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Design strategies for shape memory polymersXiaofan Luo* and Patrick T Mather
Shape memory polymers (SMPs) are polymeric materials
capable of recovering from a ‘fixed’ temporary shape to a
‘memorized’ permanent shape upon exposure to an external
stimulus. Two structural elements are required for a polymer to
exhibit useful shape memory: a network structure that defines
the permanent shape (the ‘memory’), and a switching segment
that induces a significant change in the mobility of the network
chains. Four common strategies based on various chemical/
physical principles and with different advantages/
disadvantages have been established for the design and
preparation of SMPs. A new design strategy, based on the
concept of functional composite materials, allows for a greater
control over material properties and functions and has shown
great promise in designing SMPs for a wide variety of
applications.
Address
Department of Biomedical and Chemical Engineering, Syracuse
Biomaterials Institute, Syracuse University, Syracuse, NY 13244, USA
Corresponding author: Mather, Patrick T ([email protected])* Current address: Flow Polymers, LLC, 12819 Coit Rd, Cleveland, OH
44108, USA.
Current Opinion in Chemical Engineering 2013, 2:103–111
This review comes from a themed issue on Materials engineering
Edited by Thein Kyu
For a complete overview see the Issue and the Editorial
Available online 24th November 2012
2211-3398/$ – see front matter, # 2012 Elsevier Ltd. All rights
reserved.
http://dx.doi.org/10.1016/j.coche.2012.10.006
IntroductionShape memory polymers, or SMPs, are polymeric materials
that are capable of recovering from a ‘fixed’ temporary
shape to a ‘memorized’ permanent shape in a controlled
manner upon exposure to an external stimulus. Although
heat remains the ‘intrinsic’ stimulus, triggering shape
memory using other stimuli such as light, electric current,
magnetic field, and moisture, has also become possible.
The field of SMPs has been the subject of significant
research efforts in the past several years owing to increasing
amount of technological interests in this important class of
functional polymers. Many applications, ranging from
smart textiles, actuators, to medical devices have been
developed and new shape memory phenomena discovered
that have greatly extended both the fundamental under-
standing as well as the overall scope of SMPs. A number of
articles [1��,2,3��,4–7,8�,9–11,12�,13–17,18�,19,20] have
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appeared that provide excellent reviews of the progress
in SMPs spanning a range of emphases including
material compositions [1��,3��,7,9,11,12�], applications
[2,4,8�,10,13,14,16,17,19,20], new shape memory effects
[8�,18�], stimulus methods [5,9,17], and future challenges
[6].
The behavior of most SMPs can be described using the so
called ‘one-way shape memory cycle (1W-SMC)’.
Although some variations exist, a 1W-SMC consists
essentially of 4 steps (Figure 1). First (step 1), the
SMP is deformed at a temperature higher than its charac-
teristic transition temperature, Ttrans. The deformation is
elastic in nature and mainly leads to a reduction in
conformational entropy of the constituent network
chains. After cooling to a low temperature (step 2, below
Ttrans) while holding the deformation stress, the material
enters a more rigid state via either vitrification (rubber–glass transition) or crystallization, which leads to a sig-
nificantly reduced chain mobility that ‘freezes’ the
material in this entropically unfavored state. At the
macroscopic level the material ‘fixes’ the deformed,
temporary shape even after (step 3) the release of external
stress. Heating is finally applied (step 4) to the material
under a stress-free condition. By allowing the network
chains (with regained mobility) to return to their entro-
pically favored state, the material recovers from the
temporary to its permanent shape. It is noted here that,
although the result in Figure 1 was obtained from macro-
scopic (tensile) deformations, the same shape memory
phenomenon has also been demonstrated and increas-
ingly applied in microscopic, even nanometer-scale sys-
tems [21–26].
Structurally, two elements are required for a polymer to
exhibit shape memory:(a) a transition that induces a
significant change in the mobility of polymer chains
(usually manifested as a change in modulus), and (b)
an entropic ‘memory’ of its permanent shape, usually
through a permanent or semi-permanent network struc-
ture. As mentioned earlier, the most commonly used
transitions for SMPs are the glass–rubber transition and
the crystallization-melting transition. On the other hand,
the requisite network structure can be realized by either
covalent or non-covalent/physical crosslinks. Therefore
depending on the combination of transition type and
nature of the network, SMPs can be divided into four
main classes: (I) covalently crosslinked glassy thermosets,
(II) covalently crosslinked semi-crystalline networks,
(III) physically crosslinked glassy copolymers and blends,
and (IV) physically crosslinked semi-crystalline block
copolymers and blends [3��].
Current Opinion in Chemical Engineering 2013, 2:103–111
104 Materials engineering
Figure 1
0
20
40
60
80
100
0.00
0.05
0.10
0.15
0.200.25
10 20 30 40 50 60 70 80 90
Stre
ss (M
Pa)
Temperature (°C)
Cycle 1Cycle 2Cycle 3
*
(1)
(2)(3)
(4)
Str
ain
(%
)
Current Opinion in Chemical Engineering
Shape memory cycles (SMCs) for a Sylgard/PCL shape memory
elastomeric composite (SMEC, see text for more details of this material).
The material was thermally conditioned [72�,85] before testing. Three
consecutive cycles were performed and shown. The asterisk indicates
experimental start of a cycle.
One may realize from the discussion that these two
required structural elements exist widely in almost all
polymers. For example, all polymers possess at least one
thermal transition, the glass–rubber transition (semi-crys-
talline polymers have the additional melting transition).
Many polymers (e.g. thermosets) contain crosslinks; even
polymer chain entanglements can function as crosslinks at
some time scale depending on polymer’s molecular
weight. Indeed, some researchers may hold the opinion
that shape memory is an intrinsic property to all polymers.
While such an opinion is more semantic than anything
else, it is worth pointing out that the current research in
SMPs is less about passively discovering shape memory
effects in existing polymers, and more focused on actively
designing new materials with precisely controlled shape
memory properties.
Design strategies for shape memory polymersChemical crosslinking of a high Mw thermoplastic
polymer
The most straightforward strategy to prepare a SMP, at
least conceptually, is to take an existing high Mw thermo-
plastic polymer and chemically crosslink it. As mentioned
before, all polymers have at least one thermal transition
that can be used as the ‘switching mechanism’ for shape
memory. Chemical crosslinking introduces a network
structure that defines the permanent shape, or ‘memory’.
Two common crosslinking methods used to prepare
Current Opinion in Chemical Engineering 2013, 2:103–111
SMPs are (1) organic peroxides [27–29,30�,31–33] and
(2) high energy radiation [34–38]. In fact one of the
earliest SMPs was prepared this way (radiation cross-
linked polyethylene) and led to the invention of ‘heat-
shrinkable’ materials.
This method can be applied to both amorphous and semi-
crystalline polymers, leading to Class I and Class II SMPs,
respectively. However the crosslinking efficiency can
vary significantly among different polymers. Typically,
saturated polymers are less susceptible to free radical
crosslinking compared to unsaturated polymers, resulting
in incomplete crosslinking and low gel fractions
[27,31,32,37]. This can hurt the shape memory perform-
ance, leading to both low recovery and permanent defor-
mation. One possible solution is to physically blend the
polymer with an unsaturated species that functions as a
‘sensitizer’, before crosslinking. For example, Zhu et al.[36] blended poly(e-caprolactone) (PCL, a saturated poly-
mer) with small fractions of polymethylvinylsiloxane
(PMVS) before subjecting the system to gamma-ray
irradiation. The resulting materials showed a monotonic
increase in gel fraction with increasing amounts of PMVS
at identical radiation dosages. Voit et al. conducted quite
extensive studies on the impact of different types of
sensitizers [34,35] as well as different sensitizer lengths
[35] on the electron-beam crosslinking of poly(methyl
acrylate) (PMA). In general, the use of a sensitizer was
found to significantly lower the gelation dose and lead to
good shape memory properties.
One practical advantage of this approach is that it allows
the base polymer to be shaped by conventional thermo-
plastic processing methods such as extrusion and injec-
tion molding, followed by thermal (peroxide) or radiation
crosslinking. The main disadvantage of this approach is
its rather limited control over shape memory properties.
For a given SMP prepared under this approach, the
transition temperature (either Tm or Tg) is largely inher-
ited from the starting polymer and can only be adjusted in
a small range. Moreover, such adjustment would inevi-
tably change other properties. Possible side reactions (e.g.
main chain degradation) during the crosslinking process
must also be considered.
One-step polymerization of monomers/pre-polymers
and crosslinking agents
It is possible to prepare an SMP from the ‘bottom-up’ by
reacting properly selected monomers, reactive pre-polymers
and crosslinking agents, in which polymerization and
chemical crosslinking occur in a single step. The system
usually undergoes a sol–gel transition similar to many ther-
mosetting resins. This strategy can lead to much greater
control over shape memory and other material properties.
One of the most studied systems under this general
approach is the family of copolymer networks prepared
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Design strategies for shape memory polymers Luo and Mather 105
from the free radical polymerization of acrylate/metha-
crylate monomers. Our group [39] first reported a series of
Tg-based (Class I) SMPs prepared from methyl
methacrylate (MMA), butyl methacrylate (BMA) and a
bi-functional crosslinking agent tetraethylene glycol
dimethacrylate (TEGDMA). The transition temperature
(Tg in this case) and rubbery modulus can be precisely
controlled by the MMA/BMA ratio and the amount of
TEGDMA, respectively. More extensive studies were
conducted by Ken Gall’s group, who investigated a
variety of acrylate/methacrylate monomers and crosslin-
kers and published a large body of data [40,41�,42–48]
useful for designing SMPs using these materials. Other
examples of Class I SMPs prepared by this approach
include epoxy-based SMPs [49–53], thiol-enephotopoly-
merized networks [54], thermosetting polyurethanes
[55,56], among others.
This approach can also be applied to Tm-based, Class II
SMPs. The main difference is that oftentimes a reactive
pre-polymer (typical Mw � several thousand g/mol) cor-
responding to the crystallizable switching segment is used
in addition to other monomers and/or crosslinking agents.
One of the early examples of SMPs prepared under this
approach was the free-radical polymerized co-network
based on PCL dimethacrylate and n-butyl acrylate
[57]. Again, SMPs following the same strategy but based
on a variety of other polymerization/crosslinking chem-
istries and crystallizable segments (including liquid crys-
talline segments [25,58�,59]) have been developed since
then.
Generally speaking, SMPs prepared under this approach
exhibit excellent shape memory properties (e.g. high
fixing and recovery ratios, good cycle life-time/stability)
owing to both the thermoset (permanent network) nature
of such SMPs and their well-defined structures down to
the molecular level. Control of shape memory properties
is reasonably easy, usually involving adjusting variables
such as monomer composition, pre-polymer Mw, and
crosslinker concentration and functionality. The biggest
disadvantage of this strategy may be constraints for pro-
cessing. Because of the thermosetting nature, many SMPs
exhibit relatively narrow processing windows (before the
gel time), and require processing techniques usually
relegated to those of coatings and adhesives.
One-step synthesis of phase-segregated block
copolymers
This strategy is similar to the previous one in that the
SMP is fundamentally designed at the molecular scale.
However the goal here is to yield a thermoplastic polymer
(rather than a thermoset) that can be processed using
more conventional plastics processing techniques. In
order to obtain shape memory, SMPs by this strategy
are usually phase-segregated block copolymers, with two
blocks exhibiting different transition temperatures. The
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blocks with higher and lower transition temperatures are
sometimes referred to as the ‘hard’ and ‘soft’ segments,
respectively.
The most extensively studied SMPs prepared by this
strategy are shape memory polyurethanes (SMPUs)
[60�,61�,62,63], owing to the simplicity and versatility
of urethane chemistry compared to other polymerization
techniques, as well as the good availability of raw
materials (polyols and isocyanates). In SMPUs, soft and
hard segments form a multi-block structure. The hard
segment functions as physical crosslinks and defines the
permanent shape (‘memory’) whereas the soft segment
provides the switching mechanism for shape memory.
Depending on the selection of soft segment (glassy or
semi-crystalline), both Class III and Class IV SMPs can be
prepared. The hard segment can be glassy or semi-crystal-
line polymer segments with high Tg or Tm. A novel hard
segment explored in our group is Polyhedral Oligosilses-
quioxane (POSS), a nanostructured hybrid material with
an inorganic silica-oxygen cage functionalized with up to
8 pendent organic groups [64,65]. POSS can form crystal-
line nanodomains (with relatively high Tm’s) that when
used as the hard segment for SMPUs, gives excellent
rubbery elasticity and shape memory properties.
In general, SMPs by this strategy afford good control over
shape memory as well as other material properties. For a
given combination of soft/hard segments, the key vari-
ables are the ratio between the two blocks and their
molecular weights. However, owing to the thermoplastic
and physically crosslinked nature of these SMPs, the
shape memory performance (particularly recovery) is
often inferior to that of thermoset SMPs (Classes I and
II). Deviation from ideal entropic rubber and time-de-
pendent viscoelastic behavior can occur in the physically
crosslinked state, compromising both shape recovery and
cycle stability. Reasonable shape memory is achieved in
relatively narrow compositional windows [7]. The most
significant advantage, however, lies also in being thermo-
plastic, which allows for easy processing into different
permanent shapes and functional forms.
Direct blending of different polymers
Physically blending different thermoplastic polymers
(each with no or limited shape memory) to prepare blends
with shape memory properties represents another major
design strategy. The fundamental concept is to obtain
‘memory’ from one polymer and a switching mechanism
from the other. Following this concept, two different
approaches have been demonstrated.
As mentioned earlier, all polymers contain at least one
thermal transition. What many conventional thermoplas-
tics lack is an effective network structure when the
transition temperature is exceeded. Therefore, the first
approach is to blend in a second thermoplastic polymer
Current Opinion in Chemical Engineering 2013, 2:103–111
106 Materials engineering
with a higher transition temperature (than the host/matrix
polymer) that functions as physical crosslinks for tem-
peratures between the two transitions. Based on this
concept, Liu and Mather [66] reported a Class III SMP
blend consisting of poly(vinyl acetate) (PVAc) and poly(-
lactic acid) (PLA). PVAc is an amorphous polymer with a
Tg of ca. 40 8C whereas PLA is a semi-crystalline polymer
with a high melting temperature of ca. 165 8C. PLA
crystals physically crosslink the PVAc and a well-defined
rubbery plateau is obtained when the blend is heated
between the Tg of PVAc and the Tm of PLA. Similarly,
Class IV SMPs can also be prepared by using a semi-
crystalline polymer as the low-transition component.
Examples include the poly(p-dioxanone) (PPDO)/PCL
blends reported by Behl et al. [67] and high-density
polyethylene (HDPE)/poly(ethylene terephthalate)
(PET) [68] blends studied by Li et al.
On the other hand, elastomers do possess the network
structure required for memory, but lack a useful switching
transition (since their Tg’s are typically sub-ambient)
needed for full shape memory. Thus, a second approach
is based on blending a polymer of desired Tg or Tm with an
elastomer. For example, Zhang et al. [69] prepared phase-
separated SMP blends composed of a styrene–butadiene–styrene (SBS) thermoplastic elastomer with a semi-crys-
talline PCL. A slight deviation of this approach (although
following the same basic concept) was presented by Weiss
et al. [70], who blended an elastomeric ionomer (sulfo-
Figure 2
Elastomeric Matrix Thermoplastic F
(a)
(c)
0 s 2 s
CH3
CH2CH 2CH 2CH 2CHCH3
SiOO
Cn
Shape memory elastomeric composites (SMECs). (a) Schematic illustration
morphology and (c) a series of photographs showing the recovery from a fi
80 8C.Reproduced with permission from Ref. [72�].
Current Opinion in Chemical Engineering 2013, 2:103–111
nated EPDM) with various low-Mw fatty acids and fatty
acid salts. The melting of these fatty acids and salts
constitute the transitions for shape memory.
Although conceptually simple, implementing the blending
strategy in reality can be complicated and challenging.
Blends from different polymers can vary significantly in
morphology, thermal and phase behavior, depending on a
large number of variables including miscibility, molecular
weights, specific interactions, among others. Factors such
as thermal history [66,71], interfacial strength [68], and
morphology [69] all have pronounced impact on shape
memory properties. Even more, it is often difficult to
control one material property without affecting others.
Nevertheless, this still represents an important design
strategy for SMPs.
New design strategies of SMPs
During the past several years, our group has been engaged
in developing new design strategies for SMPs that enable
good shape memory performance, easily tuned shape
memory behavior, good processability and cost-
economics for practical applications. The main achieve-
ment is a new strategy based on the concept of functional
composite materials. Below we highlight some key
examples of SMPs developed under this strategy.
The strategy was first implemented to prepare shape
memory elastomeric composites (SMECs) [72�]. This
ibers
(b)
4 s 8 s
2 - O n 10kV ×850 20 μm 20 40 SE 1
Current Opinion in Chemical Engineering
of the composite structure, (b) SEM image showing the bulk composite
xed, temporary shape to the permanent shape on a hot-plate at
www.sciencedirect.com
Design strategies for shape memory polymers Luo and Mather 107
material was designed to exhibit a two-phase morphology
in which semi-crystalline PCL exists as non-woven fab-
rics of microfibers evenly distributed in a continuous
silicone rubber (under the commercial name Sylgard
184) matrix (Figure 2A and B). None of the components
has useful shape memory on its own; however, by func-
tioning collectively, with Sylgard providing an entropic
network (‘memory’) and PCL serving as a Tm-based
switching segment, excellent shape memory performance
was observed (Figs. 1 and 2C). It should be noted that this
approach is fundamentally different from the direct
blending strategy outlined above. In contrast to direct
blending, the two components in SMECs are not blended
in a melt or solution state. Rather the material is prepared
by infiltrating an electrospun PCL mat with liquid Syl-
gard and curing the Sylgard below the Tm of PCL. There-
fore the morphology is pre-determined and not affected
by factors such as polymer miscibility, blending
Figure 3
Elastomer
Semi-crystalline/glassythermoplastic
Shape MemoryElastomeric Composites
(SMECs)
SMP or Elastomer
Semi-crystalline/glassythermoplastic
Shape Memory Assisted ShapeMemory (SMASH) Coatings
(a)
(c)Matrix
0 s 2 s
4 s 8 s
Δ
Schematic illustration of the new design strategy for SMPs based on the co
decoupled components (fibers and matrix), a variety of different properties
composites (SMECs), (b) triple-shape polymeric composites (TSPCs), (c) sh
conductive shape memory nanocomposites.Reproduced with permission fro
www.sciencedirect.com
conditions, or thermal history. Unlike any previous
strategy, this composite design decouples the two func-
tional components (memory and switching) both physi-
cally and chemically, and allows a greater control of shape
memory polymer properties by manipulation of the
underlying components individually.
Following the same basic design strategy, triple-shape
polymeric composites (TSPCs) were developed upon
simple replacement of the rubber matrix (as in SMECs)
with an epoxy based, Class I SMP [73�]. The additional
transition from the matrix Tg, which can be easily con-
trolled via the monomer ratio in the epoxy formulation
with no effect on PCL Tm, enabled triple-shape memory
[74–76,77�,78–82]. As such, the composite demonstrated
an ability to fix two independent temporary shapes and
recover sequentially from the first to the second tempor-
ary shape, and eventually to the permanent shape upon
Fibers
SMP
Semi-crystalline/glassythermoplastic
Triple-ShapePolymeric Composite
(TSPCs)
SMP
Conductive Network(Carbon Nanofibers)
Electrically ConductiveShape Memory
Nanocomposites
0
0.0 0.5 1.0 1.5 2.0 2.5 3.0
102030R
eco
very
(%
)
Time (s)
405060708090
100110
(b)
(d)
25 °C 40 °C 80 °C
Current Opinion in Chemical Engineering
ncept of functional composites. By individual manipulation of the two
and functions can be realized, including (a) shape memory elastomeric
ape memory assisted self-healing (SMASH) coatings, and (d) electrically
m Refs. [72�,73�,86].
Current Opinion in Chemical Engineering 2013, 2:103–111
108 Materials engineering
Figure 4
10 s 25 s
50 s 1140 sCurrent Opinion in Chemical Engineering
Photographs showing the shape recovery of a sponge/hydrogel composite in water at 0 8C. The hydrogel, a PEO–PPO–PEO tri-block copolymer,
exhibits LCST behavior in water. By cooling below the critical LCST temperature, the hydrogel transitions from a gel to a solution and enables
unprecedented, cooling-induced shape recovery as shown in the photographs.
Reprinted with permission from Ref. [88�].
continuous heating. The same SMP system was also
developed to function as self-healing coatings [83,84].
In addition to manipulating the matrix, one can also alter
the fiber phase to achieve novel SMPs such as Sylgard/
PVAc SMECs, by replacing the PCL fibers with PVAc
[85], and electrically conductive shape memory nanocom-
posites, by replacing the fiber phase with PAN-based
carbon nanofibers [86]. A illustrative summary of this
new design strategy is shown in Figure 3.
Table 1
Comparison of different design strategies for shape memory polymer
Design strategy Advantag
Chemical crosslinking of a
high Mw thermoplastic polymer
Conceptually simple; can us
polymers
One-step polymerization of
monomers/pre-polymers and
crosslinking agents
Good control over SM prope
One-step synthesis of
phase-segregated block copolymers
Good control over SM prope
processing methods
Direct blending of different polymers Conceptually simple; potenti
Fiber/matrix based
composite strategy
Widely applicable to a large
good control over SM prope
design flexibility
Current Opinion in Chemical Engineering 2013, 2:103–111
Very recently, work along similar lines has appeared from
various other groups. For example, Stone et al. [87]
fabricated ananocomposite by incorporating poly(vinyl
alcohol) (PVA) fibers into an elastomeric ethylene-
oxide/epichlorohydrin copolymer matrix. The material
exhibits water-responsiveness and decreases its modulus
by 2 orders of magnitude upon 10–15 min exposure to
water, which can be utilized for water-triggered shape
memory, although this subject was not explored yet by
s
es Disadvantages
e conventional Limited control over SM properties; can be
challenging to achieve high gel fraction and
control side reactions in certain polymer
systems
rties Limited processing methods
rties; flexibility in Resulting material often displays inferior SM
properties, especially shape recovery
ally easy to process Requires either strong interactions or
compatibilization to achieve good SM
properties; controlling the morphology can
be challenging
number of materials;
rties; large material
May require less conventional processing
methods
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Design strategies for shape memory polymers Luo and Mather 109
the authors. In a quite unique embodiment of this same
strategy, Wang et al. [88�] infiltrated a plastic sponge
(elastic owing to the porous structure, composition unspe-
cified) with a hydrogel to make a composite material. The
hydrogel, a polyethylene-polypropylene-polyethylene
(PEO–PPO–PEO) triblock copolymer, was selected
based on its well-known LCST behavior. For this com-
posite, at temperatures higher than the LCST critical
temperature, gelation occurs which can be used as a
mechanism to fix a temporary deformation of the material
(by resisting the elastic recovery of the sponge). At
temperatures below the critical temperature, the hydrogel
becomes a solution and can no longer carry stress, allow-
ing the shape recovery of the sponge to occur. Therefore
this innovative design enables unprecedented, cooling-
induced shape memory (Figure 4).
SummaryIn this article, we have briefly reviewed both common and
emergent design strategies for thermally responsive
SMPs. A summary of these strategies is presented in
Table 1. By design, our review is not comprehensive.
Instead, we hope that it can serve as basic guidance on
what strategy to choose when designing an appropriate
SMP for a specific application. The key factors to consider
when evaluating these strategies include: shape memory
performance, the degree of control over shape memory as
well as other material properties, processing complexities,
and raw material availability/cost. With the rapid progress
in the field of SMPs we envision new and more innovative
design strategies to further emerge in the future.
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� of special interest
�� of outstanding interest
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24. Le DM, Kulangara K, Adler AF, Leong KW, Ashby VS: Dynamictopographical control of mesenchymal stem cells by cultureon responsive poly(e-caprolactone) surfaces. Adv Mater 2011,23:3278-3283.
25. Burke KA, Mather PT: Soft shape memory in main-chain liquidcrystalline elastomers. J Mater Chem 2010, 20:3449-3457.
26. Ishida K, Hortensius R, Luo X, Mather PT: Soft bacterialpolyester-based shape memory nanocomposites featuringreconfigurable nanostructure. J Polym Sci Part B Polym Phys2012, 50:387-393.
27. Li F, Zhu W, Zhang X, Zhao C, Xu M: Shape memory effect ofethylene – vinyl acetate copolymers. J Appl Polym Sci 1999,71:1063-1070.
28. Liu C, Chun SB, Mather PT, Zheng L, Haley EH, Coughlin EB:Chemically cross-linked polycyclooctene: synthesis,characterization, and shape memory behavior.Macromolecules 2002, 35:9868-9874.
29. Kolesov IS: Multiple shape-memory behavior and thermal-mechanical properties of peroxide cross-linked blends oflinear and short-chain branched polyethylenes. Express PolymLett 2008, 2:461-473.
30.�
Chung T, Romo-Uribe A, Mather PT: Two-way reversible shapememory in a semicrystalline network. Macromolecules 2008,41:184-192.
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110 Materials engineering
An early report of two-way shape memory behavior in semi-crystallinepolymer networks.
31. Yu X, Zhou S, Zheng X, Guo T, Xiao Y, Song B: A biodegradableshape-memory nanocomposite with excellent magnetismsensitivity. Nanotechnology 2009, 20:235702.
32. Li J, Rodgers WR, Xie T: Semi-crystalline two-way shapememory elastomer. Polymer 2011, 52:5320-5325.
33. Cuevas JM, Laza JM, Rubio R, German L, Vilas JL, Leon LM:Development and characterization of semi-crystallinepolyalkenamer based shape memory polymers. Smart MaterStruct 2011, 20:035003.
34. Voit W, Ware T, Gall K: Radiation crosslinked shape-memorypolymers. Polymer 2010, 51:3551-3559.
35. Ware T, Voit W, Gall K: Effects of sensitizer length on radiationcrosslinked shape-memory polymers. Radiat Phys Chem 2010,79:446-453.
36. Zhu G, Xu S, Wang J, Zhang L: Shape memory behaviour ofradiation-crosslinked PCL/PMVS blends. Radiat Phys Chem2006, 75:443-448.
37. Zhu G, Liang G, Xu Q, Yu Q: Shape-memory effects of radiationcrosslinked poly (e-caprolactone). J Appl Polym Sci 2003,90:1589-1595.
38. Hearon K, Gall K, Ware T, Maitland DJ, Bearinger JP, Wilson TS:Post-polymerization crosslinked polyurethane shape memorypolymers. J Appl Polym Sci 2011, 121:144-153.
39. Liu C, Mather PT: Thermomechanical characterization of atailored series of shape memory polymers. J Appl Med Plast2002, 6:47-52.
40. Yakacki CM, Shandas R, Lanning C, Rech B, Eckstein A, Gall K:Unconstrained recovery characterization of shape-memorypolymer networks for cardiovascular applications.Biomaterials 2007, 28:2255-2263.
41.�
Safranski DL, Gall K: Effect of chemical structure andcrosslinking density on the thermo-mechanical properties andtoughness of (meth)acrylate shape memory polymernetworks. Polymer 2008, 49:4446-4455.
A comprehensive study of structure–property relationships in Tg based(meth)acrylate SMPs.
42. Yakacki CM, Willis S, Luders C, Gall K: Deformation limits inshape-memory polymers. Adv Eng Mater 2008, 10:112-119.
43. Ortega AM, Kasprzak SE, Yakacki CM, Diani J, Greenberg AR, Gall K:Structure–property relationships in photopolymerizable polymernetworks: effect ofcomposition on the crosslinked structure andresulting thermomechanical properties of a (meth)acrylate-based system. J Appl Polym Sci 2008, 110:1559-1572.
44. Yakacki CM, Shandas R, Safranski D, Ortega AM, Sassaman K,Gall K: Strong, tailored, biocompatible shape-memory polymernetworks. Adv Funct Mater 2008, 18:2428-2435.
45. Smith KE, Temenoff JS, Gall K: On the toughness ofphotopolymerizable (meth)acrylate networks for biomedicalapplications. J Appl Polym Sci 2009, 114:2711-2722.
46. Smith KE, Sawicki S, Hyjek MA, Downey S, Gall K: The effect ofthe glass transition temperature on the toughness ofphotopolymerizable (meth)acrylate networks underphysiological conditions. Polymer 2009, 50:5112-5123.
47. Voit W, Ware T, Dasari RR, Smith P, Danz L, Simon D, Barlow S,Marder SR, Gall K: High-strain shape-memory polymers. AdvFunct Mater 2010, 20:162-171.
48. Smith KE, Trusty P, Wan B, Gall K: Long-term toughness ofphotopolymerizable (meth)acrylate networks in aqueousenvironments. Acta Biomater 2011, 7:558-567.
49. Xie T, Rousseau IA: Facile tailoring of thermal transitiontemperatures of epoxy shape memory polymers. Polymer2009, 50:1852-1856.
50. Rousseau IA, Xie T: Shape memory epoxy: composition,structure, properties and shape memory performances. JMater Chem 2010, 20:3431-3441.
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51. Leonardi AB, Fasce LA, Zucchi IA, Hoppe CE, Soule ER, Perez CJ,Williams RJJ: Shape memory epoxies based on networks withchemical and physical crosslinks. Eur Polym J 2011, 47:362-369.
52. Song WB, Wang LY, Wang ZD: Synthesis andthermomechanical research of shape memory epoxy systems.Mater Sci Eng A 2011, 529:29-34.
53. Feldkamp DM, Rousseau IA: Effect of chemical composition onthe deformability of shape-memory epoxies. Macromol MaterEng 2011, 296:1128-1141.
54. Nair DP, Cramer NB, Scott TF, Bowman CN, Shandas R:Photopolymerized thiol-ene systems as shape memorypolymers. Polymer 2010, 51:4383-4389.
55. Lin JR, Chen LW: Shape-memorized crosslinked ester-typepolyurethane and its mechanical viscoelastic model. J ApplPolym Sci 1999, 73:1305-1319.
56. Chen W, Zhu C, Gu X: Thermosetting polyurethanes with water-swollen and shape memory properties. J Appl Polym Sci 2002,84:1504-1512.
57. Lendlein A, Schmidt AM, Langer R: AB-polymer networks basedon oligo(e-caprolactone) segments showing shape-memoryproperties. Proc Natl Acad Sci USA 2001, 98:842-847.
58.�
Rousseau IA, Mather PT: Shape memory effect exhibited bysmectic-C liquid crystalline elastomers. J Am Chem Soc 2003,125:15300-15301.
The first report of one-way shape memory behavior in a liquid crystallineelastomer.
59. Rousseau IA, Qin H, Mather PT: Tailored phase transitions viamixed-mesogen liquid crystalline polymers with silicon-basedspacers. Macromolecules 2005, 38:4103-4113.
60.�
Kim BK, Lee SY, Xu M: Polyurethanes having shape memoryeffects. Polymer 1996, 37:5781-5793.
A classic paper on thermoplastic shape memory polyurethanes.
61.�
Lendlein A, Langer R: Biodegradable, elastic shape-memorypolymers for potential biomedical applications. Science 2002,296:1673-1676.
One of the earliest reports on biodegradable shape memory polymers.
62. Hu JL, Ji FL, Wong YW: Dependency of the shape memoryproperties of a polyurethane upon thermomechanical cyclicconditions. Polym Int 2005, 54:600-605.
63. Mohr R, Kratz K, Weigel T, Moneke M, Lendlein A: Initiation ofshape-memory effect by inductive heating of magneticnanoparticles. Proc Natl Acad Sci USA 2006, 103:3540-3545.
64. Knight PT, Lee KM, Qin H, Mather PT: Biodegradablethermoplastic polyurethanes incorporating polyhedraloligosilsesquioxane. Biomacromolecules 2008, 9:2458-2467.
65. Wu J, Ge Q, Mather PT: PEG–POSS multiblock polyurethanes:synthesis, characterization, and hydrogel formation.Macromolecules 2010, 43:7637-7649.
66. Liu C, Mather PT: Thermomechanical characterization ofblends of poly(vinyl acetate) with semicrystalline polymers forshape memory applications. In Proceedings of the AnnualTechnical Conference of the Society of Plastics Engineers(ANTEC). 2003:1962-1966.
67. Behl M, Ridder U, Feng Y, Kelch S, Lendlein A: Shape-memorycapability of binary multiblock copolymer blends with hardand switching domains provided by different components.Soft Matter 2009, 5:676-684.
68. Li S, Lu L, Zeng W: Thermostimulative shape-memory effect ofreactive compatibilized high-density polyethylene/poly(ethylene terephthalate) blends by an ethylene–butyl acrylate–glycidyl methacrylate terpolymer. J Appl Polym Sci 2009,112:3341-3346.
69. Zhang H, Wang H, Zhong W, Du Q: A novel type of shapememory polymer blend and the shape memory mechanism.Polymer 2009, 50:1596-1601.
70. Weiss RA, Izzo E, Mandelbaum S: New design of shape memorypolymers: mixtures of an elastomeric ionomer and low molarmass. Macromolecules 2008, 41:2978-2980.
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Design strategies for shape memory polymers Luo and Mather 111
71. Campo C, Mather PT: Shape memory binary blends:compositionally tailored fixing and recovery. In TheProceedings of the Annual Technical Conference of the Society ofPlastics Engineers (ANTEC). 2006:1510-1514.
72.�
Luo X, Mather PT: Preparation and characterization of shapememory elastomeric composites. Macromolecules 2009,42:7251-7253.
The first paper that demonstrates the new composite design strategy ofSMPs.
73.�
Luo X, Mather PT: Triple-shape polymeric composites (TSPCs).Adv Funct Mater 2010, 20:2649-2656.
An important implementation of the composite design strategy to preparetriple-shape SMPs.
74. Behl M, Bellin I, Kelch S, Wagermaier W, Lendlein A: One-stepprocess for creating triple-shape capability of AB polymernetworks. Adv Funct Mater 2009, 19:102-108.
75. Xie T, Xiao X, Cheng Y-T: Revealing triple-shape memory effect bypolymer bilayers. Macromol Rapid Commun 2009, 30:1823-1827.
76. Qin H, Mather PT: Combined one-way and two-way shapememory in a glass-forming Nematic network. Macromolecules2009, 42:273-280.
77.�
BellinI, Kelch S, Langer R: Lendlein a: polymeric triple-shapematerials. Proc Natl Acad Sci USA 2006, 103:18043-18047.
The first paper that introduces the concept of triple-shape memory.
78. Behl M, Lendlein A: Triple-shape polymers. J Mater Chem 2010,20:3335-3345.
79. Pretsch T: Triple-shape properties of athermoresponsivepoly(ester urethane). Smart Mater Struct2010, 19:015006.
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80. Chen S, Hu J, Yuen CW, Chan L, Zhuo H: Triple shape memoryeffect in multiple crystalline polyurethanes. Polym Adv Technol2009, 21:377-380.
81. Luo H, Hu J, Zhu Y, Zhang S, Fan Y, Ye G: Achieving shapememory: reversible behaviors of cellulose–PU blends in wet–dry cycles. J Appl Polym Sci 2011, 125:657-665.
82. Luo H, Hu J, Zhu Y: Polymeric shape memory nanocompositeswith heterogeneous twin switches. Macromol Chem Phys 2011,212:1981-1986.
83. Luo X, Mather PT: Shape memory assisted self-healingcoatings, in preparation.
84. Mather PT, Luo X: Self-healing product. US Patent Application12/644,766.
85. Luo X: Thermally responsive polymer systems for self-healing,reversible adhesion and shape memory applications. PhDDissertation. Syracuse University; 2010.
86. Luo X, Mather PT: Conductive shape memory nanocompositesfor high speed electrical actuation. Soft Matter 2010, 6:2146.
87. Stone DA, Wanasekara ND, Jones DH, Wheeler NR, Wilusz E,Zukas W, Wnek GE, Korley LTJ: All-organic, stimuli-responsivepolymer composites with electrospun fiber fillers. ACS MacroLett 2012, 1:80-83.
88.�
Wang CC, Huang WM, Ding Z, Zhao Y, Purnawali H: Cooling-/water-responsive shape memory hybrids. Compos Sci Technol2012, 72:1178-1182.
A very innovative design of a shape memory composite that exhibitsunprecedented cooling-induced shape memory.
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