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Universality of dissipative self-assembly from quantum dots to human cells


An important goal of self-assembly research is to develop a general methodology applicable to almost any material, from the smallest to the largest scales, whereby qualitatively identical results are obtained independently of initial conditions, size, shape and function of the constituents. Here, we introduce a dissipative self-assembly methodology demonstrated on a diverse spectrum of materials, from simple, passive, identical quantum dots (a few hundred atoms) that experience extreme Brownian motion, to complex, active, non-identical human cells (~1017 atoms) with sophisticated internal dynamics. Autocatalytic growth curves of the self-assembled aggregates are shown to scale identically, and interface fluctuations of growing aggregates obey the universal Tracy–Widom law. Example applications for nanoscience and biotechnology are further provided.

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Fig. 1: Universal dissipative self-assembly methodology.
Fig. 2: Universal dynamics.
Fig. 3: Spatiotemporal control over the aggregates.
Fig. 4: Proof-of-principle demonstrations on living organisms.

Data availability

The data represented in Fig. 2, Supplementary Fig. 2 and Extended Data Figs. 3, 4, 5, 7, 8 and 10 are available as Source Data. Additional data may be requested from the corresponding author.

Code availability

MATLAB codes used to compute simulations of the Hammersley process and the length of the longest increasing subsequence for a given permutation, Tracy–Widom GUE simulations, image processing, and holographic algoritms are available from the authors on reasonable request.


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This work received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement 853387), TÜBİTAK under projects 115F110 and 117F352, and a L’Oréal–UNESCO FWIS award. F.Ö.I., G.M. and H.V.D. gratefully acknowledge funding from the ERC Consolidator Grant ERC-617521 NLL, TÜBİTAK under project 117E823, and NRF-NRF 1-2016-08 and TÜBA, respectively.

Author information




S.I. designed the research and wrote the paper. S.I., S.G., R.G., G.M., Ö.Y. and O.B. performed the experiments. G.M. carried out image processing. G.M., O.B. and F.Ö.I. performed statistical analysis. Ü.S.N. carried out the numerical simulations of fluid dynamics. G.Y. provided the MATLAB code for the mathematical models. E.D.E. prepared the microorganisms. Ö.A. and Ö.Ş. prepared the human cells. K.G., D.D. and H.V.D. prepared the quantum dots.

Corresponding author

Correspondence to Serim Ilday.

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The authors declare no competing interests.

Additional information

Peer review information Nature Physics thanks Gili Bisker and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Numerical simulation of the fluid flows.

Numerical simulation results show velocity field distribution of the fluid flows and corresponding streamlines (a) in the presence and (b) in the absence of a cavitation bubble. Red (dark blue) denotes the highest (lowest) flow speeds. The cavitation bubble is depicted by the white circle located at the centre of the computational area. The effect of the laser pulses is modelled as a boundary heat source at the lower right quarter of the bubble. A porous medium was introduced to model the aggregate positioned adjacent to the lower-right quarter of the bubble. In the absence of a bubble, we described the aggregate, also as a porous medium, located at the centre of the computational area (black circle). The beam profile of the laser is Gaussian, so we introduced a heat source with a Gaussian temperature profile to represent the effect of the laser. This source was located at the centre of the aggregate. The diameters of the porous medium and the laser beam were chosen to be 15 µm and 8 µm. The streamlines can be seen to penetrate the aggregate because it is modelled as a porous medium.

Extended Data Fig. 2 Microscopy images of aggregate collection and dissolution.

Microscopy images showing collection (laser on) and dissolution (laser off) of the aggregates of the particles and organisms. Red dot denotes position of the laser beam. Beam sizes are not drawn to scale; it is fixed to be ~8 µm in all experiments. A ×40 objective was used for imaging the CdTe quantum dots, polystyrene spheres, and S. cerevisiae yeast cells, whereas a ×100 objective was used for M. Luteus and E. Coli bacterial cells and ×10 for MCF10A human cells.

Extended Data Fig. 3 Growth curves at original time scales.

Graphs showing individual growth curves of particles and living organisms at their original time scales. Source data

Extended Data Fig. 4 Growth curves in the presence or absence of a cavitation bubble.

Comparison of the growth curves of aggregates growing at and in the absence of a cavitation bubble. Source data

Extended Data Fig. 5 Interface fluctuations of a virtual aggregate.

Illustration and microscopy image (inset) showing the control experiment. Arrows denote the direction of the fluid flow towards the open end of sample. Particles are assumed to be collected passing the virtual boundary. Interface fluctuations were calculated using average width (yn) and height (h(tframe, yn)) of the growing aggregates. Semi-log scale plot showing experimentally obtained probability distribution function of the interface fluctuations (green dots). A ×100 objective was used for the imaging. Source data

Extended Data Fig. 6 Control over the aggregate movement.

Time-lapse microscopy images showing an aggregate of 0.5-µm polystyrene colloids following the movement of laser beam to form a line (top), and a wave-like pattern (bottom). The bright white dot is the laser beam. The transmission of a small portion of the infrared beam is allowed from the visible lowpass filter (with relatively low attenuation to infrared) to show its exact position. Illustrated red dots show the movement of the beam. ×60 objective was used for the imaging. .

Extended Data Fig. 7 CdTe characterization.

a, Transmission electron microscopy image and (b) photoluminescence spectrum of the aqueous CdTe quantum dots. Source data

Extended Data Fig. 8 Filling ratios for different region of interests.

Filling ratio of different selected areas (region of interest: ROI) with E.coli and M. Luteus bacterial cells during their aggregation. Areas with different sizes (ROI 1, 2, and 3 of (a), (b), and (c)), shapes (a square, ROI 3, and a circle, ROI 4), and positions within the aggregate (c) do not affect the filling ratio. Source data

Extended Data Fig. 9 Detection of CdTe photoluminescence signal.

Time-lapse microscopy images (top) and plots (bottom) showing detection of photoluminescence signal emitted from growing CdTe quantum dot aggregate. A ×40 objective was used for the imaging.

Extended Data Fig. 10 Tracy–Widom GUE temporal span analysis.

The evolution of the scaled normalized moments of temporal fluctuations with that of Tracy–Widom GUE with respect to temporal span. Source data

Supplementary information

Supplementary Information

Supplementary text, methods, Figs. 1–3, Table 1 and references.

Reporting Summary

Supplementary Video 1

Aggregation of 0.5-µm polystyrene spheres adjacent to a cavitation bubble (left) or at the laser spot (right) in a quasi-2D setting through ultrafast laser-induced flows using a ×60 objective.

Supplementary Video 2

Collection (laser on) and dissolution (laser off) of the aggregates of particles and organisms. Red dot denotes the position of laser beam. Beam sizes are not drawn to scale, it is fixed to be ~8 µm in all experiments. A ×40 objective was used for the imaging of CdTe quantum dots, polystyrene spheres, and S. cerevisiae bacterial cells, whereas a ×100 objective was used for M. Luteus and E. Coli bacterial cells and ×10 for MCF10A human cells.

Supplementary Video 3

Three different experiments showing circularly growing aggregates (first column), calculated standard deviation maps (second column), and detected interfaces (third column) of growing aggregates using a ×100 objective.

Supplementary Video 4

Unidirectional fluid flow due to the pressure gradient, which drags particles towards the open end of the sample (right-hand-side) in the absence of a laser light using a ×100 objective.

Supplementary Video 5

Spatiotemporal control over the aggregates of polystyrene spheres using a ×60 objective. The bright white dot is the laser beam. The transmission of a small portion of the infrared beam is allowed from the visible low-pass filter (with relatively low attenuation to infrared) to show its exact position.

Supplementary Video 6

Aggregates of 0.5-µm polystyrene spheres can be patterned to write words and to give arbitrary geometrical shapes following the beam patterns (left) using a ×40 objective.

Supplementary Video 7

Spatiotemporal control over the aggregates of ~3 nm large quantum dots and ~5 µm large S. cerevisiae yeast cells using a ×40 objective.

Supplementary Video 8

Separation of M. luteus (gram positive) from E. coli (gram-negative) bacterial cells from an initially homogeneous mixture using a ×100 objective.

Supplementary Video 9

Formation of vertex flows that stirs S. cerevisiae yeast cells when the laser beam is moved using a ×40 objective.

Supplementary Video 10

Detection of E. coli and M. luteus bacterial cells using a ×100 objective in a selected rectangular area to calculate the filling ratio.

Source data

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Makey, G., Galioglu, S., Ghaffari, R. et al. Universality of dissipative self-assembly from quantum dots to human cells. Nat. Phys. 16, 795–801 (2020).

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