# Probing temperature-responsivity of microgels and its interplay with a solid
surface by superresolution microscopy and numerical simulations

Xhorxhina Shaulli  Department of Physics, University of Fribourg, Chemin du
Musée 3, 1700, Fribourg, Switzerland  Rodrigo Rivas-Barbosa  Department of
Physics, Sapienza University of Rome, Piazzale Aldo Moro 2, 00185 Roma, Italy
Maxime Jolisse Bergman  Department of Physics, University of Fribourg, Chemin
du Musée 3, 1700, Fribourg, Switzerland  Chi Zhang  Department of Physics,
University of Fribourg, Chemin du Musée 3, 1700, Fribourg, Switzerland
Nicoletta Gnan  CNR Institute of Complex Systems, Uos Sapienza, Piazzale Aldo
Moro 2, 00185, Roma, Italy  Frank Scheffold<EMAIL_ADDRESS>Department of Physics, University of Fribourg, Chemin du Musée 3, 1700,
Fribourg, Switzerland  Emanuela Zaccarelli<EMAIL_ADDRESS>CNR
Institute of Complex Systems, Uos Sapienza, Piazzale Aldo Moro 2, 00185, Roma,
Italy

###### Abstract

Superresolution microscopy has become a powerful tool to investigate the
internal structure of complex colloidal and polymeric systems, such as
microgels, at the nanometer scale. The ability to monitor microgels response
to temperature changes in situ opens new and exciting opportunities to design
and precisely control their behaviour for various applications. When
performing advanced microscopy experiments, interactions between the particle
and the environment can be important. Often microgels are deposited on a
substrate since they have to remain still for several minutes during the
experiment. This study uses dSTORM microscopy and advanced coarse-grained
molecular dynamics simulations to investigate, for the first time, how
individual microgels anchored on hydrophilic and hydrophobic surfaces undergo
their volume phase transition in temperature. We find that, in the presence of
a hydrophilic substrate, the structure of the microgel is unperturbed and the
resulting density profiles quantitatively agree with simulations performed in
bulk conditions. Instead, when a hydrophobic surface is used, the microgel
spreads at the interface and an interesting competition between the two
hydrophobic strengths — monomer-monomer vs monomer-surface — comes into play
at high temperatures. The remarkable agreement between experiments and
simulations makes the present study a fundamental step to establish this high-
resolution monitoring technique as a platform for investigating more complex
systems, being these either macromolecules with peculiar internal structure or
nanocomplexes where molecules of interest can be encapsulated in the microgel
network and controllably released with temperature.

###### keywords:

American Chemical Society, LaTeX

Equal first author Equal first author Department of Physics, Sapienza
University of Rome, Piazzale Aldo Moro 2, 00185 Roma, Italy Equal senior
author Department of Physics, Sapienza University of Rome, Piazzale Aldo Moro
2, 00185 Roma, Italy Equal senior author IR,NMR,UV

##  1 Introduction

One of the fascinating aspects of colloidal science is the possibility of
investigating mesoscopic particles with experimental techniques that are able
to resolve their structure and dynamics at the single particle level. Thanks
to real-space particle tracking, colloidal particles with size ranging from
hundreds of nanometers up to a few microns have long been used as suitable
benchmarks to test theories and numerical models. This approach has been
successful for a long time, bringing important experimental contributions to
fundamental problems as, for example, the nucleation and growth of colloidal
crystals 1, the structural relaxation dynamics close to the glass transition
2, 3 or the 2D melting scenario of hard colloids 4. Recently, however, the
rapidly-growing field of “smart” materials, i.e. materials designed to respond
to external stimuli, has moved the community’s attention from standard “hard-
sphere” colloids to softer particles, mainly of polymeric nature and with a
complex internal architecture. Within this category microgels are among the
most studied colloidal systems. Microgels are cross-linked polymer networks
whose properties are intimately related to the type of polymer they are made
of and to the topology of the network. Their complex internal architecture
provides particles with an internal elasticity that allows them to shrink,
deform and interpenetrate to some extent. Even more intriguing is their
ability to respond to external stimuli: depending on the type of polymer
employed in the synthesis, microgels can adjust their size in response to
temperature, light, or pH changes, to name a few 5, 6, 7. A well-known example
is that of poly- (N-isopropylacrylamide) (pNIPAM) based microgels that exhibit
conformational changes in response to solvent composition or temperature.
Specifically, they undergo a reversible volume phase transition (VPT) at a
temperature of about 32 °C that leads to network shrinking and, consequently,
to a change in the size of the particle 8, 9, 10, 11. Additionally, it is
worth stressing that synthesis advances allow tuning of the microgels size and
architecture, as well as decoration and functionalization, all of which in the
end influence the responsiveness to external cues 12, 9 and the mutual
effective interactions between the particles 13.
All these features provide microgels a richer behavior compared to standard
“hard-sphere” colloids: in particular, the possibility to tune their
properties in a controllable way makes them suitable as building blocks for
designing materials that can be used for several purposes, from understanding
fundamental problems in physics 9, 14, 15, 16, 17 to industrial applications
as, for example, viscosity modifiers, tunable optical scattering components or
carrier agents 18, 19, 20. In this context, understanding how the internal
complexity of microgels at the nanoscale translates into specific macroscopic
material properties is one of the main goals of soft matter physics. This
requires the use of novel experimental techniques that go beyond standard
optical microscopy and push the resolution below the microscale.
To this aim, the recent scientific and technological breakthrough in super-
resolved fluorescence microscopy (SRFM) 21, 22, 23 has had a direct impact on
many research areas in biology and materials science. The advanced techniques
were soon embraced as valuable characterization tools with unprecedented
accuracy and specificity using multi-color fluorescent labeling protocols 24,
25, 26. Compared to biological studies, applications of SRFM progressed slower
in investigating colloidal and polymer systems. More recently, however, it has
been shown to be a unique and valuable imaging and characterization method 27,
28, 29, 30, 31, 32. In contrast to other characterization methods, such as
X-ray scattering or atomic force microscopy (AFM), SRFM provides single-
particle information on the nanoscale and chemical specificity at the same
time.
In the past years, SRFM combined with other methods, like computer simulations
and light scattering techniques, have been used to tackle many unanswered
questions regarding the microgel network and its properties. In particular,
Conley et al. demonstrated the successful application of direct Stochastic
Optical Reconstruction Microscopy (dSTORM) to investigate stimuli-responsive
pNIPAM microgels 28 at different solvent compositions. The same technique was
then used in pure water by the same authors to probe the behavior of
concentrated microgels suspensions in swollen conditions 26. In addition,
Bergmann et al. studied microgels with different cross-linking densities using
dSTORM via a non-specific labeling approach 33, 34. Different kinds of
microgels were studied: first , Wöll and co-workers investigated core-shell
microgel particles combining in situ electron and super-resolution microscopy
to unravel new levels of structural details of their particles 30, then they
studied pNIPMAM microgels on solid-liquid interfaces below the VPT showing the
dependency of the spread of the microgel with the surface degree of
hydrophobicity 35 and with the procedure in which the microgel is placed on it
36. None of these previous super-resolution studies have tackled the problem
of probing the thermoresponsivity of the microgels directly by changing
temperature, in order to directly visualize the occurrence of the VPT in the
internal structure of the microgels. The only notable exception is the
application of the PAINT technique to core-shell pNIPAM-pNIPMAM microgels 37,
which however only gained information on the radial distribution of the
polarity within the particles, rather than the true polymer density profile.
Here we fill this gap by providing a dSTORM investigation of the volume phase
transition of pNIPAM microgels, validating the experimental approach through
the comparison with state-of-the-art computer simulations. Indeed, the
application of dSTORM technique currently requires the anchoring of the
microgels at a nearby surface, that, in principle, could affect the results.
The present work shows that the use of a hydrophilic surface allows us to
probe the VPT without detectable external perturbation of the microgel
structure, both in the swollen and in the collapsed state. This is achieved by
a careful comparison with numerical simulations both in the presence and in
the absence of the nearby surface. Next, we actually exploit the presence of
the surface to also characterize the VPT of a microgel close to a hydrophobic
wall, thus revealing a subtle interplay between surface adhesion energy and
hydrophobic interactions within the microgel, whose microscopic mechanism is
unveiled by the numerical simulations. Our work thus represents a crucial step
in the nanoscopic characterization of thermoresponsive soft particles, paving
the way for its use in a variety of different systems.

##  2 Results

###  2.1 dSTORM imaging of the Volume Phase Transition of microgels

We investigate pNIPAM microgels with $\sim$1.5 mol% BIS using dSTORM across
the volume phase transition (VPT). Particles are fluorescently labeled in the
outer region to facilitate discrimination of different parts of the microgel,
namely the core region and the shell including the dangling ends, i.e. the
microgel corona. Details on the labeling protocol can be found in the Methods
Section. In the following, we denote as shell the part of the microgel network
that is dye-labeled, even if the transition from the dense core to the loosely
crosslinked corona is rather gradual and there is no sharp border 10, 32.
To perform dSTORM experiments, microgels must be anchored to a surface, upon
which they are deposited irreversibly by drying and re-suspending them in
water 28. In order to appropriately characterize the well-studied phenomenon
of the VPT in pNIPAM microgels, we must thus ensure that this anchoring
surface does not appreciably interfere with the microgel swelling and
deswelling. To this aim, we use a hydrophilic surface, where the coverslip is
treated with KOH 3M, sonicated for 10 min, and then exposed for 10 min on a
UV-Ozone cleaner, obtaining surfaces with contact angle less than 20°, as
shown in Fig. S1 of the Supplementary Information (SI). For the imaging
process we induce stochastic blinking of the fluorophores as described in the
Methods Section. For each dSTORM image, we acquire 30000 - 60000 frames with
an exposure time of 10 - 20 ms and proceed with image analysis using the open-
source Picasso software 38, as detailed in SI, following Conley and coworkers
28, 39. Under these conditions we obtain an experimental resolution of around
30 nm in the plane (Figs. S2-S5). In Fig. 1 we show the typical experimental
workflow for all microgel particles. We extract quantitative information from
dSTORM using a customised MATLAB (MathWorks, USA) routine to determine the 2D
fluorophore density profiles $\rho^{2D}(r)$ of the microgels as previously
explained in Ref. 28. In order to describe the radially decaying density of
the microgel under swollen conditions, the classic fuzzy sphere model is
used6, adapted to the case where microgels solely contain fluorophores in
their outer shell, with their denser core being ‘invisible’ in dSTORM
experiments, as discussed in the SI (Fig. S6). The fuzzy-sphere model assumes
that a microgel is made of a dense inner core of size $R$ and a fuzzy shell of
thickness $2\sigma_{surf}$. Both parameters can be obtained from a fit of the
2D density profiles taken from dSTORM thus providing an estimate of the radius
of the particle, $R_{tot}=R+2\sigma_{surf}$, projected on a plane. An example
of the corresponding fit of the 2D density profile is reported in Fig. 1(c),
showing good agreement with the dSTORM data.

Figure 1: (a) dSTORM image of 1.5 mol% crosslinked, shell labeled, swollen
microgel particles at 25°C . (b) Zoomed individual particle of the same
sample. Rendering mode set to individual localization precision, iso
(Picasso). (c) dSTORM analysis of the microgel radial density profiles
illustrating a measured 2D profile (symbols) (averaged over $\sim$ 100
different microgels) and the corresponding fit with the fuzzy sphere model
(line).

The novel implementation of a temperature controller in the super-resolution
setup allows us to observe the VPT of microgels in situ. We perform
experiments at four different temperatures 25, 30, 35 and 38 °C, covering the
range where the transition occurs. The corresponding 2D density profiles are
reported in Fig. 2(a), showing the typical deswelling behavior of the
microgels across the VPT, also visible in the images of Fig. 2(b). From the
fuzzy sphere fits, we find that the microgel 2D radius $R_{tot}$ (see fig. S6)
decreases from $R_{tot}\sim 424$ nm to $R_{tot}\sim 194$ nm within the
investigated temperature range. Moreover, the size of the microgel estimated
by dSTORM is found to be in good agreement with the hydrodynamic radius
$R_{H}^{DLS}$ measured by dynamic light scattering (DLS), as reported in Fig.
2(c).
To understand whether the anchoring to the hydrophilic surface may affect the
swelling behavior of the microgels, we compare the experimental data with
simulations of a realistic model of microgels 40, having the same nominal
crosslinker concentration of the experimental sample.

We start for simplicity by showing results for simulations performed in bulk
conditions, i.e. in the absence of a nearby surface. To appropriately model
the deswelling of the microgel, we use an effective solvophobic potential,
which controls the monomer-monomer attraction through an effective temperature
$\alpha_{\text{mm}}$, as done in previous works 41 and described in the
Methods Section. In order to obtain a meaningful comparison between
experiments and simulations we mimic in silico the distribution of
fluorophores detected by dSTORM. This is done by defining a smooth core-shell
interface and converting to fluorophores all monomers belonging to the shell.
The procedure ensures that the fluorophores are smoothly distributed across
the interface as in the experiments, giving rise to a hollow profile as shown
in Fig. 2(d). More details on the protocol employed to select the fluorophores
can be found in the Methods Section and in the SI. We then calculate, as in
experiments, the 2D radial density profiles of the simulated microgel across
the VPT, only taking into account the external monomers and averaging over all
three directions in order to improve statistics.

The resulting $\rho^{2D}(r)$ are reported in Fig. 2(a), showing a nearly
quantitative agreement with experiments for all the main features: the mass of
the core, the extension of the corona as well as the presence of a peak (and
its position) at low temperatures. Such a peak is a feature stemming from the
combination of the 2D nature of the data and the fluorophore shell labeling,
as shown in Fig. S7. At temperatures below the VPT, we obtain a very good
description of the density profiles with the addition of a small noise, that
can be attributed to the finite optical resolution of dSTORM (see also Methods
Section and SI, Figs. S8-S9). This is further illustrated in the fluorophore
distributions with respect to their distance from the center of mass of the
microgel, reported in Fig. S10, which show that, as $T$ increases, the
employed fluorophore profiles move more and more toward the center of mass of
the microgel. The situation becomes more complex at higher temperatures. First
of all, we find that at $T=38$ °C, the experimental profiles coincide with
those of the full simulated microgel. Indeed, under these conditions the
microgels are fully collapsed and there is no longer evidence of a hollow
structure from the images of Fig. 2(b). We interpret the latter as a
consequence of several possible effects due to the twofold decrease in
particle size above the VPT. First, the size of the microgel’s central region,
where we expect a depleted fluorescence signal, is significantly reduced and
shifted to a small area around the center of the microgel 2D projection. For
small $r$-values, however, the radially averaged dSTORM signal is very noisy
due to a small number of fluorophore localisations. Moreover, the depth of
field and the microgel size are comparable, which may lead to subtle changes
in imaging conditions upon shrinkage 42. In addition, we should consider the
possibility that shrinkage leads to a slight redistribution of the dye-labeled
polymer strands across the volume, blurring the hole signal. Furthermore, the
refractive index of the microgels increases to a degree where we cannot
entirely neglect the scattering and attenuation of the exciting light beam.
Deciphering the various contributions to the disappearance of the apparent
hole is beyond the scope of this paper but will be addressed in future work.
Interestingly, at $T=35$ °C, just above the VPT, we find the presence in the
samples of fluctuations within the different microgel reconstructions, so that
some of them clearly display a hole, while some others do not, which could be
due to the fact that the experimental situation, e.g. the illumination or the
inclination, varies somewhat, as well as the large experimental uncertainty
near $r=0$. We consider this experimental observation in our modeling as
discussed in detail in the SI (see Fig. S9), which leads us to separately
account for the profiles of the two microgel populations and appropriately mix
them in the right proportion in order to obtain the 35 °C profile shown in
Fig. 2(a).

Figure 2: (a) Experimental 2D density profiles (symbols) for microgels at 25,
30 35 and 38 °C and numerical 2D density profiles (solid lines) calculated
with $\alpha_{\text{mm}}=0.0,0.5,0.9$ and 1.2, which best match the
experimental profiles at the four studied temperatures. In particular, data at
$T=25$ °C are described without any noise on the fluorophore location
($\sigma_{\text{sd}}=0.0$), while for $T=30$ °C $\sigma_{\text{sd}}=0.3$. At
$T=38$ °C, the microgels show a full collapse characterized by the complete
absence of a hollow structure and are thus compared to the whole simulated
microgel. Finally, for $T=35$ °C we adopt a mixture of hollow
($\sigma_{\text{sd}}=0.4$) and full microgels, as described in the text and SI
(Fig. S9) for further details. In order to compare experimental and simulation
density profiles, we normalize their area integral to one. The simulated
curves are reported on experimental units matching the respective 2D gyration
radii, as explained in the text; (b) dSTORM images of individual microgels at
25, 30, 35 and 38 °C (top to bottom). For the complete captured region of
interest (ROI), see Fig. S3; (c) Comparison of dSTORM estimated radius
$R_{tot}$ as a function of temperature with the DLS $R_{H}^{DLS}$ and
numerical $R_{H}^{sim}$ hydrodynamic radii. Also, 2D gyration radii from
dSTORM $R_{g}^{dSTORM}$ and simulations $R_{g}^{sim}$ are reported; (d)
Monomers-to-fluorophores conversion process: (d.1) Initial full microgel,
(d.2) monomers distinction according to their position either inside or
outside the core-shell interface and (d.3) final converted fluorophores.

Remarkably, it is evident from Fig. 2(a) that the decay at large distances is
extremely well captured at all temperatures, signaling that the simulations
are able to fully grasp the amount of shrinking observed in experiments. To
this aim, we note that a previous work 41 had established a mapping between
temperature and solvophobic parameter $\alpha_{\text{mm}}$ by comparing
numerical form factors with experimental ones of PNIPAM microgels obtained by
small angle X-ray scattering. Using dSTORM, we now confirm similar values of
$\alpha_{\text{mm}}$ in the studied temperature range. Small deviations occur
probably due to the differences in the synthesis process. To convert numerical
into experimental units, we impose that the 2D radius of gyration of the
microgel at $\alpha_{\text{mm}}=0.0$, i.e. in the absence of any monomer-
monomer attraction, has to be equal to the experimental value obtained at the
lowest temperature $T=25$ °C 43. With this procedure, we find that the
numerical unit length $\sigma$, corresponding to the size of a monomer bead in
the model, corresponds to $\sim 9\text{ nm}$ in real units. Such a conversion
is then used at all temperatures and throughout the manuscript. The resulting
numerical and experimental 2D gyration radii, $R_{g}^{sim}$ and
$R_{g}^{dSTORM}$ respectively, are found to be in good agreement at all
temperatures, as reported in Fig. 2(c). In addition, we also calculate the
hydrodynamic radius $R_{H}^{sim}$ numerically using the Zeno algorithm 44,
which was recently validated for microgels 45, 46, and again we find an
overall satisfactory agreement between experiments and simulations. The slight
discrepancy observed at high temperatures with respect to the DLS data may
likely be due to the fact that the dSTORM buffer solution is different from
the one used in DLS measurements. Indeed, for the same buffer conditions as in
dSTORM, DLS measurements of $R_{H}$ are not possible at high temperatures,
because of the onset of microgel aggregation.

Overall, the agreement of numerical simulations in the absence of a surface
with dSTORM data suggests that the anchoring of microgels in experiments has a
negligible effect on the particles structure throughout the volume phase
transition. To confirm that this is the case, we also perform simulations in
the presence of a nearby hydrophilic surface. Since the interactions between
monomers and wall is mainly repulsive, the surface does not affect the results
at any temperature, because the microgel always remain relatively far from the
surface. We then mimic the experimental procedure by anchoring a small
fraction of microgel monomers on the surface, as detailed in the Methods
Section. By adding the surface, we first need to quantify the effect of the
number of anchoring sites, which we have assessed in Fig. S11, by a direct
comparison to experiments. From this, we get a rough estimate of the fraction
of monomers bonded to the surface that is found to be smaller than 0.1%. Using
such a value, we find that the wall-anchored density profiles are equivalent
to those in bulk as shown in Fig. S12, confirming that the microgels structure
across the VPT remains unperturbed when anchored to a hydrophilic surface.
Altogether, these results validate the adopted experimental procedure,
indicating that they can clearly detect the changes in the internal structure
of the particles at different temperatures, paving the way for its application
to different systems. In particular, we expect that dSTORM will be very useful
to study copolymer microgels with more complex internal architecture where
different temperature behaviors of the forming polymers are at play 47, 48,
13.

###  2.2 Changing the surface affinity: the VPT of microgels close to a
hydrophobic surface

We now discuss the case of microgels close to a hydrophobic surface. To
realize this situation, the coverslips were first cleaned with KOH 3M and then
exposed overnight to 0.1 mL Hexamethyldisilazane (HMDS). Contact angle
measurements are shown in SI (Fig. S1), reporting contact angles larger than
80°. The resulting 2D density profiles are reported in Fig. 3(a), still
showing a clear deswelling of the particles with temperature, however
accompanied by the presence of a long tail at large distances, which persists
even in the more collapsed conditions. To visualize this behavior, dSTORM
images at three studied temperatures are shown in Fig. 3(b), clearly
indicating that the microgels adopt a core-shell-like arrangement, since the
external shell tends to maximize the contact with the surface. This behavior
is in agreement with previous super-resolution experiments 35, which were
performed at low temperatures only. The present results put forward novel
insights on how the tail is maintained even at high temperatures, denoting a
large affinity of the microgel to the surface. It is also interesting to
compare these results with observations at liquid-liquid interfaces, where
microgels adopt the so-called “ fried egg” configuration, confirmed by a large
amount of experiments (mainly through atomic force microscopy, after
deposition onto a surface) 49 as well as numerical simulations 50.
Interestingly, in the case of a liquid-liquid interface, temperature effects
on the microgel conformation are not very pronounced 51 because of the
dominant role of the interfacial tension, as also recently confirmed by direct
investigation through in situ neutron reflectometry 52. Hence, it is worth to
examine in more detail the role played by temperature in the present work,
where it seems that a competition between hydrophobic interactions, monomer-
monomer vs monomer-surface, is at work.

Figure 3: (a) Experimental 2D density profiles for microgels at 25, 30 and 35
°C on a surface exposed to HMDS. Inset: same density profiles with the y-axis
in logarithmic scale to better visualize the tail of the profiles; (b) dSTORM
images of individual microgels at 25, 30 and 35 °C (top to bottom). Here the
brightness for the image at 35 °C is increased to make the anchoring parts
visible for the reader. For the complete captured ROI, see SI Fig. S4.

To this aim, we resort to numerical simulations and we model the interactions
between monomer and surface with the same potential shape as for monomer-
monomer one. In this case, the parameter $\alpha_{\text{ms}}$ controls the
affinity between monomers and wall particles. While in the case of hydrophilic
surface, we set $\alpha_{\text{ms}}=0$, we now tune $\alpha_{\text{ms}}>0$, to
model the hydrophobicity of the surface, as detailed in the Methods Section.
We separately consider in the following both the case of a free microgel next
to the attractive surface (unbonded), which spontaneously sticks to it, and
that of microgel anchored to the surface (bonded). After tuning
$\alpha_{\text{ms}}$ as shown in Fig. S13, we find that the best agreement
between experiments and simulations is obtained for $\alpha_{\text{ms}}=0.9$,
i.e. a relatively large value of the attraction strength, confirming the
rather high hydrophobic character of the HMDS surface.

Figure 4:  (a) Experimental 2D density profiles (symbols) for microgels at 25
°C and corresponding numerical ones obtained with $\alpha_{\text{mm}}=0.0$ and
$\alpha_{\text{ms}}=0.9$ at different times for unbonded (dashed lines) and
bonded microgels (solid lines); the time intervals are display in the inset;
Inset: Evolution of the potential energy per particle of the bonded and
unbonded microgels versus time in numerical simulations. The highlighted
regions indicate the time intervals where the correspondingly coloured curves
have been calculated in the main panel; (b) additional measurements for 2D
density profiles at 25 °C and waiting times $t_{w}=$0 h, 2 h and 24 h in
comparison to the data reported in Fig. 3(a) and elsewhere in the manuscript.

We compare the resulting numerical and experimental 2D density profiles at
$T=$25 °C in Fig. 4(a) and note that the system takes a very long time to
reach equilibrium. This is illustrated in the inset, reporting the energy per
particle versus time, which is found to slowly decrease, denoting a long aging
regime, for both bonded and unbonded microgels. During this time, also the
density profile of the microgel slowly evolves, accumulating more and more
monomers at large distances from the center of mass. This indicates that, for
the chosen conditions, the hydrophobic attraction of the monomers to the wall
is dominant and pushes the microgel to extend further and further on the
surface. Interestingly, from the analysis at different values of
$\alpha_{\text{ms}}$, we found that only for $\alpha_{\text{ms}}\gtrsim 0.7$,
the microgel sticks to the surface, otherwise it tends to remain in a
spherical (almost unperturbed) condition. However, as soon as the microgel
sticks, slow rearrangements of the monomers on the surface take place, giving
rise to this non-negligible long-time evolution. These results show that, on
increasing time, the outer shell progressively extends more than in the
experiments, thus overestimating the tail of the density profiles in the case
of an unbonded microgel. This is also reflected in the short-distance
behaviour of the numerical $\rho^{2D}(r)$ whose height results to be lower
than the height observed with dSTORM. Instead, looking at the numerical data
for a bonded microgel, also reported in Fig. 4(a), we find that the
experimental data are much better captured and, in particular, at long
simulation times the tail of the distribution roughly extends of the same
amount as in experiments. Hence, differently from the case of a hydrophilic
surface, here a small degree of anchoring does affect the overall conformation
of a microgel due to the strong binding to the surface and the underlying
connectivity of the network. We thus conclude that we cannot neglect the
presence of anchoring in the simulations for a more quantitative description
of super-resolution data close to a hydrophobic surface.

To verify whether this holds at all $T$, we extend the comparison to higher
temperatures to tackle the interesting case of a collapsing microgel close to
a hydrophobic surface. To this aim, several details need to be taken into
account. The first point to address is aging, which is very pronounced in the
simulations, due to the fact that, at high $T$, there is a competition between
the monomer-monomer attraction, modelled by $\alpha_{\text{mm}}=0.9$ and the
monomer-surface attraction, modelled with $\alpha_{\text{ms}}=0.9$ as
determined at $T=25$ °C. In particular, we find that the monomer-monomer
attraction, being augmented by the large number of nearby monomers and the
overall connectivity, will eventually dominate when the two $\alpha$
parameters are the same, so that the microgel will tend to collapse further,
decreasing the extent of the large distance tail. It is now important to
assess the role of aging on the experimental results. To this aim, we
performed additional measurements for microgels on the hydrophobic surface at
different waiting times $t_{w}$ for $T=25$ °C. We define $t_{w}=0$ as the time
when we started the dSTORM measurements. When comparing the density profiles
of measurements done at $t_{w}=$0h, 2h and 24h, reported in Fig. 4(b), we
detect no significant differences in the curves. Indeed, the time in between
sample preparation and data acquisition, which is roughly of the order of one
hour, seems to be long enough for the system to reach equilibrium, so that we
can neglect aging effects in the experimentally measured time window. These
results should thus be compared with numerical ones at very long times.

Figure 5:  (a) Experimental 2D density profiles (symbols) and numerical 2D
density profiles (solid lines) calculated with $\alpha_{\text{ms}}=0.9$,
$\alpha_{\text{mm}}=0.0,0.5,0.9$. Data for $T=25$ °C are described without any
noise on the fluorophore location ($\sigma_{\text{sd}}=0.0$), while for $T=30$
°C $\sigma_{\text{sd}}=0.3$, in analogy with the hydrophilic case. Instead,
data for $T=35$ °C show the absence of a hollow structure and are thus
compared to the whole simulated microgel. Inset: y-axis in logarithmic scale.

Next, we consider the fact that, at high $T$ the density profiles may also
depend on the way the microgel is anchored on the surface. To address this
problem, we consider the anchoring process of a swollen microgel (see Methods)
both onto a hydrophilic and onto a hydrophobic surface. This yields different
bonding patterns: when the anchoring process is done on a hydrophobic surface
the bonds tend to be made for monomers located further away from its plane
projected center of mass. We then calculate $\rho^{\text{2D}}(r)$ above the
VPT, (see SI, Fig. S15) and find that the presence of a hydrophobic surface
allows for the formation of bonds over a more extended region compared to the
hydrophilic one, which results in a larger tail of the density profile. This
is in closer agreement with experiments, because it also more realistically
mimics the way that the anchoring is made.

Having established the optimal ways of comparing experiments and simulations,
we are now able to finally report the comparison of experimental and numerical
density profiles at all investigated temperatures close to the hydrophobic
surface in Fig. 5. Below the VPT temperature ($T$=25 and 30 °C) the profiles
still show the presence of a peak, indicative of the fluorophore outer shell
distribution, that we keep exactly identical to that used in the presence of
the hydrophilic surface (see Fig. S10). However, at the highest studied $T$=35
°C the experimental profile shows a monotonic decrease at low distances, being
characterized by the absence of a hollow structure, similarly to what observed
at high temperatures for the case of a hydrophilic surface. Indeed, we find
that the comparison with any fluorophore distribution is not able to reproduce
the experimental data (see Fig.S15), but instead we consider the full microgel
profile and obtain a very satisfactory agreement. This happens at a slightly
lower temperature with respect to the hydrophilic case, probably due to the
presence of the attractive surface, which effectively favours an anticipated
collapse of the microgel. Overall, Fig. 5 shows that simulations, taking
appropriately into account the subtleties of anchoring and fluorophore
detection, can capture the experimental data at all temperatures also in the
case of a hydrophobic surface.

The quantitative comparison between experiments and simulations can be
summarized in Fig. 6. This reports the average images, recorded by dSTORM, of
individual microgels at three studied temperatures and close to the two
different surfaces. While for the hydrophilic case, the typical spherical
pattern is retained, for the hydrophobic surface, the shell around the core
persists at all temperatures, in very good agreement between experiments and
simulations. In addition, it is clear from the direct comparison of the images
for 35 °C for the two examined cases, that the microgel internal structure is
much more compact (without a perceptible hole in the centre) for the
hydrophobic conditions, in agreement with the employed numerical description.

Figure 6: Top panel - averaged images of individual microgels for increasing
temperatures (25, 30 and 35 °C) on hydrophilic surface (dSTORM-left, MD-
right). Bottom panel - averaged images of individual microgels for increasing
temperatures (25, 30 and 35 °C) on hydrophobic surface (dSTORM-left, MD-
right). The brightness for the images on hydrophobic surface is increased to
make the anchoring parts clearly visible for the reader.

##  3 Conclusions

In this manuscript we performed a detailed investigation of the volume phase
transition of standard pNIPAM microgels by dSTORM measurements combined with
coarse-grained numerical simulations of realistic microgels. The experiments
were carried out by anchoring the microgels to two different substrates: a
fully hydrophilic and a largely hydrophobic surface.

In the former case, we find that the presence of the nearby wall does not
significantly affect the conformations of the microgels, which always remain
spherical at all investigated temperatures. It was thus possible to monitor
the occurrence of the VPT and to quantitatively compare with simulations of
isolated microgels in bulk and on hydrophilic surfaces. The comparison was
performed adopting a procedure where only a fraction of monomers, those
emulating the fluorophores, were taken into account in the calculation at low
temperatures. Instead, with the increasing collapse of the microgel, the
hollow core progressively becomes less experimentally detectable, so that the
profile is well captured by that of the full microgel. The favourable
agreement of experiments and simulations, on one hand, reinforces the
numerical model, previously shown to be able to describe the evolution of
microgels form factors across the VPT 41 and now validated also in direct
space, and on the other hand, establishes the use of the temperature-
controlled dSTORM approach used in this work for the nano-scale resolution of
the internal structure of microgels. Indeed, despite the technique requires
the anchoring onto a surface, the successful comparison with simulations
indicates that such anchoring can be safely neglected in the description,
because it does not affect the overall swelling-deswelling process. To this
aim we showed that the dSTORM measurements are quantitatively captured also by
simulations performed in the absence of a surface.

Having validated the combination of our methods, we then moved on to examine
the influence of the surface on the VPT when interactions between monomers and
substrate are highly attractive. For the latter case, we are not aware of any
previous detailed characterization of the microgel conformation attached to a
hydrophobic surface as a function of temperature. While at low temperatures,
in agreement with previous works 35, 36, the preferential attraction to the
surface induces the microgel to spread and deform, rather similarly to the
case of liquid-liquid interfaces 53, 50, thus adopting a core-shell-like
structure, the previously unexplored high temperature regime reveals an
interesting behavior. This is due to the emerging competition between microgel
deswelling, controlled by the decrease in the solvent-monomer affinity, and
the adhesion to the surface, modulated by the hydrophobic coating of the
substrate. Thanks to the help of simulations, where these two parameters can
be varied more easily than in experiments, a delicate interplay between the
two mechanisms arises, giving rise to a subtle aging behavior in the
simulations. However, in experiments, the typical preparation time is about
one hour, which allows for a full equilibration of the system, so that we can
safely neglect aging effects in the measurements. In the simulations, we
instead find that a very long equilibration takes place, which does not change
the overall shape of the density profiles but may affect the tail of the
profiles. To determine the long-time behavior of such tails, it turns out to
be crucial to appropriately consider the presence of the anchoring of the
microgels to the substrate. In particular, in the absence of anchoring, our
simulated microgels would continue to spread even further at the studied
conditions, a process that is inhibited by the presence of permanent bonds
with the surface which, combined with the network structure of the particles,
limits the growth of the tail at long times, in good qualitative agreement
with experiments. Importantly, comparing the present results for the
hydrophobic surface with those obtained at liquid-liquid interfaces, we note
that the use of a solid substrate enables a more effective exploitation of
temperature effects on the final microgel configuration. Indeed, we find a
rather exotic conformation made of a collapsed-core plus an extended shell due
to the interplay between the two tunable and competing hydrophobic strengths.
Such conformation is not easily observed at liquid-liquid interfaces, because
in that case the interfacial tension is always dominant with respect to
temperature.

In future works it will be interesting to extend the present results to the
investigation of different surfaces, varying the hydrophobic affinity of the
microgel 32 as well as to vary the crosslinker concentration. While here we
focused on rather soft microgels with low amount of crosslinkers, we expect
that a variation of the particle softness 54 will produce additional
interesting features. In particular, the use of ultra-low-crosslinked
microgels may be especially worth exploring, due to their intrinsic difference
with respect to standard ones 52, 55. In addition, a careful comparison
between microgel conformations at liquid-solid vs liquid-liquid interface,
building on the one performed by AFM measurements 55, would be desirable to
fully unveil the conformational differences of the particles with nanoscopic
resolution.

The present study opens the way to use dSTORM to investigate the VPT behavior
of microgels of complex inner structure, including co-polymerized ones with
different responsivity with respect to temperature or pH or different internal
architectures, e.g. interpenetrated network microgels. To this aim, it would
be desirable to first extend the present analysis to different labeling over
the whole microgel and not only on the surface, as previously done in Ref. 28,
to be able to detect variations throughout the particles, even in the core. In
addition, a full 3D imaging should be implemented so that the full density
profiles could be directly compared to simulations or to experimental form
factors, although this is not crucial in the presence of microgel deformation
onto a surface, such as the one studied in this work.

Finally, an additional step foward will be to move from simple microgels to
compartmentalized ones 30 useful for segregating reactive components and
coordinating chemical reactions, or to nanocomplexes where these are decorated
with other objects, such as nanoparticles, e.g. for enhancing plasmonic or
optical properties56, or biomolecules, e.g. for delivery purposes 57, 58, 59.
In these cases, the advanced superresolution approach developed in the present
work will enable the visualization of the full temperature behavior in situ,
which is crucial to control adsorption and release of these molecules with
high potential for their fundamental mechanisms occurring at the nanoscale and
for improving their applications in different fields.

##  4 Materials and Methodology

###  4.1 Microgel synthesis

We synthesised pNIPAM microgels using the free radical precipitation
polymerisation method as previously described by Conley et al. 28
N-isopropylacrylamide (Acros Organics, 99%), NIPAM, is the monomeric unit
which is recrystallised in hexane before use and N,N-methylene bis(acrylamide)
(Sigma–Aldrich, 99%), BIS, is the cross-linker. In addition, N-(3-aminopropyl)
methacrylamide hydrochloride (Polysciences), APMA, is added as a co-monomer to
incorporate free amine groups into the microgel network. The primary amines
are used to fluorescently label the microgels by reacting with the
succinimidyl ester groups of the dye AlexaFluor 647.
2,2-Azobis(2-methylpropionamidine) dihydrochloride (Sigma–Aldrich, 98%), AAPH,
is used to initiate the polymerisation. First, in a three-neck round bottom
flask, NIPAM (1.430 g) and BIS (0.029 g) are dissolved in 85 g H2O. The
reaction mixture is purged with nitrogen for 30 min before the temperature is
raised to 70 °C. Next, 0.0365 g AAPH, previously dissolved in 5 g H2O, is
added to the reaction mixture. Five minutes after the initiator is added and
the solution has started to turn white, 0.0198 g APMA dissolved in 10 g H2O is
introduced to the reaction using a syringe pump at an addition rate of 0.5
ml/min. The reaction mixture is kept at 70 °C for 4 h before cooling down
rapidly in an ice bath. Following this protocol, inhomogeneous core-shell
microgels are formed with APMA co-monomer only being present on the shell of
the microgel network. A purification step follows to remove all unreacted
monomers in the solution by centrifugation (3-4 times). We label the microgels
using an excess amount of fluorescent dye Alexa 647 and leave them in an
oscillation plate for 1 h before another purification step to remove all
unreacted dye.

###  4.2 Super resolution microscopy

To perform dSTORM experiments, we use a Nikon TiEclipse inverted microscope
with an EMCCD camera (Andor iXon Ultra 897) and total internal reflection
fluorescence (TIRF) arm to achieve highly inclined illumination and limit the
fluorescence background noise. We use a continuous wave red laser, coherent
Genesis MX-STM with 1000 mW output power at 639 nm, providing a single mode
TEM00 Gaussian beam, horizontally polarised. The high power red laser enables
fluorophores in their excited state through intersystem crossing to occupy the
triplet state where they get trapped. A second laser (Toptica iBeam Smart)
with 120 mW output at 405 nm, vertically polarized, is used to tune the
blinking density. Both lasers are coupled into a single-mode fiber (S405XP,
Thorlabs) into the TIRF arm. The light is focused on the back aperture of a
high numerical aperture and magnification objective (NA 1.49 and 100x
magnification). With an extra zoom lens placed before the camera, the final
pixel size is 110 nm. A dichroic filter with a wavelength of 700 nm and
bandwidth of 50 nm is placed in the detection pathway (ET700/50, Chroma).
First, we set the appropriate buffer conditions at 50 mM Cysteamine, and pH
adjusted to 8. Second, we illuminate the sample at a wavelength of
$\lambda=639$ nm using a high laser power (3.4 kW/cm2) to bring the
fluorophores to a metastable dark state. We employ a second laser at 405 nm to
induce stochastic spare fluorescent light emission and control the blinking
density by adjusting the laser power. We acquire 30000 - 60000 frames with an
exposure time of 10 - 20 ms. Using Picasso software, for each frame we
localize individual blinking events that we then fit with a 2D Gaussian
profile using the maximum likelihood estimation method (MLE). Following this
procedure, we generate a database that contains x-y positions, intensity, and
localization precision (x,y). To reconstruct the entire super-resolution image
we filter the list of localization and only retain localizations above a
certain photon count threshold, with low ellipticity ($<$0.2) and with a
sufficient localization precision in x and y ($<$ 0.2 pixel). We correct the
reconstructed image’s drift using a redundant cross-correlation algorithm and
then render the image using the individual localization precision (iso) mode
unless otherwise stated.

For the aging measurements, we took into account that the number of
localization may vary with time, due to the fact that the buffer conditions
change over long imaging time 60. In order to compare among the different
waiting times, we then consider only the first 10000 blinking frames of the
measurement, corroborating that the number of localizations per microgel
(about 3000) were comparable for all cases.

###  4.3 Light scattering measurements

DLS measurements are performed using the commercial LS Spectrometer, 2D-DLS
Pseudo cross correlation set-up (LS Instruments AG, Switzerland). We increase
the temperature from 25 to 35 °C with $\Delta T$ = 1 °C as step size. Laser
light with wavelength of 660 nm was used to perform the experiments at
scattering angles of 40 - 80° with 5° step size. Three measurements of 40
seconds were obtained at every angle. The diffusion coefficients D and
hydrodynamic radii $R_{H}^{DLS}$ were extracted from a multi angle analysis of
the first order cumulant fit.

###  4.4 Numerical Methods

####  4.4.1 Microgel Modeling

We simulate the microgels assembly following the methods described in Refs.
40, 41. The assembly method is a two step process: first, (i) bi- and
tetravalent patchy particles self-assemble inside a spherical cavity forming a
fully-bonded disordered network; then, (ii) the topology of the network gets
fixed by replacing the patchy interactions with permanent bonds. For the fixed
topology, particles composing the microgel, which we will also refer to as
monomers, interact with the Kremer-Grest bead-spring model 61, i.e. monomers
overlap is avoided with the repulsive Weeks-Chandler-Andersen (WCA) potential
62

$V_{\text{WCA}}(r)=\begin{cases}4\epsilon\left[\left(\frac{\sigma}{r}\right)^{12}-\left(\frac{\sigma}{r}\right)^{6}\right]+\epsilon&\text{
if }r\leq 2^{1/6}\sigma\\\ 0&\text{ otherwise }\end{cases}$ (1)

while permanent bonding between monomers is ensured by adding the Finite-
Extensible-Nonlinear-Elastic (FENE) potential for bonded pairs 63

$V_{\text{FENE}}(r)=-\epsilon
k_{\text{F}}R_{0}^{2}\ln{\left[1-\left(\frac{r}{R_{0}\sigma}\right)^{2}\right]}\text{
if }r\leq R_{0}\sigma$ (2)

where $\sigma$ is the monomers diameter, $\epsilon$ the energy scale,
$k_{\text{F}}=15$ the dimensionless spring constant, and $R_{0}=1.5$ the
maximum bond extension. The volume phase transition of the microgels is
reproduced by adding an attractive solvophobic interaction
$V_{\alpha_{\text{mm}}}(r)$ among monomers 64, 65. The attraction strength is
controlled by the parameter $\alpha_{\text{mm}}$ which encodes the monomer-
monomer effective attraction, modeling implicitly the reduction of monomer-
solvent affinity when increasing the temperature:

$V_{\alpha_{\text{mm}}}(r)=\begin{cases}-\epsilon\alpha_{\text{mm}}&\text{ if
}r\leq 2^{1/6}\sigma\\\
-\frac{1}{2}\epsilon\alpha_{\text{mm}}\left[\cos{\left(\gamma(r/\sigma)^{2}+\beta\right)}-1\right]&\text{
if }2^{1/6}\sigma<r\leq R_{0}\sigma\\\ 0&\text{ otherwise }\end{cases}$ (3)

with $\gamma=\pi(2.25-2^{1/3})^{-1}$ and $\beta=2\pi-2.25\gamma$. When
$\alpha_{\text{mm}}=0$ no implicit-solvent attraction is added between
monomers, reproducing good solvent conditions and a swollen microgel; instead,
monomers attraction rises by increasing $\alpha_{\text{mm}}$, thus shrinking
the microgel size and mimicking the worsening of the implicit solvent.

####  4.4.2 Solid Surface Modeling and Monomers Anchoring

To reproduce the behavior of the microgel close to a surface, we model the
latter as two layers of wall particles, that are initially located on a
compact square lattice of site $\sigma$ at the bottom of the simulation box;
the separation between layers is $0.7\sigma$. To avoid crystallization of the
monomers close to the surface, the wall particles are randomly displaced from
the lattice sites, including the direction perpendicular to the plane,
following a Gaussian distribution with standard deviation
$\sigma_{\text{sd}}=0.2$. The obtained layers are then subsequently fixed
throughout the whole simulation runs.

Microgel monomers interact with wall-particles via the WCA potential eq. 1 and
the $V_{\alpha_{\text{ms}}}$ potential, which is identical to Eq. 3, but this
time replacing the monomer-monomer attraction $\alpha_{\text{mm}}$ with the
monomer-surface one, $\alpha_{\text{ms}}$, now encoding the surface
hydrophobicity.

To mimic experimental conditions where the microgel is physically anchored to
the wall, we also consider the case where permanent bonds between a few
monomers and the surface particles are formed. This is obtained by the
following procedure (illustrated in Fig. 7 for the hydrophilic scenario
$\alpha_{\text{ms}}=0$): (i) a swollen microgel (equilibrated in bulk at
$\alpha_{\text{mm}}=0$) is pushed towards the wall; (ii) when it comes in
contact with the surface, monomers with distance less than $dz=1.5\sigma$ from
the upper layer of the wall are considered, and among them, (iii) $b$ monomers
are randomly chosen and anchored to their closest wall-particle via a harmonic
potential $V(r)=K(r-r_{0})^{2}$ with $K=15$ and $r_{0}=2^{1/6}\sigma$;
finally, (iv) the microgel is let to relax to its equilibrium state. The
procedure is then repeated for different surface $\alpha_{\text{ms}}$
conditions. We did it both for the hydrophilic $\alpha_{\text{ms}}=0$ and
hydrophobic $\alpha_{\text{ms}}=0.9$ conditions, yielding different bonding
patterns. Density profiles calculated with microgels anchored to a hydrophilic
surface are comparable to experiments in all cases, except for the
measurements at $T=$ 35°C close to a hydrophobic surface, where we found that
the extension of the tail is better captured by simulations of microgels
initially anchored to a hydrophobic surface (see Fig. S15). Another important
parameter to take into account is the number of bonds $b$ that we should
consider in the simulations. As mentioned in the Results Section and shown in
Fig. S11, for the hydrophilic surface we tried different values of $b$ and
found that $b=25$ is the most similar to experimental data. For the
hydrophobic case, we expect in experiments a much larger number of bonds due
to the additional attraction to the surface in the anchoring procedure. For
this reason, we performed all simulations (that take much longer, also due to
the long aging regime) with a fixed value $b=200$, roughly one order of
magnitude difference with respect to the hydrophilic case. This value was then
found to be in rather good agreement with experiments in terms of the tail of
the density profiles.

Figure 7: Bonding of the microgel to the hydrophilic surface: (a) the microgel
in the bulk is pushed towards the wall, (b) when in contact, a list is made
with all monomers at a distance to the wall smaller than $dz$ (monomers in
green), from which some are randomly chosen (blue) and bonded to their closest
wall-particle (pink bond), (c) the microgel is let to relax.

####  4.4.3 Fluorophore Emulation

Super-resolution experiments only detect the presence of fluorophores labeling
the microgels. We thus need a way to go from the full monomer representation
of the microgel to the case of only counting fluorophores in the calculation
of the density profiles to mimic the partial labeling performed in the
experimental synthesis. In our simulation this is equivalent to choose a
particular set of monomers that are considered to be the fluorophores. Since
microgels are synthesized in such a way that these are mostly located in the
surface, the monomer-to-fluorophore conversion process must take this into
account. To this aim, we perform the fluorophore selection with the following
procedure, illustrated in Fig. 2(d): we allocate monomers to the fluorophore
list according to their distance $r$ from the microgels center of mass (CM) in
a single equilibrated configuration by testing if $r>\langle R_{g}\rangle\cdot
P(\sigma_{\text{sd}},\mu=1)$, where $P(\sigma_{\text{sd}},\mu=1)$ is a random
number taken from a Gaussian distribution of mean $\mu=1$ and standard
deviation $\sigma_{\text{sd}}$. The addition of such a Gaussian noise softens
the definition of the interface and takes into account averaging over
different equilibrium configurations as well as the fact that there could be
mislocalization or loss of resolution in the experiments. Indeed, we find that
$\sigma_{\text{sd}}$ increases with temperature, due to the fact that it is
more difficult to resolve the core-shell interface and the position of the
fluorophores once the microgel collapses, as shown in Fig. S8. We find that
$\sigma_{\text{sd}}=0.0$ and $0.3$ for 25, 30 °C. However, at high
temperatures, when the microgel approaches a full collapse, the fluorophore
description loses its validity. In particular, at 38 (35) °C for the
hydrophilic (hydrophobic) case, respectively, as discussed in the main text,
the density profiles do not show a peak in the outer shell, and we resort to
consider all the monomers to calculate the numerical 2D density profiles.
Instead, for 35 °C and hydrophilic surface a mixture of the two approaches is
needed, because fluctuations from microgel to microgel are large and we have
almost an equal population of fully resolved and hollow profiles. We thus
average them following experimental proportions, using
$\sigma_{\text{sd}}=0.4$ for the fluorophore distribution.

####  4.4.4 Simulation Parameters

Molecular dynamics (MD) simulations of three independent realizations of the
microgels with different topologies were performed using LAMMPS66. Microgels
were assembled using oxDNA package67 starting with $N=42000$ patchy particles,
of which 1.5% are tetravalent (crosslinkers) to match experimental conditions.
It is important to note that the adopted $N$ yields the size of a monomer to
be around 9 nm, as described in the text. This value is slightly larger than
the estimated Kuhn length for PNIPAM 68, but we previously showed 41 that,
upon increasing the number of monomers, the bead size approaches the correct
value without qualitatively affecting the results. The wall is made of two
layers of 90000 wall-particles each. Hence, each system has about 222000
particles in total.

We used a Langevin thermostat with reduced temperature
$T^{*}=k_{\text{B}}T/\epsilon=1$, particles mass $m=1$, and integration time
$\delta t=0.002\sqrt{m\sigma^{2}/\epsilon}$. Wall-particles are kept fixed by
not including them in the integration scheme. The length of the simulations
changes for the different scenarios; we monitored the energy as to see when
the system had thermalized, and then ran additional steps from where the
density profiles were calculated. Simulations in the bulk and on a hydrophilic
surface take around $1\times 10^{6}$ steps to equilibrate. After this, we ran
additional $15\times 10^{6}$ steps. Density profiles are calculated from
configurations in the last $5\times 10^{6}$ steps of the simulation. Instead,
the evolution of microgels placed close to a hydrophobic surface is very slow.
Hence, in this case we performed $100\times 10^{6}$ steps. We calculated the
profiles at different waiting times and found that their shape did not show
significant changes (except at very large distances), and is similar to what
reported in Fig. 4.

The radial density profiles of individual equilibrated microgels were
calculated as

$\rho(r)=\left\langle\frac{1}{N}\sum_{i=1}^{N}\delta\left(\left|\vec{r}_{i}-\vec{r}_{\text{CM}}\right|-r\right)\right\rangle$
(4)

where $\langle\cdot\rangle$ is the average over several configurations. When
calculating the 2D profiles, $\vec{r}$ and $\vec{r}_{\text{CM}}$ do not
include the component perpendicular to the surface. Observables were averaged
over three independent microgel configurations in order to avoid singular
topology characteristics that may occur. Images of numerical microgels were
created with ovito 69.

We thank G. Del Monte for help with the calculation of the hydrodynamic
radius. X.S., R. R.-B., F.S. and E.Z. acknowledge funding from the European
Union’s Horizon 2020 research and innovation program under the Marie
Skłodowska-Curie (ITN SUPERCOL, Grant Agreement 860914). This work has
benefited from financial support from the Swiss National Science Foundation
through the National Centre of Competence in Research ’Bio-Inspired Materials’
and project number # 149867 (F.S. and M.B.).

Additional details on the sample preparation for dSTORM measurements, the
image analysis, and the density profiles fit using the fuzzy sphere model for
microgels on the hydrophilic surface; also additional simulation details
regarding the 2D projected density profiles, the estimation of the fluorophore
distribution, the comparison between bulk and hydrophilic surface results, the
monomer-surface parameter and the anchoring of the microgel to the surfaces.

## References

  * Gasser et al. 2001 Gasser, U.; Weeks, E. R.; Schofield, A.; Pusey, P.; Weitz, D. Real-space imaging of nucleation and growth in colloidal crystallization. _Science_ 2001, _292_ , 258–262
  * Weeks et al. 2000 Weeks, E. R.; Crocker, J. C.; Levitt, A. C.; Schofield, A.; Weitz, D. A. Three-dimensional direct imaging of structural relaxation near the colloidal glass transition. _Science_ 2000, _287_ , 627–631
  * Hallett et al. 2018 Hallett, J. E.; Turci, F.; Royall, C. P. Local structure in deeply supercooled liquids exhibits growing lengthscales and dynamical correlations. _Nature communications_ 2018, _9_ , 1–10
  * Thorneywork et al. 2017 Thorneywork, A. L.; Abbott, J. L.; Aarts, D. G.; Dullens, R. P. Two-dimensional melting of colloidal hard spheres. _Physical review letters_ 2017, _118_ , 158001
  * Pelton 2000 Pelton, R. Temperature-sensitive aqueous microgels. _Advances in colloid and interface science_ 2000, _85_ , 1–33
  * Stieger et al. 2004 Stieger, M.; Richtering, W.; Pedersen, J. S.; Lindner, P. Small-angle neutron scattering study of structural changes in temperature sensitive microgel colloids. _The Journal of Chemical Physics_ 2004, _120_ , 6197–6206
  * Schimka et al. 2017 Schimka, S.; Lomadze, N.; Rabe, M.; Kopyshev, A.; Lehmann, M.; von Klitzing, R.; Rumyantsev, A. M.; Kramarenko, E. Y.; Santer, S. Photosensitive microgels containing azobenzene surfactants of different charges. _Physical Chemistry Chemical Physics_ 2017, _19_ , 108–117
  * Wu and Wang 1998 Wu, C.; Wang, X. Globule-to-coil transition of a single homopolymer chain in solution. _Physical review letters_ 1998, _80_ , 4092
  * Saunders and Vincent 1999 Saunders, B. R.; Vincent, B. Microgel particles as model colloids: theory, properties and applications. _Advances in colloid and interface science_ 1999, _80_ , 1–25
  * Senff and Richtering 1999 Senff, H.; Richtering, W. Temperature sensitive microgel suspensions: Colloidal phase behavior and rheology of soft spheres. _The Journal of chemical physics_ 1999, _111_ , 1705–1711
  * Karg and Hellweg 2009 Karg, M.; Hellweg, T. New “smart” poly (NIPAM) microgels and nanoparticle microgel hybrids: Properties and advances in characterisation. _Current Opinion in Colloid & Interface Science_ 2009, _14_ , 438–450
  * Agrawal and Agrawal 2018 Agrawal, G.; Agrawal, R. Stimuli-Responsive Microgels and Microgel-Based Systems: Advances in the Exploitation of Microgel Colloidal Properties and Their Interfacial Activity. _Polymers_ 2018, _10_
  * Rivas-Barbosa et al. 2022 Rivas-Barbosa, R.; Ruiz-Franco, J.; Lara-Peña, M. A.; Cardellini, J.; Licea-Claverie, A.; Camerin, F.; Zaccarelli, E.; Laurati, M. Link between Morphology, Structure, and Interactions of Composite Microgels. _Macromolecules_ 2022, _55_ , 1834–1843
  * Alsayed et al. 2005 Alsayed, A. M.; Islam, M. F.; Zhang, J.; Collings, P. J.; Yodh, A. G. Premelting at defects within bulk colloidal crystals. _Science_ 2005, _309_ , 1207–1210
  * Zhang et al. 2009 Zhang, Z.; Xu, N.; Chen, D. T.; Yunker, P.; Alsayed, A. M.; Aptowicz, K. B.; Habdas, P.; Liu, A. J.; Nagel, S. R.; Yodh, A. G. Thermal vestige of the zero-temperature jamming transition. _Nature_ 2009, _459_ , 230–233
  * Yunker et al. 2014 Yunker, P. J.; Chen, K.; Gratale, M. D.; Lohr, M. A.; Still, T.; Yodh, A. Physics in ordered and disordered colloidal matter composed of poly (N-isopropylacrylamide) microgel particles. _Reports on Progress in Physics_ 2014, _77_ , 056601
  * Philippe et al. 2018 Philippe, A.-M.; Truzzolillo, D.; Galvan-Myoshi, J.; Dieudonné-George, P.; Trappe, V.; Berthier, L.; Cipelletti, L. Glass transition of soft colloids. _Physical Review E_ 2018, _97_ , 040601
  * Das et al. 2006 Das, M.; Zhang, H.; Kumacheva, E. Microgels: Old materials with new applications. _Annu. Rev. Mater. Res._ 2006, _36_ , 117–142
  * Fernandez-Nieves et al. 2011 Fernandez-Nieves, A.; Wyss, H.; Mattsson, J.; Weitz, D. A. _Microgel suspensions: fundamentals and applications_ ; John Wiley & Sons, 2011
  * Zeng et al. 2021 Zeng, Z.; Liang, J.; Yu, R.; Liu, J.; Cao, M.; Wang, S.; Xia, Y. Programmable Color in a Free-Standing Photonic Microgel Film with Ultra-Fast Response. _ACS Applied Materials & Interfaces_ 2021, _13_ , 25563–25570
  * Heilemann et al. 2008 Heilemann, M.; van de Linde, S.; Schüttpelz, M.; Kasper, R.; Seefeldt, B.; Mukherjee, A.; Tinnefeld, P.; Sauer, M. Subdiffraction-Resolution Fluorescence Imaging with Conventional Fluorescent Probes. _Angewandte Chemie International Edition_ 2008, _47_ , 6172–6176
  * Huang et al. 2009 Huang, B.; Bates, M.; Zhuang, X. Super-resolution fluorescence microscopy. _Annual review of biochemistry_ 2009, _78_ , 993–1016
  * Betzig et al. 2014 Betzig, E.; Hell, S. W.; Moerner, W. E. The nobel prize in chemistry 2014. _Nobel Media AB_ 2014,
  * Henriques et al. 2011 Henriques, R.; Griffiths, C.; Hesper Rego, E.; Mhlanga, M. M. PALM and STORM: unlocking live-cell super-resolution. _Biopolymers_ 2011, _95_ , 322–331
  * Bückers et al. 2011 Bückers, J.; Wildanger, D.; Vicidomini, G.; Kastrup, L.; Hell, S. W. Simultaneous multi-lifetime multi-color STED imaging for colocalization analyses. _Optics express_ 2011, _19_ , 3130–3143
  * Conley et al. 2017 Conley, G. M.; Aebischer, P.; Nöjd, S.; Schurtenberger, P.; Scheffold, F. Jamming and overpacking fuzzy microgels: Deformation, interpenetration, and compression. _Science advances_ 2017, _3_ , e1700969
  * Busko et al. 2012 Busko, D.; Baluschev, S.; Crespy, D.; Turshatov, A.; Landfester, K. New possibilities for materials science with STED microscopy. _Micron_ 2012, _43_ , 583–588
  * Conley et al. 2016 Conley, G. M.; Nöjd, S.; Braibanti, M.; Schurtenberger, P.; Scheffold, F. Superresolution microscopy of the volume phase transition of pNIPAM microgels. _Colloids and Surfaces A: Physicochemical and Engineering Aspects_ 2016, _499_ , 18–23
  * Wöll and Flors 2017 Wöll, D.; Flors, C. Super-resolution Fluorescence Imaging for Materials Science. _Small Methods_ 2017, _1_ , 1700191
  * Gelissen et al. 2016 Gelissen, A. P. H.; Oppermann, A.; Caumanns, T.; Hebbeker, P.; Turnhoff, S. K.; Tiwari, R.; Eisold, S.; Simon, U.; Lu, Y.; Mayer, J.; Richtering, W.; Walther, A.; Wöll, D. 3D Structures of Responsive Nanocompartmentalized Microgels. _Nano Letters_ 2016, _16_ , 7295–7301, PMID: 27701865
  * Pujals et al. 2019 Pujals, S.; Feiner-Gracia, N.; Delcanale, P.; Voets, I.; Albertazzi, L. Super-resolution microscopy as a powerful tool to study complex synthetic materials. _Nature Reviews Chemistry_ 2019, _3_ , 68–84
  * Scheffold 2020 Scheffold, F. Pathways and challenges towards a complete characterization of microgels. _Nature Communications_ 2020, _11_ , 1–13
  * Bergmann et al. 2018 Bergmann, S.; Wrede, O.; Huser, T.; Hellweg, T. Super-resolution optical microscopy resolves network morphology of smart colloidal microgels. _Physical Chemistry Chemical Physics_ 2018, _20_ , 5074–5083
  * Otto et al. 2020 Otto, P.; Bergmann, S.; Sandmeyer, A.; Dirksen, M.; Wrede, O.; Hellweg, T.; Huser, T. Resolving the internal morphology of core–shell microgels with super-resolution fluorescence microscopy. _Nanoscale Advances_ 2020, _2_ , 323–331
  * Hoppe Alvarez et al. 2019 Hoppe Alvarez, L.; Eisold, S.; Gumerov, R. A.; Strauch, M.; Rudov, A. A.; Lenssen, P.; Merhof, D.; Potemkin, I. I.; Simon, U.; Wöll, D. Deformation of Microgels at Solid–Liquid Interfaces Visualized in Three-Dimension. _Nano Letters_ 2019, _19_ , 8862–8867, PMID: 31642321
  * Hoppe Alvarez et al. 2021 Hoppe Alvarez, L.; Rudov, A. A.; Gumerov, R. A.; Lenssen, P.; Simon, U.; Potemkin, I. I.; Wöll, D. Controlling microgel deformation via deposition method and surface functionalization of solid supports. _Phys. Chem. Chem. Phys._ 2021, _23_ , 4927–4934
  * Purohit et al. 2019 Purohit, A.; Centeno, S. P.; Wypysek, S. K.; Richtering, W.; Wöll, D. Microgel paint-nanoscopic polarity imaging of adaptive microgels without covalent labelling. _Chemical Science_ 2019, _10_ , 10336–10342
  * Schnitzbauer et al. 2017 Schnitzbauer, J.; Strauss, M. T.; Schlichthaerle, T.; Schueder, F.; Jungmann, R. Super-resolution microscopy with DNA-PAINT. 2017,
  * Conley 2017 Conley, G. M. Superresolution Microscopy of PNIPAM Microgels. Ph.D. thesis, PhD thesis, University of Fribourg, 2017, 2017
  * Gnan et al. 2017 Gnan, N.; Rovigatti, L.; Bergman, M.; Zaccarelli, E. In silico synthesis of microgel particles. _Macromolecules_ 2017, _50_ , 8777–8786
  * Ninarello et al. 2019 Ninarello, A.; Crassous, J. J.; Paloli, D.; Camerin, F.; Gnan, N.; Rovigatti, L.; Schurtenberger, P.; Zaccarelli, E. Modeling microgels with a controlled structure across the volume phase transition. _Macromolecules_ 2019, _52_ , 7584–7592
  * Conley et al. 2019 Conley, G. M.; Zhang, C.; Aebischer, P.; Harden, J. L.; Scheffold, F. Relationship between rheology and structure of interpenetrating, deforming and compressing microgels. _Nature Communications_ 2019, _10_
  * 43 Given that the error bar on the experimental $R_{g}$ is of the order of 10 nm, we choose the numerical value within the experimental interval, finding that the best agreement is at its lowest end.
  * Chremos et al. 2022 Chremos, A.; Horkay, F.; Douglas, J. F. Influence of network defects on the conformational structure of nanogel particles: From “closed compact” to “open fractal” nanogel particles. _The Journal of Chemical Physics_ 2022, _156_ , 094903
  * Del Monte et al. 2021 Del Monte, G.; Truzzolillo, D.; Camerin, F.; Ninarello, A.; Chauveau, E.; Tavagnacco, L.; Gnan, N.; Rovigatti, L.; Sennato, S.; Zaccarelli, E. Two-step deswelling in the Volume Phase Transition of thermoresponsive microgels. _Proceedings of the National Academy of Sciences_ 2021, _118_ , e2109560118
  * Elancheliyan et al. 2022 Elancheliyan, R.; Del Monte, G.; Chauveau, E.; Sennato, S.; Zaccarelli, E.; Truzzolillo, D. Role of charge content in the two-step deswelling of Poly (N-isopropylacrylamide)-based microgels. _Macromolecules_ 2022, _55_ , 7526–7539
  * Hertle and Hellweg 2013 Hertle, Y.; Hellweg, T. Thermoresponsive copolymer microgels. _Journal of Materials Chemistry B_ 2013, _1_ , 5874–5885
  * Keerl et al. 2009 Keerl, M.; Pedersen, J. S.; Richtering, W. Temperature sensitive copolymer microgels with nanophase separated structure. _Journal of the American Chemical Society_ 2009, _131_ , 3093–3097
  * Geisel et al. 2012 Geisel, K.; Isa, L.; Richtering, W. Unraveling the 3D localization and deformation of responsive microgels at oil/water interfaces: a step forward in understanding soft emulsion stabilizers. _Langmuir_ 2012, _28_ , 15770–15776
  * Camerin et al. 2019 Camerin, F.; Fernandez-Rodriguez, M. A.; Rovigatti, L.; Antonopoulou, M.-N.; Gnan, N.; Ninarello, A.; Isa, L.; Zaccarelli, E. Microgels adsorbed at liquid–liquid interfaces: A joint numerical and experimental study. _ACS nano_ 2019, _13_ , 4548–4559
  * Harrer et al. 2019 Harrer, J.; Rey, M.; Ciarella, S.; Löwen, H.; Janssen, L. M.; Vogel, N. Stimuli-responsive behavior of PNiPAm microgels under interfacial confinement. _Langmuir_ 2019, _35_ , 10512–10521
  * Bochenek et al. 2022 Bochenek, S.; Camerin, F.; Zaccarelli, E.; Maestro, A.; Schmidt, M. M.; Richtering, W.; Scotti, A. In-situ study of the impact of temperature and architecture on the interfacial structure of microgels. _Nature Communications_ 2022, _13_ , 1–12
  * Destribats et al. 2011 Destribats, M.; Lapeyre, V.; Wolfs, M.; Sellier, E.; Leal-Calderon, F.; Ravaine, V.; Schmitt, V. Soft microgels as Pickering emulsion stabilisers: role of particle deformability. _Soft Matter_ 2011, _7_ , 7689–7698
  * Scotti et al. 2022 Scotti, A.; Schulte, M. F.; Lopez, C. G.; Crassous, J. J.; Bochenek, S.; Richtering, W. How Softness Matters in Soft Nanogels and Nanogel Assemblies. _Chemical Reviews_ 2022, _122_ , 11675–11700, PMID: 35671377
  * Schulte et al. 2019 Schulte, M. F.; Scotti, A.; Brugnoni, M.; Bochenek, S.; Mourran, A.; Richtering, W. Tuning the Structure and Properties of Ultra-Low Cross-Linked Temperature-Sensitive Microgels at Interfaces via the Adsorption Pathway. _Langmuir_ 2019, _35_ , 14769–14781, PMID: 31638406
  * Karg et al. 2007 Karg, M.; Pastoriza-Santos, I.; Pérez-Juste, J.; Hellweg, T.; Liz-Marzán, L. M. Nanorod-coated PNIPAM microgels: Thermoresponsive optical properties. _Small_ 2007, _3_ , 1222–1229
  * Pergushov et al. 2021 Pergushov, D. V.; Sigolaeva, L. V.; Balabushevich, N. G.; Sharifullin, T. Z.; Noyong, M.; Richtering, W. Loading of doxorubicin into surface-attached stimuli-responsive microgels and its subsequent release under different conditions. _Polymer_ 2021, _213_
  * Chen et al. 2021 Chen, R.; Shi, J.; Liu, C.; Li, J.; Cao, S. In situ self-assembly of gold nanorods with thermal-responsive microgel for multi-synergistic remote drug delivery. _Advanced Composites and Hybrid Materials_ 2021,
  * Dave et al. 2022 Dave, R.; Randhawa, G.; Kim, D.; Simpson, M.; Hoare, T. Microgels and Nanogels for the Delivery of Poorly Water-Soluble Drugs. _Molecular Pharmaceutics_ 2022,
  * Olivier et al. 2013 Olivier, N.; Keller, D.; Gönczy, P.; Manley, S. Resolution Doubling in 3D-STORM Imaging through Improved Buffers. _PLoS ONE_ 2013, _8_
  * Kremer and Grest 1990 Kremer, K.; Grest, G. S. Dynamics of entangled linear polymer melts: A molecular-dynamics simulation. _The Journal of Chemical Physics_ 1990, _92_ , 5057–5086
  * Weeks et al. 1971 Weeks, J. D.; Chandler, D.; Andersen, H. C. Role of Repulsive Forces in Determining the Equilibrium Structure of Simple Liquids. _The Journal of Chemical Physics_ 1971, _54_ , 5237–5247
  * Warner 1972 Warner, H. R. Kinetic Theory and Rheology of Dilute Suspensions of Finitely Extendible Dumbbells. _Industrial & Engineering Chemistry Fundamentals_ 1972, _11_ , 379–387
  * Soddemann et al. 2001 Soddemann, T.; Dünweg, B.; Kremer, K. A generic computer model for amphiphilic systems. _The European Physical Journal E_ 2001, _6_ , 409–419
  * Verso et al. 2015 Verso, F. L.; Pomposo, J. A.; Colmenero, J.; Moreno, A. J. Simulation guided design of globular single-chain nanoparticles by tuning the solvent quality. _Soft Matter_ 2015, _11_ , 1369–1375
  * Plimpton 1995 Plimpton, S. Fast parallel algorithms for short-range molecular dynamics. _Journal of computational physics_ 1995, _117_ , 1–19
  * Rovigatti et al. 2015 Rovigatti, L.; Šulc, P.; Reguly, I. Z.; Romano, F. A comparison between parallelization approaches in molecular dynamics simulations on GPUs. _Journal of Computational Chemistry_ 2015, _36_ , 1–8
  * Lopez et al. 2019 Lopez, C. G.; Scotti, A.; Brugnoni, M.; Richtering, W. The Swelling of Poly (Isopropylacrylamide) Near the $\theta$ Temperature: A Comparison between Linear and Cross-Linked Chains. _Macromolecular Chemistry and Physics_ 2019, _220_ , 1800421
  * Stukowski 2009 Stukowski, A. Visualization and analysis of atomistic simulation data with OVITO–the Open Visualization Tool. _Modelling and Simulation in Materials Science and Engineering_ 2009, _18_ , 015012