based characterisation methods and catalytic (time on stream testing). Indeed this sort of 
approach is likely to be of real interest to catalyst and even materials science researchers as they seek 
to obtain a better understanding of spatial-temporal effects in functional materials. 

I am not really able to comment authoritatively on the diffusion modelling approach used but I do feel 
that there are two issues with the interpretation of the SIM data which need clarification. Firstly it is 
not clear to me how the illumination and detection strategy used here can really differentiate between 
the proposed chemical species. Whilst the premise is logical, i.e. that illumination with particular 
incident light will mainly excite molecules with the equivalent band gap (i.e. the authors say that 
methylbenzenes can be probed using an excitation wavelength of 405 nm), in effect there are many 
possible energy transfer mechanisms that means that molecules, particularly those that can be excited 
at a longer wavelength (> 405 nm), can also give a signal. Furthermore the images are constructed 
from a ‘fluorescence’ signal, but it has recently been shown that hydrocarbon species present in 
zeolites exhibit both fluorescence and phosphorescence (see 
https://doi.org/10.1021/acs.jpcc.9b09050). So I am wondering whether the authors can really 
differentiate/assign the chemical species based on their response to a (tunable) incident wavelength? 
Looking at the SIM figures, the distribution of species looks remarkably similar for the 488, 561 and 
640 nm which suggests that actually it is not easy to differentiate between species as has been 
proposed. 

A second issue concerns the lack of correlation with the UV-Vis data. All of the UV-Vis data for the 
crystals towards the end of the respective TOS studies contain very little absorbance at ~ 640 nm, yet 
according to the SIM fluorescence signal, this component represents the most prevalent species. 
Either then the imaging studies are not representative or else (again) there is an issue with the link 
with speciation. Or is there a problem with (fast) quenching of some species excited at shorter 
wavelengths? Notwithstanding that the extinction coefficient (even identity!) of the species present 
are not really known it is not a given that the intensity of an absorption band (ergo, an emission band) 
is an indication of the number of species present. As such it is not so easy to correlate the presence of 
particular species with deactivation phenomena. To be fair to the authors however, the simulations 
and citations of past work has been used intelligently to mitigate these risks but it is difficult to draw 
meaningful detailed conclusions from a rather straightforward analysis of fluorescence microscopy 
data. Also, if these experiments were not performed in a reactive atmosphere, it is known that the 
signals can become quenched in air – for example, emissivity begins to resemble that of graphene 
oxide in place of graphene. 

Additional comments include; the EDX mapping of the crystals only indicates the elemental 
distribution at the crystal surface but not below the surface. As such this mapping is not proof of an 
uneven distribution of Si:Al. 
Minor comments: Figure 1 is too general for the paper. For example, the authors discuss/highlight the 
possibilities of using XRD-CT (amongst other methods) but this isn’t a review article so it doesn’t need 
to be mentioned. Better to focus on what is being presented in the article only.