# Water-methanol mixture confined in a graphene slit-pore

Roger Bellido-Peralta
National Graphene Institute
Univeristy of Manchester
M13 9PL, Manchester

Secció de Física Estadística i Interdisciplinària - Departament de Física de
la Matèria Condensada
Universitat de Barcelona
08028, Barcelona
<EMAIL_ADDRESS>
&Fabio Leoni
Department of Physics
Sapienza University of Rome
00185, Rome
<EMAIL_ADDRESS>
&Carles Calero
Secció de Física Estadística i Interdisciplinària - Departament de Física de
la Matèria Condensada
Universitat de Barcelona
08028, Barcelona

Institut de Nanociència i Nanotecnologia
Universitat de Barcelona
08028, Barcelona
<EMAIL_ADDRESS>
&Giancarlo Franzese
Secció de Física Estadística i Interdisciplinària - Departament de Física de
la Matèria Condensada
Universitat de Barcelona
08028, Barcelona

Institut de Nanociència i Nanotecnologia
Universitat de Barcelona
08028, Barcelona
<EMAIL_ADDRESS>

###### Abstract

Efficient and sustainable techniques for separating water-methanol mixtures
are in high demand in the industry. Recent studies have revealed that
membranes and 2D materials could achieve such separation. In our research, we
explore the impact of a nanoconfining graphene slit-pore on the dynamics and
structure of water-methanol mixtures. By Molecular Dynamics simulations of a
coarse-grained model for water mixtures containing up to 25% methanol, we show
that, for appropriate pore sizes, water tends to occupy the center of the
pore. In contrast, methanol’s apolar moiety accumulates near the hydrophobic
walls. Additionally, modifying the pore’s width leads to a non-monotonic
change in the diffusivity of each component. However, water always diffuses
faster than methanol, implying that it should be possible to identify an
optimal configuration for water-methanol separation based on physical
mechanisms. Our calculations indicate that one of the more effective pore
sizes, 12.5Å, is also mechanically stable, minimizing the energy cost of a
possible filtering membrane.

##  1 Introduction

Mixtures of water and methanol are widely utilized in various processes in the
chemical [1], food, and pharmaceutical industries [2], as well as in biofuel
production [3]. However, traditional methods for separating these mixtures,
such as distillation, can be inefficient and expensive. As a result,
researchers have turned their attention to membrane filtration technology as a
potential solution for more cost-effective and efficient separation [4].

Graphene and its derivative, e.g., graphene oxide, are gaining significant
interest due to their exceptional properties, including high flexibility,
stiffness, and thermal conductivity [5]. These properties make it a promising
material for filtration technology. One crucial aspect of membrane filtration
is the hydrophobicity or hydrophilicity of the material [6]. However, applying
simple processes such as chemical reduction [7], or thermal annealing [8] can
alter hydrophilic materials to become more hydrophobic while still maintaining
their excellent properties, ensuring high-quality results.

Significant progress has been made in the investigation of carbon-based
materials. Experiments show that it is possible to adjust their semi-
permeability to various elements and molecules [9] or functionalize them to
improve their selectivities [10, 11, 12]. Despite their potential
applications, these materials have limitations. For example, graphene oxide
swells when exposed to water [13]. Furthermore, scalability may only sometimes
be feasible, limiting their use in industries, and some procedures required to
obtain beneficial properties can be costly and time-consuming [14, 15].

Molecular Dynamics (MD) simulations are a promising solution to overcome these
limitations. They offer valuable insight into separating binary or multiple
component mixtures, gas mixtures, and even water purification from wastewater
at the mesoscale. Several studies, such as [16, 17, 18, 19], for example, have
shown the potential of MD simulations in this regard.

The joint efforts of many research groups in the field of MD simulations of
confined water, e.g., [20, 21, 22] to mention a few, as well as our previous
MD studies, have demonstrated that confinement at the nano-level can alter the
behavior of a mono-component fluid compared to its behavior in the bulk [23,
24, 25, 26]. Building on these results, here we consider a binary mixture to
model water-methanol, nanoconfined in a graphene slit pore and specifically
examine the effects of nano-pore size on the acceptance capacity, density
profile, diffusion coefficient, and hydration pressure.

##  2 Models and Method

Our study examines a liquid mixture embedding a slit pore consisting of two
parallel graphene walls of sizes $l_{x,gr}=49$Å, $l_{y,gr}=51$Å, and separated
by a fixed distance 6.5Å $\leq\delta\leq$ 17Å. The walls are composed of fixed
carbon atoms arranged in a graphene structure and interacting with the liquid
mixture through a 12-6 Lennard Jones (LJ) potential that aligns with the
CHARMM27 force field [26]. To minimize edge effects of the walls, we calculate
the observables for the confined mixture within a reduced region of the slit-
pore, i.e., a central subvolume $V_{s}=L_{x,s}\,L_{y,s}\,\delta$, where
$L_{x,s}=L_{y,s}=30$Å (Fig. 1).

Figure 1: Simulation snapshots for a 90%-10% CSW-methanol mixture. Left:
Simulation box (in yellow) with the graphene slit-pore in its center (pink).
Right, from top to bottom: side view of a mixture monolayer in the slit pore
with $\delta=6.5\,$Å; a bilayer for $\delta=9.5\,$Å; a three-layer for
$\delta=12.5\,$Å; a different view of the monolayer to emphasize the subvolume
$V_{s}$ (in yellow) in which we compute the observables. Beads are water-like
CSW particles (blue), methanol methyl groups (purple), and methanol hydroxyl
groups (green). Pink lines represent the graphene lattice.

We use coarse-grain models to represent the two mixture components, water and
methanol. This approach reduces the computational time required for the
calculations while reproducing consistent thermodynamic results [27]. As per
the works of Hus et al. (2014), Munao et al. (2015), and Marques et al. (2020)
[28, 29, 30], we represent the methanol using two rigidly bonded beads - one
for the apolar methyl group (CH3) and the other for the polar hydroxyl group
(OH). A 24-6 LJ potential models the interaction energy of two apolar groups
while a Continuous Shouldered Well (CSW) potential [31, 32] that of two polar
moieties. The CSW model shows some of the structural, dynamical, and
thermodynamic anomalies typical of hydrogen-bonding liquids [33, 32, 34].

To facilitate the blending of methanol with water, which forms hydrogen bonds
with it, for consistency, we also model water via the CSW. However, due to its
isotropy, the CSW cannot fully replicate the entropy behavior associated with
the hydrogen-bond network [33, 32, 34]. For instance, in a hydrophobic
graphene pore, water experiences a free energy minimum corresponding to a
confined double layer dominated by potential energy and a secondary minimum
for monolayers driven by entropy [25]. In contrast, in the CSW, both the
monolayer and the bilayer minima are dominated by entropy [26]. Nonetheless,
despite these limitations, the combined representation of water and methanol
effectively captures the primary characteristics of the mixture [27]. We will
elaborate on this further in the following discussion. The definitions and
parameters of the potentials are given in Ref. [27].

To compute the density profile along the direction $z$, perpendicular to the
walls, with the origin in the center of the pore, we divide $\delta$ into
$2000$ bins of equal size $dz$ and calculate

$\rho_{\alpha}(z)\equiv\frac{\left\langle
N_{\alpha}(z)\right\rangle}{L_{x,s}\,L_{y,s}\,dz},$ (1)

where $\left\langle\cdot\right\rangle_{\alpha}$ is the temporal average at the
equilibrium, and $\left\langle N_{\alpha}(z)\right\rangle$ is the average
number of molecules of species $\alpha$ within a slab with thickness $dz$ and
size $L_{x,s}\,L_{y,s}$ centered in $z$ along the pore. The possible species
for $\alpha$ are CSW, methanol, or both.

We estimate the mean square displacement (MSD) parallel to the walls–for each
species $\alpha$–as

$\begin{split}\Delta_{\alpha}^{2}(t)\equiv\left\langle(x(t+t_{0})-x(t_{0}))^{2}+(y(t+t_{0})-y(t_{0}))^{2}\right\rangle_{\alpha}=\\\
=N_{\alpha}(t_{0})^{-1}\sum_{i=1}^{N_{\alpha}}[(x_{i}(t+t_{0})-x_{i}(t_{0}))^{2}+(y_{i}(t+t_{0})-y_{i}(t_{0}))^{2}],\end{split}$
(2)

where the averages are calculated over the center of mass of each molecule of
the species $\alpha$, and $N_{\alpha}(t_{0})$ is the number of molecules of
that species in the volume $V_{s}$ at the time $t_{0}$.

From the large-time (diffusive) regime limit of the MSD, we calculate the
parallel diffusion coefficient

$D_{\|}(\alpha)\equiv\left[\frac{\Delta_{\alpha}^{2}(t_{l})}{4t_{l}}\right]_{t_{l}>t_{D}}.$
(3)

For all our simulations, we check that $t_{D}=2$ ps is enough to explore the
diffusive regime (Fig. S1). Factor 4 accounts for the two dimensions of the
planar diffusion, and $t_{l}$ is the lag time, i.e., the time the molecules
spend in the subvolume. Because we are interested in $D_{\|}$ within the
confined subvolume $V_{s}$, we use for the calculation only the particles that
remain within $V_{s}$ along the whole simulation.

Furthermore, we compute the acceptance capacity of the pore, defined as the
ratio between the number of molecules of the species $\alpha$ inside the
subvolume $V_{s}$ and its area

$\sigma(\alpha)\equiv\frac{\left\langle N_{\alpha}\right\rangle}{A_{s}},$ (4)

where $A_{s}\equiv L_{x,s}\,L_{y,s}$, and the average number density inside
the subvolume

$\rho(\alpha)\equiv\frac{\left\langle N_{\alpha}\right\rangle}{V_{s}}.$ (5)

Finally, we compute the hydration pressure as the difference between the
normal pressure $P_{\perp}$ exerted by the confined particles over the slit-
pore walls and the bulk pressure $P_{\rm bulk}$ [23, 35, 36]

$P_{\rm hydr}\equiv P_{\perp}-P_{\rm bulk},$ (6)

where

$P_{\perp}\equiv\frac{1}{2A}\left\langle\sum_{i}(F_{i,\,b}-F_{i,\,t})\right\rangle,$
(7)

$F_{i,\,b}$ and $F_{i,\,t}$ are the forces exerted by the particle $i$ on the
bottom and the top graphene walls, respectively, and $A$ is the area of the
walls.

We perform simulations in the canonical ensemble with a fixed total number $N$
= 25,000 of particles composing the mixture, in a total volume $V=84\times
84\times 98$ Å3 with periodic boundary conditions, at fixed temperature
$T=100$ K controlled by the Nosé–Hoover thermostat, as implemented in the
LAMMPS software [37]. The temperature is chosen to allow us to compare the
present results with the previous data for the pure CSW case [26]. Using the
Leap–Frog integration algorithm [38], a standard second-order and time-
reversible integration method, with timestep $\delta t=1$ fs, we equilibrate
the system for 1 ns ($10^{6}$ MD steps) starting from an initial configuration
with two intertwined hexagonal lattices of CSW beads and methanol dimers. We
gather 1000 datapoints along the simulation to calculate observables. To
accurately analyze the short and long times of the simulation, we use an
uneven distribution of time steps to record the particle trajectories. Further
details are presented in Ref. [27].

##  3 Results and Discussion

###  3.1 Acceptance capacity and density inside the pore.

####  3.1.1 The pure CSW case.

To analyze the current findings, we first refer to the results obtained for
the pure CSW under confinement [26]. When $\delta$ is commensurable with an
integer number of layers (solid vertical lines in Fig. 2 for the pure CSW case
[26]), $\sigma$ remains constant regardless of minor changes in $\delta$.
These specific values of $\delta$ correspond to the lowest points in the pure
CSW free energy and are mechanically stable, as the associated hydration
pressure within the slit-pore vanishes [26]. This is consistent with our
current findings, which will be further discussed in section 3.4. Although a
slight decrease in $\delta$ near a stable point does not impact $\sigma$ since
$N_{\alpha}$ remains constant, thanks to the softness of the CSW, it induces a
maximum in $\rho$ because of the change in $V_{s}$ and in the number of
layers.

Figure 2: Acceptance capacity and fluid density in the slit-pore changing
width. The acceptance capacity $\sigma$ (upper panels) and the fluid density
$\rho$ inside the pore (lower panels) for the CSW-methanol mixture (panels a,
d), for compositions 100%-0% (red), 90%-10% (blue), and 75%-25% (green), have
non-monotonic contributions for the CSW (panels b, e) and the methanol (c, f)
components as a function of the slit-pore width $\delta$. Vertical solid
(dashed) lines mark approximately local minima (maxima), within the present
numerical resolution, of the pure CSW free energy [26].

In contrast, if $\delta$ corresponds to a maximum in the CSW free energy
(dashed vertical lines in Fig. 2 for the pure CSW case [26]), the hydration
pressure is zero only at a point of mechanical instability. As $\delta$
increases around this value, the pressure quickly transitions from negative
(indicating effective attraction between walls) to positive (effective
repulsion between walls) [26]. Therefore, a slight increase of $\delta$ near
these mechanically unstable values causes a quick rise in both $\sigma$ and
$\rho$ due to the resulting increase in $N_{\alpha}$.

As the width of the slit-pore increases, the plateaus and extreme points of
$\rho$ and $\sigma$ become less pronounced due to the reduced structure of the
liquid inside the pore. This behavior continues until the density $\rho$
approaches a value similar to the overall density of the system.

####  3.1.2 The mixture case.

When both liquids are in the pore, we find that the mixture’s acceptance
capacity $\sigma$ and density $\rho$ do not change their qualitative behavior
compared with the pure CSW case (Fig. 2 a, d). However, within our numerical
precision, both quantities increase slightly upon adding methanol.

The analysis of the behavior of each component reveals that the water-like
liquid in the mixture is displaced from the inside of the pore compared to the
pure case. The CSW acceptance capacity and density between the walls decrease
significantly for all $\delta$ values (Fig. 2 b, e). Furthermore, the local
maxima and minima in $\sigma$ and $\rho$ are recovered for the same values of
$\delta$ as in the pure CSW case, albeit with less clarity.

We observe a more intriguing pattern for the methanol in the pore. When the
pore size is less than 9.0 Å, the amount of methanol inside the pore
significantly increases with $\delta$. However, above this pore-size
threshold, $\rho$ reaches a maximum for both methanol concentrations, and the
acceptance capacity levels off. The maximum in $\rho$ and the level in
$\sigma$ increase for larger methanol concentration, overcoming the values of
the CSW component and inverting their overall relative concentrations.

Finally, when $\delta$ is increased, the value of $\sigma$ for methanol
stabilizes, but the value for the CSW continues to rise. As a result, the
alcohol density inside the pore decreases gradually when the pore size exceeds
9.0 Å(Fig. 2 c, f). These results indicate that the methanol, sequestered
inside the slit pore from the bulk reservoir, has higher adsorption inside the
structure than the CSW, as the density of methanol is larger (at almost every
plate separation) than the water-like one despite representing a much lower
percentage of the mixture (see Fig. 2 e, f).

###  3.2 Density profile

To better understand the behavior of $\sigma$ and $\rho$ inside the slit pore,
we calculate the density profiles of each component at different values of
$\delta$ and compositions (Figs. 3, S2 – S7). At low methanol percentage
(90%-10% mixture), we recover the layering observed in [26] for pure CSW for
all the pore widths. The methanol concentrates near the walls, thereby
altering the density of the CSW in the same region and within the overall
pore.

The larger methanol concentration near the wall results from its stronger
interaction with the graphene atoms than the CSW. Indeed, in our model, the
methanol dumbbells interact with the walls via twice the points of the CSW
beads. This interaction creates a preferential attraction between methanol and
the graphene sheets, similar to the hydrophobic collapse expected between
carbon walls and methyl moieties.

The methanol adsorption on the walls does not have a significant impact on the
structure of the CSW in the center of the pore, where the methanol is almost
absent in all those cases (with $\delta\geq$ 10 Å) where there are at least
three CSW layers (central panels in Figs. 3, S2 – S7). This is consistent with
what is observed in atomistic simulations for water-methanol [39] and water-
ethanol mixtures [40] where the apolar CH3 groups concentrate on the walls,
leaving more space for water in the central layers. Therefore, although our
model does not mimic the amphiphilic property of methanol because it lacks
directional hydrogen bonds, it gives results consistent with detailed models.

Our analysis is also confirmed at the higher methanol concentration for the
75%-25% mixture composition. Under these conditions, the concentration of
alcohol in the pore increases, both in the central layer and near the walls,
inducing a higher decrease in the CSW density, although the number of CSW
layers does not change (right panels in Figs. 3, S2 – S7).

Figure 3: The density profiles in the slit pore show methanol segregation.
Density profiles $\rho(z)$ along the $z$ direction orthogonal to the pore
walls for large slit pores with $\delta=12.5$ Å (upper panels), 13.0 Å
(central row) and 13.5 Å (lower panels) and CSW-methanol mixture compositions
100%-0% (left panels), 90%-10% (central column) and 75%-25% (right panels).
The CSW water-like component (red) shows three layers. Instead, the methanol
$\rho(z)$ (blue) shows only two layers, with significant depletion in the
central region of the slit pore, especially for $\delta=12.5$ Å and
$\delta=13.0$ Å at 90%-10% CSW-methanol composition.

When the value of $\delta$ is less than 10 Å, the CSW can only accommodate a
maximum of two layers inside the pore along with the methanol (Figs. S2 – S4).
This means that the presence of methanol in the pore significantly lowers the
concentration of CSW, resulting in the highest density $\rho$ of methanol at
$\delta\simeq$ 8.5 Å, as seen in Fig.2. As $\delta$ further decreases, steric
hindrance causes a reduction in the methanol $\sigma$ within the pore,
lowering its $\rho$.

In particular, at $\delta\simeq 7.5$ Å, the density difference between the two
components within the slit pore favors methanol the most (Fig. 2). This
corresponds to the pore size that exhibits the first maximum in the CSW free
energy [26], which means that the CSW density inside the pore is also at its
lowest in the pure case (Fig. 2). Thus, methanol can easily replace CSW in the
pore under these circumstances. However, maintaining the pore size at this
distance would require significant energy, as indicated by the maximum free
energy.

On the other hand, when the size is $\delta=12.5$ Å, the separation between
the two components is significant (Fig. 3), and the pore free energy is at a
minimum (Fig. 2) [26]. Therefore, the methanol segregation can be attained at
this pore size in mechanically stable conditions with minimal energy cost.

Under these circumstances, a concentration gradient in the pore could prompt a
lamellar flow, enabling the recovery of both components separately. Moreover,
even without a concentration gradient, the segregation effect could lead to a
significant difference in the diffusion of the mixture’s components. We will
assess this prospect in the following section.

###  3.3 Confined diffusion coefficient

We observe that the overall value of $D_{\|}$ decreases for each mixture
component within the slit pore as $\delta$ is reduced (Fig. 4). This can be
attributed to the fact that stronger confinement results in a reduced volume
for diffusion. In the case of pure CSW [26], $D_{\|}$ displays oscillations
with minima for those values of $\delta$ where the liquid reaches maximum
density and maxima where the density is minimum (solid and dashed vertical
thick lines in Fig. 4, respectively).

The extrema in $D_{\|}$ for the mixture’s components are less evident and
suggest an interplay between the two liquids. For instance, CSW’s $D_{\|}$ in
the 90%-10% mixture decreases at $\delta\simeq 8.5$ Å (Fig. 4) where
methanol’s $\rho$ is maximum (Fig. 2). This behavior could be a consequence of
how the density of methanol impacts the diffusion of CSW in the mixture,
specifically when their layers overlap ($\delta<10$ Å, Fig. S3, S4). This
effect, however, is no longer evident for $\delta>$15 Å, where the diffusion
of CSW in the mixture becomes comparable to that of the pure case.

We generally observe that the CSW is faster than the methanol within the pore.
This could be due to the ability of the CSW soft-core potential to adapt
quickly to pore widths that do not match with the components layers (dashed
vertical thick lines for the pure CSW in Fig. 4), as, for example, at
$\delta\simeq 7.5$ Å (Fig. S2). The partial overlaps of the soft cores of
different molecules can lead to multiple maxima (and minima) in $D_{\|}$ at
values of $\delta$ higher compared to the pure CSW case (Fig. 4).

Also, as the methanol concentration increases in the mixture, the thermal
diffusion of the water-like liquid becomes slower (Fig.4). This phenomenon can
be attributed to the steric effect of the voluminous methanol molecule, which
restricts the availability of free volume for the diffusion of CSW. As a
result, the value of CSW’s $D_{\|}$ diminishes.

Figure 4: Diffusion coefficient parallel to the slit pore walls for confined
CSW-methanol mixtures. The longitudinal $D_{\|}$ is non-monotonic with the
pore width $\delta$. For pure CSW (red symbols and lines), it has maxima and
minima near the free energy maxima and minima (marked by dashed and solid
vertical thick lines, as in Fig. 2), respectively. For both 90%-10% (left
panel) and 75%-25% (right panel) CSW-methanol mixtures, the water-like
component (blue or green solid circles) is faster than the methanol (blue or
green open circles), especially for large values of $\delta$. However, the
partial mismatch of diffusion extrema with the pure case suggests, within our
numerical errors, a complex dynamic interplay between the two components.

The extra-volume mechanism that facilitates thermal diffusion, due to the soft
core, has a limited effect on methanol. This is because only one of its
moieties interacts with the CSW potential. As a result, the methanol’s maximum
diffusion coefficient values are smaller than those of the water-like liquid.
This observation is consistent with the fact that increasing the methanol
concentration in the mixture or decreasing $\delta$ diminishes the soft-core
facilitating effect, leading to a slowdown in the overall mixture (Fig. 4).

Furthermore, methanol experiences a more significant slowing down than the CSW
component, which can be attributed to its stronger interaction with the walls.
Methanol particles interact with the walls twice as much as CSW particles,
leading to more effective hydrophobic attraction.

Therefore, a) the slowing down of methanol due to hydrophobic attraction and
b) the facilitation effect of soft polar interaction combine to induce 1) CSW
depletion from inside the pore for small $\delta$ values, 2) segregation
between mixture components at higher $\delta$ values, and 3) generally faster
longitudinal diffusion of the water-like liquid. These effects are promising
prerequisites for efficient physical separation of the two components.

###  3.4 Hydration Pressure

Figure 5: The mixture’s hydration pressure oscillates between positive and
negative values depending on the slit-pore width. Only a few values of
$\delta$ correspond to mechanically stable (i.e., with $P_{\rm hydr}=0$ and
$\partial P_{\rm hydr}/\partial\delta<0$, solid vertical thick lines) or
mechanically unstable (i.e., with $P_{\rm hydr}=0$ and $\partial P_{\rm
hydr}/\partial\delta>0$, dashed vertical thick lines) equilibrium widths. In
between these values, external energy would be needed to keep $\delta$ fixed
against the effective (mixture-mediated) wall-wall attraction ($P_{\rm
hydr}<0$) or repulsion ($P_{\rm hydr}>0$). The range of variation of $P_{\rm
hydr}$ is larger for the 75%-25% CSW-methanol mixture (green symbols and
lines) than for the 90%-10% mixture (blue), while the pure CSW case (red) is
approximately intermediate. Vertical dashed and solid thick lines are as in
Fig. 2.

Next, we investigate the relationship between the hydration pressure $P_{\rm
hydr}$ and $\delta$ to determine the most energy-efficient pore size for
separation. Indeed, $P_{\rm hydr}$ reflects the force required to maintain a
specific distance $\delta$ between the walls of the slit pore. By integrating
$P_{\rm hydr}$ along the $z$-axis, we can evaluate the amount of energy
required to maintain $\delta$ fixed under the influence of external factors,
such as an induced mixture flux.

Like water [20, 25, 41] and pure CSW liquid [26], the CSW-methanol mixture can
either attract or repel the confining walls depending on the pore width
$\delta$ (Fig. 5). If $P_{\rm hydr}>0$, the confined mixture’s internal
pressure on the pore walls is greater than the bulk pressure in Eq.(6), and an
external force is required to balance the internal pressure to keep $\delta$
constant and prevent the walls from moving far apart. Conversely, if $P_{\rm
hydr}<0$, the bulk pressure is greater than the internal pressure, and an
external force is necessary to avoid the collapse of the slit-pore.

As discussed for pure fluids, the force imbalance results from the system’s
changes in free energy. The stable equilibrium conditions ($P_{\rm hydr}=0$
and $\partial P_{\rm hydr}/\partial\delta<0$) correspond to the minima in free
energy. In contrast, the unstable equilibrium conditions ($P_{\rm hydr}=0$ and
$\partial P_{\rm hydr}/\partial\delta>0$) represent the maxima [25, 26].

An example that helps to understand this concept is when $P_{\rm hydr}=0$
under two conditions: at $\delta\approx 7.5$ Å and $\delta\approx 9.5$ Å. Our
simulations show that two liquid layers form between these two values of
$\delta$. If we decrease $\delta$ from $\delta\approx 7.5$ Å, the pressure
becomes negative, and the slit-pore would collapse. Conversely, if we increase
the width of the slit, $P_{\rm hydr}>0$ and the walls move apart. A similar
but opposite situation occurs at $\delta\approx 9.5$ Å. If we decrease the
width, the mixture applies $P_{\rm hydr}>0$ to restore the width to the
equilibrium value. Conversely, if we increase $\delta$, $P_{\rm hydr}<0$ and
the walls return to their original $\delta$ value.

Here we find that the free-energy minima conditions for the mixtures are
consistent, within the error bars, with those for pure CSW. Additionally, we
observe that the maxima have a weak dependence on the percentage of methanol
component (Fig. 5). Specifically, we find that the slit-pore is in stable
equilibrium when $\delta\simeq 9.5$ Å, 12.5 Å and 16 Å. These widths
correspond to 1) a confined mixture bilayer with well-mixed CSW and methanol
(Fig. S3 and 1), 2) three confined layers where CSW and methanol are
segregated (Fig. 3 and 1), and 3) four CSW layers with characteristics similar
to the three-layer case.

Furthermore, in the case of three and four-layers of mixture, the water-like
liquid has a thermal diffusion constant approximately 20% to 50% greater than
methanol. However, in bilayer configurations, this difference diminishes, and
it almost disappears when $\delta$ is decreased, with methanol concentration
changes in the mixture having only a minor impact (Fig. 4).

Therefore, based on our findings, we identify two potential configurations
that can be used for effective segregation processes. The first, at high
energy cost, involves the mechanical instability surrounding $\delta\simeq
7.5$ Å, where there is a significant difference in the amount of CSW water and
methanol present within the pore. The second configuration is around
$\delta\simeq 12.5$ Å, which offers ideal segregation and diffusion conditions
for mechanically separating the water-like liquid from methanol in an energy-
efficient way.

##  4 Summary and conclusions

The separation of water and methanol is essential in various industrial
processes, such as extracting methanol for biofuels [42, 43]. However,
conventional techniques have their limitations regarding efficiency and costs
[44]. Thus, it is crucial to seek alternative methods that are both
technologically feasible and scientifically compelling. This is particularly
relevant for achieving the UN Sustainable Development Goals (SDGs) of “Clean
Water and Sanitation" and “Affordable and Clean Energy”.

Here, we aim to explore the potential of separating a water-methanol mixture
by confining it in a graphene slit pore using Molecular Dynamics. We perform
simulations of mixtures with varying compositions in slit pores of different
widths. We use the CSW water-like liquid [31] and the dumbbell methanol model
[28] to coarse-grain the mixture to ensure simulation efficiency while
retaining the correct thermodynamic behavior of the mixture [27]. Both models
have been previously tested in the literature [33, 29] and have successfully
replicated mixture properties [30, 39].

Under appropriate conditions, we observe that the two liquids a) segregate and
b) diffuse at different rates along the pore in a slit geometry. Due to its
strong hydrophobic attraction with graphene, it is noteworthy that methanol
tends to concentrate inside the slit pore. The interplay between 1) layering,
2) slowing down caused by the interaction of graphene and methanol, and 3)
facilitation of diffusion due to soft polar interactions create optimal
conditions for confining the mixture, leading to enhanced separation.

In particular, our findings suggest that separating water and methanol in a
graphene pore is most effective when the pore width contains at least three
mixture layers, equivalent to a width of approximately 12.5 Å. At this pore
size, three factors contribute to an energetically-favorable mixture
separation. I) The components segregate, concentrating the methanol near the
walls and the water in the pore center. This setup allows efficient water
extraction from the mixture through proper filtering geometry. II) The
methanol is about 20% slower than water in the concentration range of 10% to
25%, further aiding in the separation process. III) The pore remains
mechanically stable, minimizing the energetic costs against external forces
such as fluxes applied to the mixture. These results suggest the potential of
exploring optimal pore parameters for the physical filtration and purification
of water-alcohol mixtures through nanotechnology.

Funding
This research was funded by MCIN/AEI/ 10.13039/ 501100011033 and “ERDF A way
of making Europe" grant number PGC2018-099277-B-C22 and PID2021-124297NB-C31.

Acknowledgments
G.F. acknowledges the Visitor Program of the Max Planck Institute for The
Physics of Complex Systems for supporting a six-month visit that started on
November 2022.
R. Bellido-Peralta would like to acknowledge the assistance given by Research
IT and the use of the Computational Shared Facility at The University of
Manchester.

## References

  * [1] N. Miganakallu, Z. Yang, R. Rogóż, Ł. J. Kapusta, C. Christensen, S. Barros, and J. Naber, “Effect of water - methanol blends on engine performance at borderline knock conditions in gasoline direct injection engines,” Applied Energy, vol. 264, p. 114750, 2020.
  * [2] G. Shu, Y. Tan, L. Cui, Y. Zhang, and L. Zhang, “Effect of mixed solvents containing ethylene glycol and various salts on the vapor–liquid equilibrium of water + methanol + ethanol,” J. Chem. Eng. Data, vol. 65, no. 6, pp. 3029–3036, 2020.
  * [3] S. Masoumi and A. K. Dalai, “Techno-economic and life cycle analysis of biofuel production via hydrothermal liquefaction of microalgae in a methanol-water system and catalytic hydrotreatment using hydrochar as a catalyst support,” Biomass and Bioenergy, vol. 151, p. 106168, 2021.
  * [4] A. Mohammad, Y. Teow, W. Ang, Y. Chung, D. Oatley-Radcliffe, and N. Hilal, “Nanofiltration membranes review: Recent advances and future prospects,” Desalination, vol. 356, pp. 226–254, 2015.  State-of-the-Art Reviews in Desalination.
  * [5] D. G. Papageorgiou, I. A. Kinloch, and R. J. Young, “Mechanical properties of graphene and graphene-based nanocomposites,” Progress in Materials Science, vol. 90, pp. 75–127, 2017.
  * [6] Y. Kopel and N. Giovambattista, “Comparative study of water-mediated interactions between hydrophilic and hydrophobic nanoscale surfaces,” The Journal of Physical Chemistry B, vol. 123, pp. 10814–10824, 12 2019.
  * [7] C. K. Chua and M. Pumera, “Chemical reduction of graphene oxide: a synthetic chemistry viewpoint,” Chem. Soc. Rev., vol. 43, no. 1, pp. 291–312, 2014\.
  * [8] P. V. Kumar, N. M. Bardhan, S. Tongay, J. Wu, A. M. Belcher, and J. C. Grossman, “Scalable enhancement of graphene oxide properties by thermally driven phase transformation,” Nature Chemistry, vol. 6, no. 2, pp. 151–158, 2013.
  * [9] R. R. Nair, H. A. Wu, P. N. Jayaram, I. V. Grigorieva, and A. K. Geim, “Unimpeded permeation of water through helium-leak–tight graphene-based membranes,” Science, vol. 335, pp. 442–444, 01 2012.
  * [10] X. Chen, S. Mohammed, G. Yang, T. Qian, Y. Chen, H. Ma, Z. Xie, X. Zhang, G. P. Simon, and H. Wang, “Selective permeation of water through angstrom-channel graphene membranes for bioethanol concentration,” Advanced Materials, vol. 32, no. 33, p. 2002320, 2020.
  * [11] A. Wang, W. Chen, H. Xu, Z. Xie, X. Zheng, M. Liu, Y. Wang, N. Geng, X. Mu, and M. Ding, “Heterostructured MoS2 quantum dot/GO lamellar membrane with improved transport efficiency for organic solvents inspired by the namib desert beetle,” Journal of Membrane Science, vol. 650, p. 120402, 2022\.
  * [12] R. Castro-Muñoz, J. Buera-González, Ó. de la Iglesia, F. Galiano, V. Fíla, M. Malankowska, C. Rubio, A. Figoli, C. Téllez, and J. Coronas, “Towards the dehydration of ethanol using pervaporation cross-linked poly(vinyl alcohol)/graphene oxide membranes,” Journal of Membrane Science, vol. 582, pp. 423–434, 2019.
  * [13] G. Huang, B. Ghalei, A. Pournaghshband Isfahani, H. E. Karahan, D. Terada, D. Qin, C. Li, M. Tsujimoto, D. Yamaguchi, K. Sugimoto, R. Igarashi, B. K. Chang, T. Li, M. Shirakawa, and E. Sivaniah, “Overcoming humidity-induced swelling of graphene oxide-based hydrogen membranes using charge-compensating nanodiamonds,” Nature Energy, vol. 6, no. 12, pp. 1176 – 1187, 2021.  Cited by: 17.
  * [14] A. T. Smith, A. M. LaChance, S. Zeng, B. Liu, and L. Sun, “Synthesis, properties, and applications of graphene oxide/reduced graphene oxide and their nanocomposites,” Nano Materials Science, vol. 1, no. 1, pp. 31–47, 2019.
  * [15] J. Jang, Y. T. Nam, D. Kim, Y.-J. Kim, D. W. Kim, and H.-T. Jung, “Turbostratic nanoporous carbon sheet membrane for ultrafast and selective nanofiltration in viscous green solvents,” Journal of Materials Chemistry A, vol. 8, no. 17, pp. 8292–8299, 2020.
  * [16] S. Gravelle, H. Yoshida, L. Joly, C. Ybert, and L. Bocquet, “Carbon membranes for efficient water-ethanol separation,” The Journal of Chemical Physics, vol. 145, no. 12, p. 124708, 2016.
  * [17] B. M. Yoo, J. E. Shin, H. D. Lee, and H. B. Park, “Graphene and graphene oxide membranes for gas separation applications,” Current Opinion in Chemical Engineering, vol. 16, pp. 39–47, 2017.  Nanotechnology / Separation Engineering.
  * [18] D. Mangindaan, K. Khoiruddin, and I. Wenten, “Beverage dealcoholization processes: Past, present, and future,” Trends in Food Science and Technology, vol. 71, pp. 36–45, 2018.
  * [19] K. Zuo, K. Wang, R. M. DuChanois, Q. Fang, E. M. Deemer, X. Huang, R. Xin, I. A. Said, Z. He, Y. Feng, W. Shane Walker, J. Lou, M. Elimelech, X. Huang, and Q. Li, “Selective membranes in water and wastewater treatment: Role of advanced materials,” Materials Today, vol. 50, pp. 516–532, 2021\.
  * [20] J. Engstler and N. Giovambattista, “Comparative study of the effects of temperature and pressure on the water-mediated interactions between apolar nanoscale solutes,” The Journal of Physical Chemistry B, vol. 123, pp. 1116–1128, 02 2019.
  * [21] H. R. Corti, G. A. Appignanesi, M. C. Barbosa, J. R. Bordin, C. Calero, G. Camisasca, M. D. Elola, G. Franzese, P. Gallo, A. Hassanali, K. Huang, D. Laria, C. A. Menéndez, J. M. M. de Oca, M. P. Longinotti, J. Rodriguez, M. Rovere, D. Scherlis, and I. Szleifer, “Structure and dynamics of nanoconfined water and aqueous solutions,” The European Physical Journal E, vol. 44, no. 11, p. 136, 2021.
  * [22] B. H. S. Mendonça, E. E. de Moraes, R. J. C. Batista, A. B. de Oliveira, M. C. Barbosa, and H. Chacham, “Water diffusion in carbon nanotubes for rigid and flexible models,” The Journal of Physical Chemistry C, vol. 127, pp. 9769–9778, 05 2023.
  * [23] F. Leoni and G. Franzese, “Structural behavior and dynamics of an anomalous fluid between attractive and repulsive walls: Templating, molding, and superdiffusion,” The Journal of Chemical Physics, vol. 141, no. 17, p. 174501, 2014.
  * [24] F. Leoni and G. Franzese, “Effects of confinement between attractive and repulsive walls on the thermodynamics of an anomalous fluid,” Physical Review E, vol. 94, pp. 062604–, 12 2016.
  * [25] C. Calero and G. Franzese, “Water under extreme confinement in graphene: Oscillatory dynamics, structure, and hydration pressure explained as a function of the confinement width,” Journal of Molecular Liquids, vol. 317, p. 114027, 2020.
  * [26] F. Leoni, C. Calero, and G. Franzese, “Nanoconfined fluids: Uniqueness of water compared to other liquids,” ACS Nano, vol. 15, pp. 19864–19876, 12 2021.
  * [27] R. Bellido-Peralta, F. Leoni, C. Calero, and G. Franzese, “Size-pore-dependent methanol sequestration from water-methanol mixtures by an embedded graphene slit,” Molecules, vol. 28, no. 9, 2023.
  * [28] M. Huš and T. Urbic, “Existence of a liquid-liquid phase transition in methanol,” Physical Review E, vol. 90, p. 062306, 12 2014.
  * [29] G. Munaò and T. Urbic, “Structure and thermodynamics of core-softened models for alcohols,” The Journal of Chemical Physics, vol. 142, no. 21, p. 214508, 2015.
  * [30] M. S. Marques, V. F. Hernandes, E. Lomba, and J. Bordin, “Competing interactions near the liquid-liquid phase transition of core-softened water/methanol mixtures,” Journal of Molecular Liquids, vol. 320, p. 114420, 2020.
  * [31] G. Franzese, “Differences between discontinuous and continuous soft-core attractive potentials: The appearance of density anomaly,” Journal of Molecular Liquids, vol. 136, pp. 267–273, 12 2007.
  * [32] P. Vilaseca and G. Franzese, “Softness dependence of the anomalies for the continuous shouldered well potential,” The Journal of Chemical Physics, vol. 133, no. 8, p. 084507, 2010.
  * [33] A. B. de Oliveira, G. Franzese, P. A. Netz, and M. C. Barbosa, “Waterlike hierarchy of anomalies in a continuous spherical shouldered potential,” The Journal of Chemical Physics, vol. 128, no. 6, p. 064901, 2008.
  * [34] P. Vilaseca and G. Franzese, “Isotropic soft-core potentials with two characteristic length scales and anomalous behaviour,” Journal of Non-Crystalline Solids, vol. 357, no. 2, pp. 419 – 426, 2011.  6th International Discussion Meeting on Relaxation in Complex Systems.
  * [35] B. D. Todd, D. J. Evans, and P. J. Daivis, “Pressure tensor for inhomogeneous fluids,” Physical Review E, vol. 52, no. 2, pp. 1627–1638, 1995.
  * [36] F. Varnik, J. Baschnagel, and K. Binder, “Molecular dynamics results on the pressure tensor of polymer films,” The Journal of Chemical Physics, vol. 113, no. 10, pp. 4444–4453, 2000.
  * [37] A. P. Thompson, H. M. Aktulga, R. Berger, D. S. Bolintineanu, W. M. Brown, P. S. Crozier, P. J. in ’t Veld, A. Kohlmeyer, S. G. Moore, T. D. Nguyen, R. Shan, M. J. Stevens, J. Tranchida, C. Trott, and S. J. Plimpton, “LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales,” Comp. Phys. Comm., vol. 271, p. 108171, 2022.
  * [38] C. Birdsall and A. Langdon, Plasma Physics via Computer Simulation.  CRC Press, 1991.
  * [39] P. Pršlja, E. Lomba, P. Gómez-Álvarez, T. Urbič, and E. G. Noya, “Adsorption of water, methanol, and their mixtures in slit graphite pores,” The Journal of Chemical Physics, vol. 150, p. 024705, 2019/01/23 2019.
  * [40] F. Mozaffari and M. Zeraatgar, “Molecular dynamics simulation of nanoconfined ethanol–water mixtures,” Industrial & Engineering Chemistry Research, vol. 58, pp. 12854–12867, 07 2019.
  * [41] J. Engstler and N. Giovambattista, “Temperature effects on water-mediated interactions at the nanoscale,” The Journal of Physical Chemistry B, vol. 122, pp. 8908–8920, 09 2018.
  * [42] X. Ren, P. Zelenay, S. Thomas, J. Davey, and S. Gottesfeld, “Recent advances in direct methanol fuel cells at los alamos national laboratory,” Journal of Power Sources, vol. 86, no. 1-2, pp. 111–116, 2000.
  * [43] D. A. Boysen, T. Uda, C. R. I. Chisholm, and S. M. Haile, “High-performance solid acid fuel cells through humidity stabilization,” Science, vol. 303, no. 5654, pp. 68–70, 2004.
  * [44] K. Liang, W. Li, H. Luo, M. Xia, and C. Xu, “Energy-efficient extractive distillation process by combining preconcentration column and entrainer recovery column,” Ind. Eng. Chem. Res., vol. 53, no. 17, pp. 7121–7131, 2014.

Supplementary Material

Figure S1: MSD for confined CSW-methanol mixtures. For each slit pore width
$\delta$ (7.5Å, red lines; 9.0Å, blue; 12.0Å, green), both the CSW (upper
panels) and the methanol (lower panels) reach the diffusive regime (power law
with exponent 1, dashed lines) at any composition (pure, left panel; 90%-10%,
central panels; 75%-25%, right panels) for $t>t_{D}=2$ ps. Figure S2: Density
profiles in the slit pore with at most one methanol layer. As in Fig. 3, but
for $\delta=6.5$Å (upper panels), 7.0Å (central panels), and 7.5Å (lower
panels). The CSW $\rho(z)$(red) increases from one to two at $\delta\simeq
7.5$Å, while the methanol $\rho(z)$ (blue) always has one layer, although
broadened for the larger width. Figure S3: Density profiles in the slit pore
with two layers of CSW and two of methanol. As in Fig. S2, but for
$\delta=8.0$Å (upper panels), 8.5Å (central panels), and 9.0Å (lower panels).
Figure S4: Density profiles in the slit pore with an incipient third CSW layer
in the center. As in Fig. S2, but for $\delta=9.5$Å (upper panels), 10.0Å
(central panels), and 10.5Å (lower panels). Figure S5: Density profiles in the
slit pore with a third CSW layer in the center. As in Fig. S2, but for
$\delta=11.0$Å (upper panels), 11.5Å (central panels), and 12.0Å (lower
panels). Figure S6: Density profiles in the slit pore with a third CSW layer
in the center. As in Fig. S2, but for $\delta=14.0$Å (upper panels), 14.5Å
(central panels), and 15.0Å (lower panels). Figure S7: Density profiles in the
slit pore with a third CSW layer in the center. As in Fig. S2, but for
$\delta=15.5$Å (upper panels), 16.0Å (central panels), and 16.5Å (lower
panels). Figure S8: Density profiles in the slit pore with a third CSW layer
in the center. As in Fig. S2, but for $\delta=17.0$Å.


#  BioSpark: An End-to-End Generative System for Biological-Analogical
Inspirations and Ideation

Hyeonsu B. Kang†, David Chuan-En Lin†, Nikolas Martelaro†, Aniket Kittur†
Yan-Ying Chen‡, Matthew K. Hong‡
†Human-Computer Interaction Institute, Carnegie Mellon University, Pittsburgh,
PA 15213
‡Toyota Research Institute, Los Altos, CA 94022
<EMAIL_ADDRESS>‡{yan-
<EMAIL_ADDRESS>
###### Abstract

Nature is often used to inspire solutions for complex engineering problems,
but achieving its full potential is challenging due to difficulties in
discovering relevant analogies and synthesizing from them. Here, we present an
end-to-end system, BioSpark, that generates biological-analogical mechanisms
and provides an interactive interface to comprehend and synthesize from them.
BioSpark pipeline starts with a small seed set of mechanisms and expands it
using an iteratively constructed taxonomic hierarchies, overcoming data
sparsity in manual expert curation and limited conceptual diversity in
automated analogy generation via LLMs. The interface helps designers with
recognizing and understanding relevant analogs to design problems using four
main interaction features. We evaluate the biological-analogical mechanism
generation pipeline and showcase the value of BioSpark through case studies.
We end with discussion and implications for future work in this area.

Figure 1: BioSpark consists of a _Dataset Generation Pipeline_ and an
_Organization and Interaction_ interface. Dataset construction starts from a
small set of seed mechanisms on AskNature (Step 1), expanded by iteratively
constructing taxonomic hierarchies of organisms and diversifying using
_breadth_ \- or _depth_ -focused knowledge prompts (Step 2). Dataset is
organized by semantic clusters (Step 3) and provided with interactive features
that facilitate user learning and synthesis (Step 4).

##  1 Introduction

Nature provides a valuable source of inspirations [3, 37, 32] for novel design
solutions to challenging engineering problems [34, 32]. A central cognitive
mechanism underpinning this transfer of domains is analogical processing [12,
29, 16], which involves discovering and recognizing deep structural similarity
between source and target analogs. To this end, recent work has shown that
analogs with a similar purpose (i.e., ‘ _what problem it solves_ ’), but
diverse mechanisms (i.e., ‘ _how it solves it_ ’), facilitate creative new
ideas [23, 20].

Yet, attaining the full potential of biological-analogical inspirations in
engineering and design domains has proven difficult. Many bio-inspired designs
are biased toward well-known organisms [4] and prior work has shown it can
take on average three person-hours to sift through vast, fragmented
information sources such as Google Scholar, Wikipedia, AskNature.org, or the
open Web to find a single relevant article for a design problem [36, 35].
Human-expert-curated databases such as AskNature [9], DANE [18], SAPPhIRE [6],
BioTRIZ [37], or rule-based databases (e.g., [7]) can streamline searches, but
the effort-intensive process of manual expert curation often results in data
scarcity, limiting the diversity of organisms and mechanisms available for
making analogies. Automatic generation of analogies with LLMs could create
larger datasets for bio-inspired design, but they still have limited diversity
on abstract concepts, even when tuning parameters to increase diversity such
as logic suppression and increased sampling temperature [8]. Knowledge-
augmented prompting provides an alternative design space to token-level
manipulation, as explored in [2] for improving the factuality of Q&A tasks. We
explore this design space for a different purpose of constructing a diverse
biological mechanisms dataset, with a modular and structured knowledge
augmentation approach.

Beyond sourcing biological analogies, it can be challenging for people to
understand and recognize them when presented. People’s cognitive biases
towards surface similarity and working memory limitations can impede the
mapping process between biological and engineering systems [15, 31, 13], while
the need for situating found (cross-domain) analogies within the constraints
and scope of the original design problem adds additional complexity for
designers developing their own ideas (cf.  [22, 17]). To mitigate these
challenges, we construct a scalable analogy pipeline that balances the cost of
expert-curation and the limitations of naïve LLM-based generation, and develop
an interactive system that aids designers in recognizing and understanding the
relevance and adaptability of analogs for a chosen design problem.

##  2 Usage Scenario

Consider an automobile designer who is working on a new bike rack design for
sedans. The designer identifies the core challenge of designing a secure bike
rack as turbulence management, caused by variable wind resistance due to
changing vehicle speeds and additional factors from inclement weather
conditions, such as precipitation. When the designer clicks on the ‘Manage
Turbulence’ problem as a query (Fig. 4, top right), BioSpark displays
mechanism clusters related to the problem. She quickly identifies particularly
interesting and idea-provoking mechanisms such as “Adapative shape-shifting
mechanism in intertidal microalgae” and “Friction-based attachment mechanism
of parasitic copecods,” two quite different mechanisms, yet commonly observed
in organisms that live underwater. When exposed to turbulence cues, shape-
shifting microalgae adaptively reconfigure their surface shapes to reduce
resistance from strong currents. This provides her a design idea for
adaptively “lying down” bike racks on the roof of the car that reduce air
drag, and a “tail basement” bike rack attachment to the back of the car that
joins the rotational axis of the front wheel of the bikes and adjusts during
turbulence by flexibly (counter-)swinging bikes, mimicking bird tail motions.
She clicks on the ‘Combine’ tab with the two mechanisms selected to experiment
with design ideas. The result describes an approach that simply combines the
friction-based mechanism to the shape-shifting mechanism. This gives her an
additional idea for further securing the racks, by incorporating designs
inspired from the adhesion mechanism of parasitic copecods, such as
microridges and channels for increasing friction upon contact.

##  3 System Description

Figure 2: BioSpark interface and a subset of available interaction
affordances.

###  3.1 Structuring AskNature blog posts’ text into a seed set of problem-
mechanism schemas

To source a set of diverse, high-quality biological mechanisms for a given
problem, BioSpark starts from a seed set of expert-curated biological
mechanisms on AskNature (Fig. 1, Step 1). AskNature.org provides a curated
list of organisms with detailed descriptions of their unique strategies to
functional problems (e.g., ‘Manage Impact’, ‘Modify Speed’). The organisms and
strategies can be grouped by function and viewed as a list. To curate a seed
set of high-quality mechanisms, we first choose a functional problem $p$
predicted to be highly relevant to automobile designers, excluding irrelevant
functions such as ‘Adapt Behaviors’, ‘Adapt Genotype’, ‘Coevolve’, ‘Maintain
Community’ We access the sub-list of organisms $o\in O$ and strategies posted
to $p$ on AskNature’s group-by-function page by parsing the HTML code using
the BeautifulSoup package on Python. We then access the blog post for each
organism-strategy page using the parsed URL and parse the returned HTML page
to get the title, description, and references (if available).

At this stage, the returned unstructured text is yet to contain a succinct
mechanism description. Furthermore, we found that some blog posts do not
contain any body text despite having a title and are accessible via the URL.
Some of these missing blog posts indicated that they are in maintenance and/or
planned to be updated. To structure the raw blog post text
$\text{AskNature}_{(o,p)}$, we prompt GPT4 [28] to succinctly describe (i.e.,
using 12 words or less) the core mechanism (i.e., excluding the qualities or
effects, and focusing on mechanisms with engineering design implications),
given $(o,p)$ (if blog post text is missing) or
$(o,p,\text{AskNature}_{(o,p)})$. The returned mechanism description $m$ along
with the function description makes up the problem-mechanism schema for each
organism: $\\{o\in O|(p,m,o)\\}$.

###  3.2 Iteratively constructing taxonomic trees with organisms represented
in found mechanisms and performing structured expansion of a tree

Using each schema as a seed, we iteratively prompt GPT4 to find relevant
mechanisms for the given mechanism and problem, using an even mixture of
breadth- and depth-focused expansion strategies (Fig. 1, Step 2). To enable
structured diversification of organisms and their mechanisms beyond prior work
that relied on token-level manipulation or naïvely prompting LLMs, we guide
LLMs _how_ and _where_ to expand by leveraging organism taxonomic hierarchies.
At each iteration of expansion (Fig. 1, Step 2), we aggregate the organisms
represented in found mechanisms up to that point, and construct a taxonomic
tree featuring seven levels of hierarchy on Tree of Life: {domain, kingdom,
phylum, class, order, family, genus, species}, where domain representing the
highest level and species representing the lowest level on the hierarchy.

Given this tree, we aim to identify sparsely populated branches for expansion.
We cut the tree at a given reference expansion level (e.g., class), and sort
the taxonomic ranks (nodes) on that level by the number of its immediate
children nodes111alternatively, the entire size of the subtree, rather than
immediate children, could be used for sorting, in an increasing order. For
performance, we batch 10 prompts to send to GPT4 for expansion. For half of
the prompts, we instruct breadth-first expansion which asks GPT4 to first
identify _sibling_ nodes at the given reference taxon level and existing nodes
(up to 50 most populated nodes). For example, the prompt asks “come up with a
few biological classes not in {...names of excluded classes...}”. The breadth-
first expansion prompt then instructs GPT4 to repeat the following: 1) Choose
one taxon from the list it came up with; 2) Succinctly describe (i.e., using
14 words or less) new mechanisms $m$ related to a problem $p$. For the rest of
the prompts, we instruct depth-first expansion which asks GPT4 to first
identify a new _children_ node at the given reference taxon level and existing
children nodes (up to 50 randomly sampled children). For example, the prompt
asks “come up with a few biological families in order araneae that are not any
of {araneidae, …}”. The depth-first expansion prompt then instructs GPT4 to
repeat a similar procedure as breath-first expansion. In the prototype system,
we run 10 batches for expansion to construct dataset of mechanisms for each
problem.

The returned list of mechanisms and organisms text are then fed into the
second GPT4 prompt for structuring them into a list of {mechanism, organism}
dictionaries. Finally, using each organism name, we prompt GPT3.5-turbo to
retrieve the seven-level taxonomic hierarchy, based on our model evaluation
result showing its high accuracy (§4.1).

###  3.3 Semantic Clustering of Mechanisms

The mechanism descriptions are clustered in semantically related groups for
designers’ efficient viewing (Fig. 1, Step 3). We first embed the mechanism
descriptions for a given problem into high-dimensional vectors using the text-
embedding-ada-002 end-point of the OpenAI API. We group the embeddings into
$k=20$ clusters using k-Means clustering. Each group label is generated by
chunking the mechanism descriptions in each cluster into lower-cased words,
excluding stopwords. We count the frequency of each word and take the top 5
most frequent words as a description of each cluster.

###  3.4 Representative Mechanism Image Curation

To aid designers’ visual understanding of and pique curiosity for biological-
analogical mechanisms, we retrieve representative images for corresponding
textual mechanism descriptions. We use Google Custom
Search222https://developers.google.com/custom-search/v1/overview with queries
as “[organism name]:[mechanism description]” and the file type set to images
and the safe search mode enabled. We choose the first place result of Custom
Search as the visual representation of each mechanism.

###  3.5 Interacting with Mechanism Inspirations: Explain, Compare, Combine,
and Critique

To facilitate designers’ understanding and synthesis of mechanism
inspirations, we develop several interaction features available on the
interface (Fig. 2). The Explain button is located in tooltips that pop up when
the user places the mouse over on a mechanism card in the board UI (Fig. 2,
first panel). When the user clicks on the button, BioSpark sends a prompt to
GPT4 requesting elaboration of the interacted mechanism and the organism in
the context of the chosen engineering design problem. The Compare tab is
located in the control bar of the sidebar of the interface. To use this, users
need to first click on (at least) two mechanism cards from the left, saving
them to the ‘saved inspirations’ panel at the top of the sidebar. There, users
can check any two of the saved mechanisms they wish to compare. BioSpark sends
a prompt to GPT4 when the user clicks on the tab, requesting comparison of
pros and cons between the two mechanisms in the context of the chosen
engineering problem. The result is formatted into a markdown table, with each
mechanism as the header followed by pros and cons rows detailing each point.
The Combine tab is also located in the control bar of the sidebar in the
interface. Similarly with Compare, users can check two saved mechanisms they
wish to see combined. BioSpark sends a prompt to GPT4 then requesting
elaboration of a mechanism that combines the two selected mechanisms, and
explain its potential advantages in the context of the chosen engineering
problem. The result is also formatted into a markdown page using section title
and headers for demarcating the content. Finally, the Critique button is
located inside the Ideate tab. Upon clicking the Ideate tab, users can type in
their own idea in the rich text editor in the opened tab, and optionally click
on the button below to receive constructive feedback on it. BioSpark sends a
prompt to GPT4 with the content of the text editor describing the idea, and
requests additional revision that may improve the quality, such as anticipated
failure modes and potential improvements.

##  4 Evaluation

###  4.1 Accuracy of LLM-based Taxonomy Construction

The main process in our diversification strategy is iterative construction of
taxonomic trees at each stage of expansion with a set of problem-mechanism
schemas and corresponding organisms $\\{o\in O|(p,m,o)\\}$ curated (in case of
AskNature seeds) or generated up to that point. To construct the trees, the
taxonomic hierarchy of each organism needs to be known. Here, we restrict our
tree construction to seven levels of depth, ranging from the highest to lowest
levels: domain, kingdom, phylum, class, order, family, genus, species. These
levels provide considerable branch-switching opportunities for
diversification, through significant changes in the number of members between
levels and within each level of the hierarchy. For example, while the highest
level domain consists of three members, Bacteria, Archaea, and Eukarya, there
are estimated 8.7M species in the world [33]. The next level on the hierarchy,
Genus, has an estimated number of 310K members [30], while the number in the
subsequent level, families, is estimated at 8K [26] in 2011. The number of
known species for each node on the hierarchy also changes considerably,
further contributing to the diversification opportunities. For example while
most non-avian reptile genera have only 1 species each, insect genera such as
Lasioglossum and Andrena have over 1,000 species each, while the flowering
plant genus, Astragalus, contains over 3,000 known species [38].

Our initial exploration of suitable approaches to retrieve organism taxonomies
involved using available resources such as the Global Biodiversity Information
Facility API333https://www.gbif.org/developer/species, Catalogue of Life [5],
or the Encyclopedia of Life [11], where canonical species names were retrieved
from the Darwin Core List of Terms444https://dwc.tdwg.org/list/#dwc_Organism
for corresponding organisms in problem-mechanism schemas. However, the limited
coverage, data consistency, and API availability of these tools prevented
their adoption. On the other hand, Wikipedia provides scientific
classification for some of the organism articles (for example in the Pomelo
article555https://en.wikipedia.org/wiki/Pomelo, taxonomic names for Kingdom,
Clade, Order, Family, Genus, and Species are available in the ‘biota’
information box that appears on the right-hand side of the page). However,
this data was not readily available for scalable generation.

####  4.1.1 Procedure

LLMs may provide an alternative solution to the limitations of existing
approaches for retrieving the taxonomic hierarchy for a given organism name.
To test this idea, we curated 90 gold taxonomies using Wikipedia that have
complete information in the ‘biota’ scientific classification info box. For
each organism, we prompted LLMs with each organism name zero-shot using the
chat completions API endpoint666https://api.openai.com/v1/chat/completions
using each model key (see Appendix A.1 for the prompt used). Once the
hierarchy data is generated, we lower-cased the rank names for consistency.
The complete list of organism names used for generation can be found in
Appendix A.2.

Model |  Domain |  Kingdom |  Phylum |  Class |  Order |  Family |  Genus
---|---|---|---|---|---|---|---
GPT4 |  100% (90/90) |  100% (90/90) |  100% (90/90) |  100% (90/90) |  96.7% (87/90) |  94.4% (85/90) |  98.9% (89/90)
GPT3.5-turbo |  100% (90/90) |  100% (90/90) |  100% (90/90) |  100% (90/90) |  95.6% (86/90) |  95.6% (86/90) |  93.3% (84/90)
Table 1: The accuracy of zero-shot taxonomy generation using only the organism
name.

####  4.1.2 GPT4’s Accuracy

We find that GPT4’s zero-shot taxonomy generation accuracy to range between
94.4% and 100% (Table 1). The lowest accuracy was observed in the family
taxonomy, followed by order (96.7%) and genus (98.9%).

####  4.1.3 System Optimization: GPT3.5-turbo’s Accuracy

We find that GPT3.5-turbo has comparable accuracy levels with GPT4 in zero-
shot taxonomy generation. The highest misaglignment occurred in genus, with a
6.67% error rate (equivalent to 6 out of 90). Appendix A.3 provides a further
qualitative error analysis of models’ comparative performance. Based on these
results, we opted for the more efficient GPT3.5-turbo model in our pipeline.
We leave further exploration of the capabilities of smaller, fine-tuned base
LLMs, with implications for LLM cascade777LLM cascade refers to a system
design approach that adaptively chooses optimal LLM APIs for a given query.
Smaller, task-specific LLMs are regarded as optimal when they exhibit higher
or similar levels of performance compared to models that are orders of
magnitude larger [10], with all else equal., to future work.

###  4.2 Increase in Organism Diversity

In order to evaluate the effectiveness of diversification through our
expansion strategies from iteratively constructed taxonomic trees, we
investigated how organism diversity changes upon a series of mechanism
generation.

Figure 3: Organism diversity, measured by the number of unique names among the
generation, increases monotonically as generation continues, while the number
of mechanisms generated per organism also increases, as evidenced by the
decreasing slope. This suggests mechanism concentration over each organism
increases over time.

####  4.2.1 Procedure

We generate mechanisms and corresponding species for five problems closely
related to automobile design: ‘managing impact’, ‘managing tension’, ‘managing
compression’, ‘managing turbulence’, ‘modifying speed’. We index the species
at the time of its appearance in the corresponding mechanism generation.
Hence, the index corresponds to when a new mechanism was generated via our
pipeline. At each generation index, we count the unique number of names that
are generated up to that point, for each taxonomic rank, and average the
numbers across the five problems.

####  4.2.2 The pattern of increasing organism diversity

Qualitatively we observe that the number of species generated are
monotonically increasing (Fig. 3), albeit at a decreasing rate. The ratio
between the number of unique species and the generation index is 2:1 at
index=200, and approaches 4:1 near index=800. This suggests more mechanisms
are generated and become concentrated on individual species (e.g., for
grasshoppers, there might be several distinct mechanisms relevant to the
problem of ‘managing turbulence’ such as their foldable wing structures,
lightweight exoskeleton designs, or joints in their legs enabling repeated
high jumps) on average as generation continues. In our future work, we will
explore whether and how the mechanisms generated for the same species
semantically differ from one another. We also plan to examine how our
generation approach compares to other baseline approaches in terms of the
efficiency of generating mechanisms across diverse organisms, by measuring the
slope of organism diversity over generation index.

###  4.3 Case Studies

####  4.3.1 ‘Design a secure bike rack for sedans’

Figure 4: BioSpark interface featuring biological mechanisms for the query
design challenge ‘Manage Turbulence’. User-chosen mechanisms are: “Adapative
shape-shifting mechanism in intertidal microalgae” and “Friction-based
attachment mechanism of parasitic copecods.” A new combination idea is shown
in the ‘Combine’ tab, facilitating further synthesis and ideation of a new
idea.

Fig. 4 shows user interactions based on the motivating usage scenario
described in §2. An accompanying demonstration video can also be accessed at:
https://drive.google.com/file/d/1qD1rJXsE8g1NimLnd2quF_ofR6KFXdrh/view?usp=drive_link.

####  4.3.2 ‘Design robust collision resistance for racing cars’

The details of this case study can be found in Appendix §B.

##  5 Discussion and Future Work

###  5.1 Closing the ‘Gulf of Geometric Transfer’ with combinatoric visual
mechanism generation

Figure 5: When the designer arrives at the new insight of designing a tail-
like, shape-shifting bike rack attachment (Appendix 4.3.1), she is combining
two source mechanisms and transferring the new idea into a new geometry in the
target domain (‘at the _tail_ of a sedan’) that could suit the combined
mechanism. The successful generation of this combination comes after a series
of failed attempts to generate geometries, which we call the Gulf of Geometric
Transfer.

The designer in Case Study 1 (Appendix 4.3.1) arrives at a successful transfer
of mechanisms naturally after several unsuccessful iterations of generation. A
key process in transferring a new idea into the target domain involves
visualizing a candidate geometry, such as in this case ‘a bike rack attachment
to the _tail_ of a sedan’. The process of identifying an effective geometry
for transfer like ‘at the tail of a sedan’ can resemble a combinatoric search,
where the designer visualizes the location of integration for the new idea in
the target object, from a possible source object and its geometry, such as
‘the tail of a bird’. The process of evaluating an effective geometry for
transfer may further involve analogical processing, for instance, by comparing
the integrated geometry to familiar objects and judging the aesthetic and
functional value of such a geometry.

We hypothesize that one challenge of this transfer process is what we term
‘Gulf of Geometric Transfer’ (see Fig. 5, right; the concepts related to this
namesake in design and usability studies are the Gulf of Execution and the
Gulf of Evaluation [27]), which refers to the failed iterations that one has
to endure before obtaining a successful geometric insight that supports the
new idea. To bridge this gulf, we envision a future work area that aids
designers in visualizing what successful geometries that facilitate transfer
of ideas might look like, and also helps them eliminate any blind spots in the
process that could hinder new insights. A fruitful starting point to this end
may involve generating visual representations of geometries that are related
to the new abstract idea from various key orientations, such as {up, down,
front, rear, left, right, internal, external}. Enumerating these geometries
visually could assist designers in effectively reviewing possible geometries
and facilitating their imagination for alternative geometries that may be
useful for further consideration.

###  5.2 Supporting Generative ‘Frame Slips’

The geometric transfer discussed in the previous section can be understood as
one form of a more generalized phenomenon in analogical transfer, where a
conceptual frame ‘slips’ into a different one, thereby generating novel
insight. For example, Niels Bohr transferred the concept of planets orbiting
the sun to the domain of atomic structure to propose a new model in which
electrons orbit the nucleus in a similar manner [13]. This new structure of
the atom significantly differed from the dominant ‘plum-pudding’ model of the
time, leading to a _re-structuring_ of the concept [14]. Here, _re-
structuring_ is related to conceptual frames that successfully ‘slip’ into a
new, analogous problem domain, causing structural changes in the conceptaul
framework of the receiving domain or vice versa. In the case studies, the
concept of the ‘tail of a car’ slipped into geometrically and syntactically
similar ‘tail of a bird’, engendering a new insight. Similarly while studying
how crowds generate design concepts for children’s chairs, Yu et al. [39]
found that the concept of a playground ‘slide’ slipped into another
semantically related playground concept ‘swing set’, which then gave a novel
design idea of swing chairs.

Understanding the process of ‘Frame Slipping’ is therefore crucial for
improving analogical transfer. Existing examples of ‘frame slips’ imply that
such transitions can occur through different relational paths, for example,
syntactic, semantic, functional, abstracted commonality, geometric, or other
means. While geometric transfer may impose a more concrete form of
transformation onto the transfer process, other means of transfer may be less
understood, possibly involving more complex multimodal processes that combine
multiple relational paths at once. Nonetheless, significant value of an
inspiration often results from enabling frame slips to new parts of the design
space, opening up not only a single point of an idea, but an entire space of
ideas for future exploration.

###  5.3 Supporting any problem queries by incorporating rich problem-
mechanism relations

One of BioSpark’s limitations is its fixed problem queries. Though the five
pre-generated problem queries provide a useful entry to mechanism organisms
that may be applicable to a diverse set of design challenges, it comes at the
cost of an inability to query biological mechanisms for _any_ engineering
design problems described in natural language text. As designers and engineers
progress in interacting with the system, they may naturally come up with
follow-up queries that may differ from the source queries, that could
emphasize important constraints around the design problem, or specify low-
level details newly understood to be important to consider. Adaptation to such
evolving user query intent requires further personalization and scaffolding in
the workflow. In future work, mixed-initiative workflows may leverage user
interaction traces as input to LLM operations (cf.  [24, 25]) to augment query
input and automatically search data to retrieve analogical results.

In order to enable search by free-text problem queries, the underlying data
model needs to be extended to contain multiple problem-mechanism relations
beyond the single problem present in the schema $\\{\forall
i|(p_{i},m_{i},o_{i})\\}$, and into an enriched dataset with mappings between
problems $p_{1},p_{2},\cdots,p_{n}\in P$ and an applicable mechanism $m_{k}$,
as commonly the case in engineering (e.g., ‘spider silk’ can be used for
multiple engineering challenges such as replacing steel bars in concrete or
wound suture and prosthesis [19]).

One way to expand the rich problem-mechanism relations in a scalable manner is
to prompt LLMs to come up possible engineering design problems that a given
biological mechanism could be applied to. Here, naïve prompting may suffer
from conceptual redundancy, analogous to the challenge of curating diverse
mechanisms, that limits the diversity in mechanism-to-problem mappings.
Another approach may be to intelligently use the existing dataset $\\{\forall
i|(p_{i},m_{i},o_{i})\\}$ to identify similar mechanisms $m_{i}$ in
$(p_{i},m_{i},o_{i})$ and $m_{j}$ in $(p_{j},m_{j},o_{j})$ that can be mapped
onto disparate problems: $m_{i}\sim m_{j}\rightarrow p_{i},p_{j}$. This
approach however assumes the presennce of many correlated such mechanisms with
disparate problem pairs in the dataset, which need empirical examination for
support. Once the enriched dataset $\\{\forall i|m_{i}\rightarrow S(m_{i})\\}$
(Here, $S(m_{i})$ denotes the set of engineering design problems that $m_{i}$
is applicable to) is made available, one simple approach for allowing querying
on any problem text is to construct a similarity search index (e.g., the HNSW
index of FAISS [21]) using a chosen text embedding approach.

###  5.4 Supporting improved visual representation of biological mechanisms

Future work may focus on promising methodologies for improved visual
representation of biological mechanisms. Augmentative approaches to the simple
retrieval approach described in the current prototype include ‘Retrieve and
Filter’ and ‘Highlight Species Subparts’. The ‘Retrieve and Filter’ method
involves a process of purging non-representative and low-quality images and
ranking the remaining ones based on their semantic distance to the mechanism
text, resulting in images with higher semantic coherence with the
corresponding mechanism description. On the other hand, the ‘Highlight Species
Subparts’ method will involve sourcing for representative species
illustrations or photographs, and highlighting components corresponding to
mechanisms via a translucent overlay. This method can be applied when previous
method fails to yield high-quality images.

While ‘Highlight Species Subparts’ presents potential, we also anticipate
certain limitations with it. For example, specific mechanisms could only apply
to a tiny animal area, rendering a whole body image inappropriate, such as in
cases of the adhesive paws of geckos or the hierarchical layers in pomelo
peels. Additionally, in certain cases, embedding textual labels within a
diagrammatic representation, as opposed to using realistic species images,
could provide more beneficial insights, annotating key components rather than
merely a visual highlight. Understanding the trade-off between these
strategies could be beneficial.

Alternatively, generative AI could unlock new approaches to generate
representative mechanism images on demand. Here, a systematic method such as
focusing on cases where retrieval-based images fail could prove effective. A
practical approach would involve creating a mechanism taxonomy potentially in
tabular format showcasing a mechanism’s text, its type, and specific example
images that unsuccessful retrieval-based approaches attempted to represent.
This could offer valuable insights into the areas and reasons why our
retrieval-based imaging models are not successful, thereby guiding the further
development of generatively-focused models.

##  6 Conclusion

In this work we present BioSpark, an end-to-end system for generating a
biological-analogical mechanisms dataset and an interactive interface that
facilitates learning new biological mechanisms for a design challenge, and
synthesizing new solution ideas inspired by analogical mechanisms.

##  Appendix A Taxonomy Generation

###  A.1 Prompt Used for Taxonomy Generation

The prompt used for taxonomy generation for LLMs can be found in Fig. 6.

⬇

[System Message]

You are an expert biologist who knows species and their taxonomic hierarchy
very well. Follow the instructions to the letter.

- Return the scientific term for each taxonomic rank the species belongs to.

- Enclose keys and values using double quotes ("...") and format them into a Python dictionary.

- Use the taxonomic ranks as keys and corresponding scientific terms as their values.

- Do not add any other text.

⬇

[User Message]

What {"domain", "kingdom", "phylum", "class", "order", "family", "genus"} does
"{organism}" belong to? Format your reply into a Python dictionary.

Figure 6: The prompt used to generate the taxonomy of each organism.

###  A.2 Complete List of Organisms Used for Taxonomy Generation

{‘spider monkey’, ‘prairie dog’, ‘garden tiger moth’, ‘african sacred ibis’,
‘argiope argentata’, ‘ostrich’, ‘groundhog’, ‘danio rerio’, ‘gannet’, ‘deer’,
‘cattle’, ‘glyptodon’, ‘alligator snapping turtle’, ‘leopard’, ‘arctic ground
squirrel’, ‘cormorants and shags’, ‘bears’, ‘squirrels’, ‘herons’, ‘european
badger’, ‘golden silk orb-weaver’, ‘aardvark’, ‘seahorses’, ‘banner-tailed
kangaroo rat’, ‘hyenas’, ‘pink fairy armadillo’, ‘giant otter’, ‘bighorn
sheep’, ‘hippopotamus’, ‘california ground squirrel’, ‘european bee-eater’,
‘beech marten’, ‘leopard gecko’, ‘tailorbird’, ‘testudinidae’, ‘emperor
penguin’, ‘northern pike’, ‘giant clam’, ‘stoat’, ‘horse’, ‘nutria’, ‘tree-
kangaroo’, ‘giraffe’, ‘guinea baboon’, ‘ferret’, ‘bonytail chub’, ‘baya
weaver’, ‘brook trout’, ‘pelican’, ‘mallard’, ‘roseate spoonbill’, ‘mountain
weasel’, ‘pocket gophers’, ‘lybia edmondsoni’, ‘giant anteater’, ‘common
raccoon dog’, ‘dewdrop spiders’, ‘armadillo girdled lizard’, ‘arctic fox’,
‘bison’, ‘swordfish’, ‘bald eagle’, ‘chimpanzee’, ‘asbolus verrucosus’, ‘sperm
whale’, ‘abalone’, ‘golden jackal’, ‘hornet’, ‘zebra’, ‘orangutans’,
‘peregrine falcon’, ‘atlantic cod’, ‘burrowing owl’, ‘african wild dog’,
‘maned wolf’, ‘honey bee’, ‘naked mole-rat’, ‘echidnas’, ‘bowerbirds’,
‘rhinoceros’, ‘beaver’, ‘bombyx mori’, ‘common box turtle’, ‘hummingbird’,
‘domestic sheep’, ‘wolverine’, ‘raccoon’, ‘evergreen bagworm’, ‘pig’,
‘muskrat’}

###  A.3 Error analysis

We find that some error cases in taxonomy generation could be attributed to
recent changes in classification in the literature. For example, both GPT4 and
GPT3.5-turbo models classified naked mole-rats as then literature-accepted
‘Bathyergidae’ for their family, same as other African mole-rats. However,
more recently naked mole-rats were placed in a separate family,
Heterocephalidae [1].

Among the error cases overlapping between the two models, we found cases that
either the GPT3.5-turbo or the GPT4 model wins over the other (e.g., for
‘hummingbird’, GPT3.5-turbo generated ‘archilochus’ as its genus whereas GPT4
generated ‘various’; for ‘boxer crab’, GPT3.5-turbo generated ‘hymenoptera’
which is an order of insects, whereas GPT4 generated ‘decapoda’, which is the
correct order). In other cases, both models outputted similarly incorrect
answers, for example for ‘sea snail’, GPT3.5-turbo generated ‘neogastropoda’
whereas GPT4 generated ‘archaeogastropoda’ (the Wikipedia gold answer was
‘lepetellida’).

##  Appendix B Additional Case Study: ‘Design robust collision resistance for
racing cars’

Figure 7: BioSpark displays biological mechanisms for the query design
challenge ‘Manage Compression’. User-chosen mechanisms are: “Interlocking
barbules for structural integrity during flight” and “Pressure management in
segmented skeletons.”

For another designer working on improving collision resistance for racing
cars, the core challenge might be the management of compression during a
collision. When the designer clicks on the ‘Manage Collision’ problem as a
query (Fig. 7, top right), BioSpark once again displays biological mechanism
clusters related to the problem. He finds “Interlocking barbules for
structural integrity during flight” and “Pressure management in segmented
skeletons,” interesting mechanisms employing different approaches to
compression management (the former commonly found in feathers, the latter
among isopods). He selects the ‘Combine’ tab to obtain design ideas that could
benefit from both mechanisms. The result describes an approach that applies
interlocking structures to exoskeleton segments, providing him with insight
into a ‘sliding-lock’ mechanism for modular panels of the racing car body.
This mechanism allows for extensive compression upon collision, thus
increasing impact time (subsequently decreasing peak impact force) and more
effectively dissipating force to non-critical body components. A video
demonstrating user interaction can be also accessed at:
https://drive.google.com/file/d/1F2z1AX2aTMlpAzNX9kyePuMebFsB0-Wx/view?usp=sharing.

## Acknowledgments and Disclosure of Funding

This research was supported by the Toyota Research Institute.

## References

  * [1] Naked mole-rat.  https://en.wikipedia.org/wiki/Naked_mole-rat.  Accessed: 10-02-2023.
  * [2] Jinheon Baek, Alham Aji, and Amir Saffari.  Knowledge-augmented language model prompting for zero-shot knowledge graph question answering.  In Proceedings of the First Workshop on Matching From Unstructured and Structured Data (MATCHING 2023), pages 70–98, Toronto, ON, Canada, July 2023. Association for Computational Linguistics.
  * [3] Janine M Benyus.  Biomimicry: Innovation inspired by nature.  Morrow New York, 1997.
  * [4] Chris Broeckhoven and Anton du Plessis.  Escaping the labyrinth of bioinspiration: biodiversity as key to successful product innovation.  Advanced Functional Materials, 32(18):2110235, 2022.
  * [5] O. Bánki, Y. Roskov, M. Döring, G. Ower, D. R. Hernández Robles, C. A. Plata Corredor, T. Stjernegaard Jeppesen, A. Örn, L. Vandepitte, D. Hobern, P. Schalk, R. E. DeWalt, K. Ma, J. Miller, T. Orrell, R. Aalbu, J. Abbott, R. Adlard, E. M. Adriaenssens, and et al.  Catalogue of life checklist.  Catalogue of Life, September 2023.
  * [6] Amaresh Chakrabarti, Prabir Sarkar, B Leelavathamma, and BS Nataraju.  A functional representation for aiding biomimetic and artificial inspiration of new ideas.  Ai Edam, 19(2):113–132, 2005.
  * [7] Hyunmin Cheong and LH Shu.  Retrieving causally related functions from natural-language text for biomimetic design.  Journal of Mechanical Design, 136(8):081008, 2014.
  * [8] John Chung, Ece Kamar, and Saleema Amershi.  Increasing diversity while maintaining accuracy: Text data generation with large language models and human interventions.  In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 575–593, Toronto, Canada, July 2023. Association for Computational Linguistics.
  * [9] Jon-Michael Deldin and Megan Schuknecht.  The asknature database: enabling solutions in biomimetic design.  In Biologically inspired design: computational methods and tools, pages 17–27. Springer, 2013.
  * [10] David Dohan, Winnie Xu, Aitor Lewkowycz, Jacob Austin, David Bieber, Raphael Gontijo Lopes, Yuhuai Wu, Henryk Michalewski, Rif A Saurous, Jascha Sohl-Dickstein, et al.  Language model cascades.  arXiv preprint arXiv:2207.10342, 2022.
  * [11] Encyclopedia.  Encyclopedia of life (eol).  https://eol.org/docs/what-is-eol.  Accessed: 10-02-2023.
  * [12] D. Gentner, S. Brem, R. W. Ferguson, P. Wolff, A. B. Markman, and K. D. Forbus.  Analogy and Creativity in the Works of Johannes Kepler.  In T. B. Ward, J. Vaid, and S. M. Smith, editors, Creative thought: An investigation of conceptual structures and processes, pages 403–459. American Psychological Association, Washington D.C., 1997.
  * [13] Dedre Gentner.  Structure-mapping: A theoretical framework for analogy.  Cognitive science, 7(2):155–170, 1983.
  * [14] Dedre Gentner.  Analogy in scientific discovery: The case of johannes kepler.  In Model-based reasoning: Science, technology, values, pages 21–39. Springer, 2002.
  * [15] Dedre Gentner and Russell Landers.  Analogical reminding: A good match is hard to find.  In Unknown Host Publication Title, pages 607–613. IEEE, 1985.
  * [16] Mary L Gick and Keith J Holyoak.  Analogical problem solving.  Cognitive psychology, 12(3):306–355, 1980.
  * [17] Karni Gilon, Joel Chan, Felicia Y. Ng, Hila Liifshitz-Assaf, Aniket Kittur, and Dafna Shahaf.  Analogy mining for specific design needs.  In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, CHI ’18, page 1–11, New York, NY, USA, 2018. Association for Computing Machinery.
  * [18] Ashok K Goel, Swaroop Vattam, Bryan Wiltgen, and Michael Helms.  Cognitive, collaborative, conceptual and creative—four characteristics of the next generation of knowledge-based cad systems: A study in biologically inspired design.  Computer-Aided Design, 44(10):879–900, 2012.
  * [19] Yunqing Gu, Lingzhi Yu, Jiegang Mou, Denghao Wu, Peijian Zhou, and Maosen Xu.  Mechanical properties and application analysis of spider silk bionic material.  e-Polymers, 20(1):443–457, 2020.
  * [20] Tom Hope, Joel Chan, Aniket Kittur, and Dafna Shahaf.  Accelerating innovation through analogy mining.  In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’17, pages 235–243, New York, NY, USA, 2017. ACM.
  * [21] Jeff Johnson, Matthijs Douze, and Hervé Jégou.  Billion-scale similarity search with gpus.  IEEE Transactions on Big Data, 7(3):535–547, 2019.
  * [22] Hyeonsu B. Kang*, Sheshera Mysore*, Kevin J Huang*, Haw-Shiuan Chang, Thorben Prein, Andrew McCallum, Aniket Kittur, and Elsa. Olivetti.  Augmenting Scientific Creativity with Retrieval across Knowledge Domains.  In Second Workshop on Bridging Human-Computer Interaction and Natural Language Processing at NAACL, 2022.
  * [23] Hyeonsu B. Kang, Xin Qian, Tom Hope, Dafna Shahaf, Joel Chan, and Aniket Kittur.  Augmenting scientific creativity with an analogical search engine.  ACM Trans. Comput.-Hum. Interact., 29(6), nov 2022.
  * [24] Hyeonsu B. Kang, Sherry Tongshuang Wu, Joseph C. Chang, and Aniket. Kittur.  Synergi: A Mixed-Initiative System for Scholarly Synthesis and Sensemaking.  In The 35th Annual ACM Symposium on User Interface Software and Technology (UIST), 2023.
  * [25] Michael Xieyang Liu, Tongshuang Wu, Tianying Chen, Franklin Mingzhe Li, Aniket Kittur, and Brad A Myers.  Selenite: Scaffolding decision making with comprehensive overviews elicited from large language models.  arXiv preprint arXiv:2310.02161, 2023.
  * [26] Camilo Mora, Derek P Tittensor, Sina Adl, Alastair GB Simpson, and Boris Worm.  How many species are there on earth and in the ocean?  PLoS biology, 9(8):e1001127, 2011.
  * [27] Donald A. Norman and Stephen W. Draper.  User Centered System Design; New Perspectives on Human-Computer Interaction.  L. Erlbaum Associates Inc., USA, 1986.
  * [28] OpenAI.  Gpt-4 technical report, 2023.
  * [29] R. Oppenheimer.  Analogy in science.  American Psychologist, 11(3):127–135, 1956.
  * [30] Tony Rees, Leen Vandepitte, Bart Vanhoorne, and Wim Decock.  All genera of the world: an overview and estimates based on the march 2020 release of the interim register of marine and nonmarine genera (irmng).  Megataxa, 1(2):123–140, 2020.
  * [31] Brian H Ross.  This is like that: The use of earlier problems and the separation of similarity effects.  Journal of Experimental Psychology: Learning, Memory, and Cognition, 13(4):629, 1987.
  * [32] LH Shu, K Ueda, I Chiu, and H Cheong.  Biologically inspired design.  CIRP annals, 60(2):673–693, 2011.
  * [33] L. Sweetlove.  Number of species on earth tagged at 8.7 million.  Nature, August 2011.
  * [34] Dennis Vandevenne, Thomas Pieters, and Joost R Duflou.  Enhancing novelty with knowledge-based support for biologically-inspired design.  Design Studies, 46:152–173, 2016.
  * [35] Swaroop Vattam and Ashok Goel.  Seeking bioinspiration online: a descriptive account.  In DS 75-7: Proceedings of the 19th International Conference on Engineering Design (ICED13), Design for Harmonies, Vol. 7: Human Behaviour in Design, Seoul, Korea, 19-22.08. 2013, 2013.
  * [36] Swaroop S Vattam and Ashok K Goel.  Foraging for inspiration: understanding and supporting the online information seeking practices of biologically inspired designers.  In International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, volume 54860, pages 177–186, 2011.
  * [37] Julian FV Vincent and Darrell L Mann.  Systematic technology transfer from biology to engineering.  Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, 360(1791):159–173, 2002.
  * [38] Wikipedia.  Genus.  https://en.wikipedia.org/wiki/Genus#cite_note-10.  Accessed: 10-02-2023.
  * [39] Lixiu Yu, Robert E. Kraut, and Aniket Kittur.  Distributed analogical idea generation with multiple constraints.  In Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing, CSCW ’16, page 1236–1245, New York, NY, USA, 2016. Association for Computing Machinery.