Figure adapted from , illustrating how optimization is helped by understanding how to best move in high dimensional parameter spaces.
The results of the work include super efficient novel nanostructures for applications that are vital for future energy conversion and catalysis, but these results were expected and predicted with confidence on grounds of considerations about fundamental aspects such as the dimensions of parameter spaces and the nature of complexity as such, including such issues as the efficiency of fractal structures for complex tasks. Catalysts are understood in a most fundamental way, system theoretically, unifying the chemical concept with what we understand a “catalyst” to be in the social context:
“A catalyst is an agent that (or who) accomplishes a certain task, thereby not only often speeding up a reaction but also enabling it. The catalyst, just like in the social realm, does something; it does take part in the reaction although by definition, a catalyst reemerges unchanged after having undergone the crucial processes.”Further considerations are indicated by the following quotes:
“Bimetallic catalysts multiply the catalytic rate constants of mono metallic compounds. Tri-metallic catalysts often multiply those performance parameters yet again, and often even before re-optimization(!), although the introduction of the new ingredient destroyed the previous optimization. A general approach applicable to all catalysts, be they chemical or social, demystifies the “magic” and gives input to practical research questions
The previous 2D subspace may be some random cut through the 3D space. The probability that the previous 2D subspace included the location of the 3D peak is basically zero!
Merely using an alloy combining ten different metals will not work. “The more mixed the better” (for “complexity” or social “diversity”) is certainly wrong.
Especially in catalysis and before optimization, we aim for broad distributions in crystal sizes and imperfect shapes, rather than for the usually advertised narrow control over sizes and aesthetically appealing microscopy images. A ‘smearing’ of complexity over different scales (fractality) is generally justified.
Complexity’s “magic” may well be necessarily related to that any true increase in complexity escapes previous ways of quantification. This brings us to the issue of emergent parameters. ...”
A nanotech paper can of course not be published saying that it was based on a lot of philosophy. The only hint is the mentioning of the dimension of the parameter space:
“This confirms that simply optimizing in a higher dimensional parameter space is extremely likely to lead to better values, especially if departing almost orthogonal to the already performed optimization … This allows a much better optimization. Our optimization here was only performed partially and only in a two dimensional parameter space (x and y/z), but the results already confirm this general understanding.”
Given the large role that authority plays in academia, a proper attribution can seldom be had. The whole idea of the work, including its detailed calculations and the predicted outcome, came from previously published philosophical considerations. This includes even the particular dimensions of the parameter space. For example, the chemical Pd was selected in order to supply a certain task (hydrogen transfer). The replacement reaction influences porosity – therefore the odd treatment of the parameters, for example y/z rather than the atomic fractions y or z directly. Since porosity as well as optimal diversity of tri-metalic interfaces are all important, the work focused on sub-monolayer coverings and found the optimum there straight away:
“The number of tri-metallic interfaces should be maximized near a 50% surface coverage σ ≅ 3/4 × (R/r) × s, where R is the network ligament radius and s the ratio of the number of added atoms divided by the number of initial Ag atoms. The concentrations were therefore chosen so that the resulting Agx Auy Pdz compound will have x ~ 0.9.” 
Now, if you make a careful search in the literature and look for where these concepts popped up before, you may find that the author S.V. seems to play some role. Therefore, the Author Contributions given in the new paper seem once again a little strange:
“ … S.V. contributed in the theoretical calculations for near optimization of catalytic activity in 2D parameter space. S.C.T. and X.K.M. conceived and designed the experiments, and directed the whole study. S.C.T. and S.V. revised the paper together. …”
So, the only foreigner involved (S.V.) did no more than a few calculations and then revised the paper. He is a clown we could do without if we did not have to write English. Here is the corrected Author Contributions:
“ … S.V. conceived and designed the experiments, even slight details such as Ag precursor particle sizes and into which direction to vary the parameters inside the by him suggested parameter space. He originated the whole study and contributed all vital general theory and detailed calculations for pinpoint efficient optimization of tri-metalic interface accessibility as it is coupled to inter-dependent porosity and metal ratios. He wrote large crucial parts of the paper that explain the whole. Sadly, many insights are no longer properly connected with one another in the final version, because rather than having ‘revised the paper together,’ S.V. was not involved in the revision. S.V. had to fight hard, threatening retraction of papers, so we could not once again put him as third or fourth author behind his back. …”
As you likely guessed, S.V. no longer collaborates with this group, because life is just too short to put up with demeaning nonsense for years without any improvement. Soon he will take his guitar and open a cat cafe perhaps.
 T.Y. Li, S. Vongehr, et al.: Scalable Synthesis of Ag Networks with Optimized Sub-monolayer AuPd Nanoparticle Covering for Highly Enhanced SERS Detection and Catalysis Scientific Reports 6, 37092 (2016) doi: 10.1038/srep37092 www.nature.com/articles/srep37092
 S. Vongehr, S.C. Tang, X.K. Meng: Adapting Nanotech Research as Nano-Micro Hybrids Approach Biological Complexity. J. Mater. Sci.&Tech. 32(5), 387-401 (2016) Invited Review doi: 10.1016/j.jmst.2016.01.003 www.jmst.org/EN/abstract/abstract24512.shtml
[This series of articles has details about this work.]
 S. Vongehr: “Fundamental Science for Applied and Social Science Students.” Lecture Notes, Nanjing University (2016)