Mesenchymal stem/progenitor cells (MSCs) have a multi-differentiation potential into specialized cell types, with remarkable regenerative and therapeutic results. Several factors could trigger the differentiation of MSCs into specific lineages, among them the biophysical and chemical characteristics of the extracellular matrix (ECM), including its stiffness, composition, topography, and mechanical properties. MSCs can sense and assess the stiffness of extracellular substrates through the process of mechanotransduction. Through this process, the extracellular matrix can govern and direct MSCs’ lineage commitment through complex intracellular pathways. Hence, various biomimetic natural and synthetic polymeric matrices of tunable stiffness were developed and further investigated to mimic the MSCs’ native tissues. Customizing scaffold materials to mimic cells’ natural environment is of utmost importance during the process of tissue engineering. This review aims to highlight the regulatory role of matrix stiffness in directing the osteogenic differentiation of MSCs, addressing how MSCs sense and respond to their ECM, in addition to listing different polymeric biomaterials and methods used to alter their stiffness to dictate MSCs’ differentiation towards the osteogenic lineage. Stem/progenitor cells are characterized by their outstanding differentiation potential into multiple types of specialized cell lineages, relying on their pluri- or multipotency, while maintaining their self-replicating ability ,7,8,9,10Proliferation and differentiation of MSCs can be triggered by certain growth factors and chemicals, inducing specific genetic events, affecting the release of transcriptional factors, which regulate the differentiation of MSCs into specific lineages ,17. AddiRegeneration and healing of bone injuries, particularly in large bony defects, is a complicated process . Based oECM was earlier believed to be an inert matrix that only provides physical support to cells; later, the important role of ECM in various cellular processes was introduced . The MSCSeveral transcription factors are involved in the osteogenic differentiation pathway, including runt-related transcription factor 2 (Runx2), osterix , Smad, and β-catenin ,26,27. RBMPs are famous for their capability to induce bone formation. They activate intracellular Smad, which translocates to the nucleus and acts as a transcription factor besides promoting Runx2 expression . BMP9 stOsx is an essential transcription factor for osteogenic differentiation and subsequent bone formation. In Osx null mice, no bone formation occurred; additionally, in the Runx2 null mice, no expression of Osx was noted, indicating that Osx acts as a downstream of Runx2 and emphasizing its role in MSCs osteogenic differentiation and bone formation . Moreoveβ-catenin further plays a critical role in MSCs’ osteogenic differentiation. Its absence blocks the osteogenic differentiation and allows for the chondrogenic differentiation of MSCs . Wnt sigStem/progenitor cells’ behavior is largely affected by extracellular signals from the microenvironment, including chemical and mechanical cues from the ECM . Unlike The extracellular-signal-regulated kinase (ERK) and p38 are members of the mitogen-activated protein kinase (MAPK) enzymes family that is concerned with mechanotransduction pathways . ERK is Matrix stiffness has a profound impact on MSCs’ behavior. The adhesion, proliferation, and spreading capacity of umbilical cord MSCs varied when cultured on polyacrylamide gels coated with fibronectin with different stiffness  . MaximumInsights into how stem/progenitor cells sense signals from the ECM and how they respond to these signals at the molecular level have become an area of increasing research ,22,52. LNatural and synthetic polymeric materials could offer versatile matrices, which are biocompatible and biodegradable, with tunable characteristics, precise control of their topography, and ease of processing ,55. BiomECM stiffness guides stem/progenitor cells’ differentiation down corresponding tissue lineages . OsteogeIn this review, we highlight the regulatory role of matrix stiffness in directing the osteogenic differentiation of MSCs, addressing how MSCs sense and respond to their ECM, in addition to listing different polymeric biomaterials commonly used in vitro and methods used to alter their stiffness to dictate MSCs differentiation towards the osteogenic lineage. Moreover, through the current review, we aim to elucidate the effect of ECM stiffness on the MSCs’ osteogenic potential and the underlying mechanism, which is of particular importance during the process of designing new materials for bone-tissue regeneration.MSCs can sense and assess the stiffness of extracellular substrates ,60. The Focal adhesions are complexes of highly specialized proteins and macromolecules that can attach cells to the ECM, allowing them to sense and respond to mechanical stimuli . Focal aIntegrins are alpha and beta subunits heterodimers existing in different combinations . There aIt is noteworthy that MSCs’ surface integrins’ expression can influence MSCs’ lineage commitment . FurtherThe binding of integrin to ECM components triggers the intracytoplasmic assembly of focal adhesion proteins, including talin, FAK, p130Cas, and vinculin, first forming focal complexes, which then grow, giving rise to focal adhesions, linking actin fibers to ECM components . FormatiMSCs lineage commitment and osteogenic differentiation in response to ECM mechanical cues, including matrix stiffness, involve upregulation of focal adhesion formation. Increasing matrix stiffness can promote number  and areaCytoskeletal-related proteins are responsible for the ability of stem/progenitor cells to respond to mechanical cues, including stiffness of the ECM . In addiThe actin cytoskeleton is responsible for the maintenance of cell shape, motility, and contractility. They also act as mechanical sensors for the extracellular environment . It is fThe binding of MSCs to a stiff substrate induces actin polymerization, as evident by an increased ratio of F-actin to G-actin, forming actin stress fibers, which trigger intracellular-signaling pathways . Stress Actin filaments can also interact with other components of the cellular cytoskeleton as intermediate filaments . IntermeMechanosensitive ion channels are a further mechanism implicated in MSCs’ mechanotransduction on stiff matrices. These ion channels are sensitive to substrate stiffness. Upon mechanical stimulation, they allow the intracellular influx of ions and can form complexes with stress fibers, eliciting intracellular signaling pathways . MechaniDifferent age-dependent changes in MSCs were reported, such as decreased proliferation ability  and osteMatrix stiffness regulates the MSCs’ differentiation into mature specific cells by activating transcription factors that upregulate genes responsible for the initiation and progression of particular cell-linage differentiation. The singling pathways involved in MSCs’ osteogenic differentiation are illustrated in . Rigid mThe hippo pathway is one of the signaling pathways involved in MSCs’ differentiation and is regulated by intra- and extracellular signals . The dowMSCs fate is also directed through actomyosin contractility and activated Rho/Rho kinase (ROCK) signaling , along wCells on stiff matrices develop high cytoskeletal tension, which is evidenced by enhanced actin stress fibers and large spread area. Below a compressive modulus of 25 kPa, regardless of the adhesive ligand presented, there is not enough cytoskeletal tension to promote osteogenic lineage differentiation . Based oInflammation can further counteract the inductive effect of matrix stiffness on osteogenic differentiation. Periodontal ligament stem cells (PDLSCs) cultured with the inflammatory cytokine interleukin (IL)-1β on gelatin/methacrylate hydrogels with different matrix stiffness showed a marked reduction in matrix stiffness-dependent osteogenic differentiation and expression of osteocalcin, as well as Runx2. This was through the activation of p38 signaling pathways, which were activated by IL-1β . FurtherSeveral natural and synthetic polymeric biomaterials are currently used in tissue engineering and regenerative medicine, serving as biomimetic matrices for in vitro culturing . These bAlginates extracted from seaweeds and algae are composed of β-1,4-linked blocks of β-D-mannuronic acid (M) and its C-5 epimer α-L-guluronic acid . AlginatSpatially modulating the mechanical properties in an alginate bioink, 3D printed constructs were postulated to regulate MSCs’ fate. Micro-CT-based imaging with 3D bioprinting and bioreactor system were utilized to fabricate 3D human MSCs-laden porous bone-like scaffolds with varying compressive moduli based on two unmodified polymers . Softer scaffolds with low alginate concentration  revealed accelerated and enhanced osteogenic differentiation with upregulated ALP activity than stiffer scaffolds . In the presence of the osteogenic differentiation medium, cells on soft scaffolds exhibited osteoblastic and early osteocyte-related gene expression and showed a 3D cellular network within the mineralized matrix .Increasing alginate molecular weight, as well as increasing the crosslinking ratio, produces a significantly stiffer bioink. Upon bioprinting cylindrical MSCs laden constructs with spatially variable mechanical stiffness from the core to the periphery, more MSCs underwent osteogenic differentiation within the stiffer regions of the printed constructs as evident by increased ALP staining . In contCollagen is considered to be the most abundant protein in mammals . Being tMatrix stiffness possesses a high impact on cellular bioactivity regardless of the presence or absence of growth factors. This was proved by culturing porcine adipose-derived stem cells (ADSCs) on sequentially integrated benzophenone photo-immobilization and carbodiimide, crosslinking collagen–glycosaminoglycan, with stiffness ranging from 2.85 to 5 MPa, in the presence or absence of covalently immobilized platelet-derived growth factor (PDGF-BB) and BMP-2 .In the presence of osteogenic culture media, human MSCs coated with three bilayers of collagen/alginate nanofilm with relatively high stiffness 24 and 53 MPa revealed an augmented osteogenic differentiation efficiency with a significant increase in ALP by activating transcriptional co-activators with the PDZ binding motif through extracellular signal-related kinase and p38-MAPK signaling .Nanoparticulate mineralized collagen–glycosaminoglycan scaffolds chemically crosslinked with 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide and N-hydroxysuccinimide had a higher range of elastic moduli 3.90 ± 0.36 kPa as compared to non-crosslinked materials. Cultured human MSCs on crosslinked substrates showed higher expression of osteogenic genes and proteins compared to non-crosslinked versions. This was maintained via the mechanotransduction mediators YAP/TAZ and the Wnt signaling pathway .MSCs seeded within a 3D collagen gel with an elastic modulus of ~108 Pa stimulated by vibrations of nanoscale amplitude in a vibrational bioreactor showed increased expression of Runx2, collagen I, ALP, osteopontin, osteocalcin, and BMP-2. This indicated that the viscoelastic properties of the collagen gel allowed the transfer of high-frequency vibrations to the cells seeded in 3D .In the absence of any differentiation supplementation, MSCs grew on stiffer (1.5 kPa) dehydrothermal and 1-ethyl-3-3-dimethyl aminopropyl carbodiimide (EDC) crosslinked collagen–glycosaminoglycan scaffolds showed the greatest level of Runx2 expression, while substrates with lower stiffness (0.5 kPa) resulted in significant elevation of SOX9 expression, indicating that MSCs are directed towards a chondrogenic lineage .Additionally, 3D scaffolds with the highest proportion of collagen in collagen and hydroxyapatite mixture coated on the decellularized cancellous bone with various stiffness  exhibited the highest stiffness that, in turn, promoted higher expressions of osteopontin and osteocalcin .Gelatin is a natural, biocompatible, non-immunogenic, hydrophilic, and biodegradable collagen derivative ,154,155.Gelatin crosslinking through the addition of chemical groups can help reduce these shortcomings . StiffneGelatin/starch-based hydrogel was fabricated with tunable stiffness from crosslinked gelatin with variable degrees of methacrylation  covalently bound to variable ratios of pentenoates modified starch (10 v% starch and 20 v% starch). Increasing the degree of methacrylation and combining crosslinked gelatin with starch, with subsequent increase in matrix stiffness, effectively promoted osteogenic differentiation of adipose stem cells (ASCs), as was evident by an increased ALP expression. GelMA 95% combined with starch showed the highest degree of osteogenic differentiation, while the highest degree of adipogenic differentiation was observed on the least crosslinked and most flexible gelatin hydrogel (GelMA 31%) .Three-dimensional porous gelatin scaffolds crosslinked using EDC further demonstrated an increase in the elastic modulus from ~0.6 to ~2.5 kPa, without any change in the scaffold internal structure. BMMSCs cultured on EDC-crosslinked gelatin scaffolds with increased stiffness showed an increased osteogenic differentiation as evidenced by increased Runx2 and osteocalcin expression in vitro. Subcutaneous implantation of EDC-crosslinked gelatin scaffold loaded with BMMSCs transfected with adenovirus encoding BMP-2 in mice demonstrated an increased bone formation in vivo, as compared to the control, non-crosslinked scaffold with low stiffness .Transglutaminase-crosslinked gelatin scaffold with variable stiffness was constructed, using gelatin concentrations of 3%, 6%, and 9%. The 9% gelatin gave rise to the highest stiffness (60.54 ± 10.45 kPa), while 3% gelatin resulted in the lowest stiffness (1.58 ± 0.42 kPa). BMMSCs encapsulated in the hydrogel with the highest stiffness demonstrated the highest osteogenic differentiation as revealed by ALP activity, calcified nodule formation, expression of SP7 transcription factor-2, Osx, Runx2, and osteocalcin .Augmentation of GelMA with alendronate and PEGDA showed a positive effect on osteogenic differentiation of BMMSCs. GelMA and alendronate were added at different concentrations and grafted on gelatin molecules, followed by further crosslinking, using 20 wt.% PEGDA to improve hydrogel scaffold stiffness from 4 to 40 kPa. Osteogenic differentiation of grafted BMMSCs was promoted on stiffer hydrogel with higher alendronate concentration, as evident by upregulation of ALP activity, collagen type I, and osteocalcin expression, as well as calcium deposition .Decellularized cell-derived matrices (dCDMs) could further provide a way to mimic natural tissues. It has been reported that aligned dCDMs could contribute to producing a more homogeneous environment, which resulted in a uniform response of cells to the biophysical environment, displaying a highly homogeneous phenotype and can undergo differentiation if properly stimulated . SubstraCell-derived matrices (CDMs) were attained by cultivating MC3T3-E1 pre-osteoblasts in the presence of ascorbic acid for two weeks on linear or flat surfaces. MC3T3-E1 cultivated on nanopatterned substrates produced an aligned fibrillar matrix, whose microarchitectural features remained intact after the decellularization process. Atomic force microscope measurements performed on bare dCDMs revealed very low Young’s moduli in the range of (0.01–0.1 kPa) that was increased by genipin crosslinking to reach . These matrices were further seeded with murine MSCs and cultivated in the presence of either osteogenic or adipogenic media for two weeks. Both the aligned and random dCDMs promoted murine MSC adhesion and proliferation in their pristine state, while maintaining high levels of stemness markers, with a more homogeneous population of undifferentiated cells, were seen on aligned dCDMs. On the pristine dCDMs, MSCs promptly underwent adipogenic differentiation when stimulated with induction media, while they were minimized in the presence of osteogenic medium, due to very low stiffness. On the contrary, MSCs responded consistently on stiff dCDMs, displaying a significant adipogenic and osteogenic differentiation potential .In another study, 3D demineralized bone matrices with the same 3D microstructure (porosity and pore size), but with various compressive moduli , were fabricated by controlling the decalcification duration . Low-stiffness scaffolds promoted BMMSCs’ osteogenic differentiation. Subcutaneous implantation in a rat model further revealed efficient improvement of cells’ infiltration and deposition of collagen fibers, in addition to upregulated positive osteocalcin and osteopontin expression, as well as angiogenesis upon utilizing the low-stiffness scaffolds. Further implantation in a femoral condylar defect rabbit model supported the previous findings and revealed that stromal-cell-derived factor-1α/CXC chemokine receptor (SDF-1α/CXCR4) signaling pathway was essential for the stiffness-mediated stem/progenitor recruitment and osteogenic differentiation during bone repair .Hyaluronic acid (HA) is a linear non-sulfated polysaccharide made up of repeated disaccharide molecules in alternating patterns (D-glucuronic acid and N-acetyl-D-glucosamine). This pattern is linked through interchanging β-1,4 and β-1,3 glycosidic bonds. HA is a fundamental component of ECM that regulates various cellular biological processes, such as migration, adhesion, proliferation, and differentiation, through binding with a specific receptor on the target cell ,168. OwiThrough adjusting the crosslinker (PEGTA) density, a series of hydrogels with different biochemical and biomechanical properties were developed by utilizing a thiol-functionalized HA and a thiol-functionalized recombinant human gelatin. Human BMMSCs were cultured on the hydrogels with different stiffness (storage modulus (G′) and corresponding PEGTA concentrations, namely 0.15 kPa (0.25%), 1.5 kPa (1.75%), and 4 kPa (2.5%), in adipogenic and osteogenic conditions. Adipogenic differentiation was confirmed by gene expression of lipoprotein lipase (LPL), as well as PPARγ2, with similar LPL expression levels demonstrated on the hydrogels with varying stiffness, whereas PPARγ2 expression was markedly enhanced upon increasing hydrogel stiffness. Cells exhibited spindle-shape morphology on the 0.15 kPa hydrogel, while displaying elongated and cuboidal appearance, similar to osteoblasts on greater stiffness hydrogels. Human MSCs cultured on the 1.5 kPa hydrogel significantly expressed osteopontin, while those cultured on the 4 kPa hydrogel revealed a significant upregulation in the expression of the late osteogenic gene  .In a novel HA hydrogel platform, ligation of the HAVDI adhesive peptide sequence from the extracellular N-cadherin domain 1 and the RGD adhesive motif from fibronectin led to Rac1-GTP-dependent reductions in the attachment of myosin IIA to the focal adhesions. This lack of myosin IIA incorporation into focal adhesions hindered the maturation of these adhesions with increasing substrate stiffness  and thereby decreased traction force generation on the underlying substrate. These alterations in the mechanical state of the MSCs further reduced mechanosensitive YAP/TAZ translocation to the nucleus, herewith attenuating the signaling pathways involved in mesenchymal development, including cell proliferation and osteogenic differentiation .®, a thiol-modified hyaluronan gel with the crosslinking agent PEG at ratios from (1:1 to 7:1). The co-differentiation media (a ratio of 50% chondrogenic:50% osteogenic) proved to be suitable for appropriate chondrogenic and osteogenic differentiation of human MSCs. On the stiffest matrix (HA:PEG construct at a 2:1 ratio), the three chondrogenic markers  were expressed by the differentiated human MSCs cultured for 21 days [An in vitro culture system for osteochondral tissue engineering was developed, using HA gels with various stiffness (G′ ranging from 10 to 45 Pa) attained by mixing Glycosil 21 days .Moreover, human BMMSCs were initially entrapped in a HA carrying sulfhydryl groups and a hydrophilic polymer bearing both acrylate and tetrazine groups with the shear elastic modulus (G′) =180 ± 42 Pa. The stiffness of the matrix was increased (G′ = 520 ± 80 Pa) by adding HA conjugated with multiple copies of trans-cyclooctene (TCO) to the human MSCs-laden gel culture media. The 3D matrix tagged with a TCO-cell-adhesive motif promoted the cells to undergo remarkable actin polymerization, changing from a rounded phenotype to a spindle morphology with long processes. After an additional seven days of culture in the modified media, quantitative analysis showed that RGD tagging enhanced cellular expression of matrix metalloproteinase 1, whereas it decreased the expression of tenascin C and collagen I/III. RGD tagging, however, was not sufficient alone to induce chondrogenic, adipogenic, fibroblastic/myofibroblastic, or osteogenic differentiation .Photo-crosslinked methacrylated HA hydrogels incorporating fragmented polycaprolactone (PCL) nanofibers with compression modulus 3122.5 ± 43.7 Pa promoted osteogenic differentiation of adipose-derived stem/progenitor cells incorporated into the composite hydrogel. The biomarkers collage type 1, ALP, and Runx2 were significantly expressed in the hydrogels containing nanofibers. In addition, the results of alizarin red staining confirmed osteogenic differentiation .Fibrin is one of the natural biopolymers that offers many advantages based on its excellent biocompatibility and cell-adhesion properties . HoweverPolyethylene glycol (PEG) is one of the most widely used synthetic polymers in the tissue engineering field. It is characterized by being chemically and biologically inert and by the high hydrophilicity of the polymer backbone. There is a wide range of polymer architectures and lengths that are commercially or synthetically accessible .MSCs cultured on 3D thixotropic PEG-silica nanocomposite gel with high stiffness (≥75 Pa) expressed the highest level of Runx2 and osteocalcin. Additionally, RGD cell-adhesion peptide sequence immobilization in the gel of 75 Pa stiffness promoted ~13% higher expression of the osteogenic transcription factor . Rat BMMHuman MSCs were encapsulated in a multilayer PEG-based hydrogel composed of a soft cartilage-like layer of chondroitin sulfate (48 kPa) and low RGD concentrations, a stiff bone-like layer 345 kPa with high RGD concentrations, and an intermediate interfacial layer with 100 kPa. The recorded stiffness of the multilayer hydrogel was 90 kPa. Opposite to static conditions, dynamic mechanical stimulation generated a high expression of collagens with collagen II in the cartilage-like layer, collagen X in the interfacial layer, and collagen I in the bone-like layer with the presence of mineral deposits in the bone layer .PEG/silk fibroin/HA (PEG/SF/HA) scaffold was prepared with varying HA concentrations, which influenced scaffold stiffness (80.98 to 190.51 kPa). PEG/SF/HA containing 50 mg HA cultured with rat BMMSCs enhanced cell adhesion, viability, the expression of all the osteogenesis-related markers in vitro and promoted superior calvarial defect repair in vivo, through modulating gene and protein expression levels .Additionally, human MSCs seeded on regularly and randomly patterned photodegradable PEG hydrogel surfaces with different stiff-to-soft ratios from ~2–3 kPa to ~10–12 kPa displayed higher cell spread, elongated morphologies, and superior YAP activation and osteogenic differentiation on the regularly patterned regions, as compared to those cultured on random patterns . High PE®’s crosslinker to base ratios  [Polydimethylsiloxane (PDMS) is characterized by its biocompatibility, flexibility, optical clarity, and elastic tunability . Dental  weight) ,190. Coa weight) . Osteoge weight) , while SASCs were cultured on soft and stiff PDMS substrates with moduli of elasticity ranging from (0.046 ± 0.02 MPa) and (1.014 ± 0.15 MPa), respectively. Stiff substrate enhanced the directed differentiation of ASCs into osteogenic lineages as evidenced by positive ALP stain. This enhancement was supplemented with the upregulated expression of Runx2 and Osx transcriptional factors .Osteogenic differentiation of rat MSCs incubated in osteogenic medium grown on PDMS, with stiffness gradients that ranged from 0.19 to 3.10 MPa, utilizing a temperature gradient during curing, was proven to be strongly influenced by substrate stiffness and the ECM macromolecules pre-adsorbed onto the substrates. Calcein Blue (CB)-positive bone-nodule-like colonies were only observed on the stiff end of PDMS coated with fibronectin and gelatin, while oxygen-plasma-treated surfaces were entirely devoid of CB-positive colonies after 1 week of osteoinductive culture .A variety of functionalized vinyl monomers are commercially available or can be synthetically customized, rendering vinyl polymer-based hydrogels useful as structurally diverse scaffolds . The ostCylindrical PV alcohol (PVA)/HA hydrogel prepared with a liquid nitrogen–contacting gradual freezing–thawing method to produce hydrogel with a wide range stiffness gradient (between ~20 kPa and ~200 kPa). Human BMMSCs cultured on PVA/HA hydrogel favored certain stiffness ranges to get differentiated into specific cell lineages: ~20 kPa for nerve cell, ~40 kPa for muscle cell, ~80 kPa for chondrocyte, and ~190 kPa for osteoblast . MoreovePolyesters are popular polymers that contain ester groups in the polymer backbones, enabling them to produce biomedical hydrogels that can undergo biodegradation . Poly (PHEMA), and Apacite  nanostructures (15 and 25 wt.%) with mechanical values (E ~ 7.109 MPa and σ ~ 0.414 MPa) provided a balanced microenvironment that resulted in osteogenic induction of human BMMSCs. Von Kossa staining, calcium content, and ALP results confirmed the highest bone cells’ differentiation on PCLPHEMA/25% Apacite nanocomposites .Polyacrylamide formed from only acrylamide subunits is nonionic. Copolymerizing it with other monomers such as 2-acrylamido-2-methylpropane sulfonate or acrylate forms anionic polyacrylamide, while cationic polyacrylamide could be synthesized upon copolymerization with dimethyl diallyl ammonium. Polyacrylamide substrate is bio-inert; thus, its surface must be conjugating with adhesive ECM proteins to allow for cell attachment ,198. PolUpon seeding human MSCs on 250-Pa polyacrylamide gels coated with a mixture of collagen type 1 and fibronectin, the progression of the cells throughout the cell cycle was prohibited despite the presence of serum. Conversely, the quiescent cells reentered the cell cycle when presented on a stiff polyacrylamide substrate (7.5 kPa). Moreover, the non-proliferative cells revealed an adipogenic differentiation potential upon culturing on 250-Pa gels in adipogenic media or an osteogenic potential into osteoblasts if transferred to a stiff substrate in the presence of osteogenic media . MicropaThe modulatory effect of extracellular matrix type and density on the mechanotransduction of stem/progenitor cells and the correlated integrin involved in signals translocation were assessed through conjugating each of the four major cell adhesion ECM proteins  on polyacrylamide hydrogels with tunable stiffness . The results revealed that increasing ECM ligand density alone can induce YAP nuclear translocation without changing substrate stiffness with a different optimized ligand density. Using antibody-blocking techniques for αvβ3-, α5-, and α2β1-integrins revealed the involvement of αvβ3-, α5-, and α2β1-integrins with fibronectin, while α5-integrin was further associated with collagen type I and IV. On the contrary, laminin was associated with α5- and α2β1-integrins. Moreover, altering ECM type resulted in modulation of human MSC osteogenesis confirmed by quantitative real-time (qRT)-PCR for Runx2 and ALP without changing substrate stiffness .The mechanotransduction role of FAK, α5/β1 integrin and Wnt-signaling pathways mediated by stiff matrices, in regulating osteogenic differentiation of human MSCs cultured on 62–68 kPa fibronectin-coated polyacrylamide hydrogels were further investigated. Throughout osteogenesis, gene and protein expressions of integrin α5/β1 were enhanced, together with the expression of signaling molecules FAK, p-ERK, p-Akt, GSK-3β, p-GSK-3β, and β-catenin. Antibody blocking of integrin α5 significantly downregulated the stiffness-induced expression of osteogenic markers  with associated downregulated expression of ERK, p-ERK, FAK, and β-catenin protein. Reciprocally, GSK-3β, p-GSK-3β, Akt, and p-Akt expressions were upregulated. The presence of the Akt inhibitor Triciribine reduced the expression of p-Akt and p-GSK-3β, whereas Akt, GSK-3β, and β-catenin were unchanged. These results emphasized the role of p-Akt in regulating the expression of p-GSK-3β on 62–68 kPa ECM during osteogenesis .MSCs cultivated on polyacrylamide hydrogels with elasticity (7.0 ± 1.2 and 42.1 ± 3.2 kPa) and coated with type I collagen in osteogenic medium revealed enhanced osteogenic differentiation potential on stiff substrates, with an upregulated expression of Runx2, type I collagen, and osteocalcin genes. On stiff matrices, Western blot analysis revealed an increase in mechanotransducers involved in osteogenic differentiation ROCK, FAK, and ERK1/2, whereas their inhibition resulted in decreased osteogenic markers’ expression. Furthermore, α2-integrin was upregulated on stiff matrices during osteogenesis, and its knockdown by siRNA hindered the osteogenic phenotype through FAK, ROCK, and ERK1/2. Therefore, it could be concluded that α2-integrin is involved in osteogenesis mediated by matrix stiffness .Additionally, upon culturing human MSCs on poly acrylamide-co-acrylic acid hydrogels grafted with RGDs, myogenic differentiation occurred at 13–17 kPa, while osteogenic differentiation was revealed at 45–49 kPa stiffness confirmed further with positive protein immunostaining of MyoD, as well as Osx, osteocalcin, and Runx2. Stiffer matrices grafted with BMP-2 mimetic peptide (E = 47.5 kPa) also induced osteoblast lineage commitment, having a similar effect as the ones grafted with RGDs. On the contrary, the osteogenic effect of BMP-2 mimetic peptides on MSCs was inhibited on very soft microenvironments (0.76–3.21 kPa) due to F-actin cytoskeleton reorganization that inhibited BMP-induced smad1/5/8 phosphorylation and subsequent differentiation of the cells into osteoblast-like cells .Umbilical cord (UC) MSCs attained similar behavior upon being cultivated on different stiffness  polyacrylamide gels coated with fibronectin. Quantitative RT-PCR results showed that soft matrices promoted adipogenic differentiation, as evident by upregulated expressions of adipocytic markers (PPARγ and C/EBPα). On the contrary, stiff matrices (48–53 kPa) enhanced the tendency of the cells to differentiate into muscles, as demonstrated by enhanced expression of desmin and MOYG. High stiffness substrates (62–68 kPa) significantly promoted the expression of osteogenic markers, such as Runx2, collagen type I, ALP, and osteocalcin .Moreover, the effect of mechanical loading and biomaterial stiffness on MSCs differentiation was investigated upon cultivating MSCs in osteogenic and adipogenic media on soft (23 ± 0.3 kPa) and stiff (111 ± 2 kPa) polyacrylamide as compared to PDMS (1.5 ± 0.07 MPa) either strained with 8% cyclic strain at 1 Hz or unstrained. Without strain, the expression of ALP was markedly higher on PDMS than on both polyacrylamide types. With 8% cyclic strain, ALP expression was upregulated in all groups, with the highest expression in soft polyacrylamide. Moreover, adipogenesis was the highest on the unstrained soft polyacrylamide, while it was significantly decreased on soft and stiff polyacrylamide when strained .Human MSCs were encapsulated within a 3D culture and grown on top of 2D culture biomimetic self-assembling peptide (SAP) hydrogel containing 1 mg/mL RGDs-functionalized peptide (KFE–RGD) at the shear moduli of 0.25, 1.25, 5, and 10 kPa. Changes in adipogenic and osteogenic gene expression were relatively modest with no visual signs of differentiation as mineral deposition. The cells maintained a fibroblast-like phenotype throughout the culture period. However, on introducing 1:1 mixed adipogenic/osteogenic induction medium, the stiff matrices of 10 kPa induced the most efficient osteogenesis, with alizarin red-stained calcium deposits .Indirectly 3D-printed “stiffness memory” poly(urea-urethane) (PUU)/POSS elastomeric nano-hybrid scaffolds with thermo-responsive mechanical properties that soften at body temperature by inverse self-assembling have been developed. The initial stiffness and subsequent stiffness relaxation (>10 kPa) of the scaffolds directed the proliferation and differentiation of human BMMSCs towards the osteogenic lineages on stiffer scaffolds over 4 weeks, as measured by immunohistochemistry, histology, ELISA, and qPCR, while soft substrates (<1 kPa) promoted MSCs’ chondrogenic differentiation . Table 1The world of biomaterials, specifically polymers, lingers to impact the biomedicine field. Various materials are currently under investigation to produce ECM with varying stiffness for tissue engineering. Matrix stiffness regulates the MSCs’ differentiation into mature specific cells by activating transcription factors that upregulate the genes responsible for the initiation and progression of particular cell linage differentiation. Rigid matrices lead to increased MSCs spreading and improved actomyosin contractility, promoting osteogenic differentiation. This enhanced potential is accompanied by increased Runx2, β-catenin, and Smad, implying the significant impact of mechanosensing of the matrix stiffness and its role in determining cell fate. The comprehensive signaling mechanisms by which micro-environmental stiffness controls the lineage specification of MSCs are still unknown. Additional research is needed to understand the variety of potential signaling forces involved in MSCs’ osteogenic differentiation that can lead to the development of new therapeutic modalities addressing bone disorders. Moreover, we believe that further research on the implicated mechanical and physical factors, such as topographic changes and external mechanical forces affecting cell properties, including cell shape and colony size, can offer a broader understanding of cell-fate determination.


This study aims to assess patient coverage, validity and data quality in the Swedish part of the International Enhanced Recovery After Surgery (ERAS) Interactive Audit System (EIAS).N = 12). Information registered in EIAS was compared with data from electronic medical records at each hospital to assess the overall coverage of EIAS. Twenty random-selected patients from each of the contributing centers were assessed for accuracy for a set of clinically relevant variables. All patients admitted to the contributing centers were included for the assessment of rate of missing on a selection of key clinical variables.All Swedish ERAS centers that recorded colorectal surgery data in EIAS between January 1, 2017, and December 31, 2017, were included (n = 1301) and the four 86.7% (n = 811). The average agreement for the assessed postoperative outcome variables was 96.5%. The accuracy was excellent for ‘length of hospital stay,’ ‘reoperation,’ and ‘any complications,’ but lower for other types of complications. Only a few variables had more than 5% missing data, and missingness was associated with hospital type and size.Eight hospitals provided complete information for the evaluation, while four hospitals only allowed assessment of coverage and missing data. The eight hospitals had an overall coverage of 98.8% in EIAS (This validation of the Swedish part of the international ERAS database suggests high patient coverage in EIAS and high agreement and limited missingness in clinically relevant variables. This validation approach or a modified version can be used for continued validation of the International ERAS database.The online version contains supplementary material available at 10.1007/s00268-021-06094-4. In order to receive continuous feedback on the quality of the clinical care, compliance to the ERAS protocol, perioperative data and outcome measures are consecutively recorded in the International ERAS® Interactive Audit System (EIAS)  the In conclusion, this validation of the Swedish part of the international ERAS database provides critical information for the development and adequate interpretation of multicenter studies in Sweden. The validation tool, or a modified version of it, can be used to validate other parts of the ERAS database to enable high-quality studies on an international scale.Supplementary file1 (DOCX 23 KB)Below is the link to the electronic supplementary material.


In adjusted analysis, group 1 children (PLM ratio ≤.90) were at higher risk of post-transplant in-hospital mortality . This association of the most undersized donors with recipient in-hospital mortality was similar when donor-recipient weight ratio<.88 or BSA ratio<.92 (lowest decile) were used instead. There was no difference in 1-year graft survival among groups. Utilizing donors with donor-recipient PLM ratio ≤.90 is associated with higher risk of early post-transplant mortality in pediatric HT recipients. However, this metric is not superior to donor-recipient weight ratio or BSA ratio for assessing size match.Previous analyses in pediatric heart transplant (HT) recipients using weight or height have not found donor-recipient size-mismatch to be associated with post-transplant mortality. A recent study in 3,215 normal US children developed an equation for left ventricular (LV) mass using body surface area (BSA). We assessed whether donor-recipient size match using predicted LV mass (PLM) is associated with post-transplant in-hospital mortality or 1-year graft survival. We identified 4,717 children  Transplant centers routinely provide a weight-range for an acceptable donor when listing a candidate for heart transplant (HT). This range is often 80%–200% of the recipient weight in children. Donor-recipient (DR) height match may also be considered when reviewing a donor offer. Size match using body measurements is essentially an attempt to match the donor and the recipient for their “normal” or “predicted” heart size to allow adequate mediastinal space for the donor heart and a donor heart that is able to support the recipient circulation after removal of the diseased heart. Previous analyses in pediatric HT recipients using weight or height to assess the effect of DR size match have shown either absent or only a marginal association of DR size-mismatch with recipient survival –3. This Until recently, the practice of selecting donor size in adult HT candidates has been similar to the pediatric practice. After investigators of a population-based study (Multi Ethnic Study of Atherosclerosis) in the United States (US) performed cardiac magnetic resonance imaging (MRI) in healthy adults to develop normative equations for left ventricular (LV) and right ventricular (RV) mass using age, gender, height and weight , 5, seveA recent Pediatric Heart Network study in 3,215 healthy, racially-diverse US children with adequate representation across the pediatric age range published an equation for left ventricular (LV) mass using body surface area (BSA) which can be used to estimate/predict LV mass for normal children with BSA of the HT recipient and the donor . BecauseThe specific aims of this study were 1) to assess the association of DR size match using predicted LV mass with post-transplant in-hospital mortality and 1-year graft survival in pediatric HT recipients and, 2), to compare its performance to the association of DR size-match using weight, height and BSA ratio with these outcomes in the same cohort.We identified all children <18 years old in the Organ Procurement and Transplant Network (OPTN) database who received first HT in the US between January 1st, 2000 and March 31st, 2015. Children who received heart re-transplant or multi-organ transplant were excluded. We also excluded recipients with missing weight or height for the recipient or the donor. The OPTN database includes baseline information at transplant and follow-up data in all recipients in the US submitted by transplant centers. These data are supplemented with death data from the social security master death file and are provided as de-identified data by the United Network for Organ Sharing to investigators. Post-transplant follow-up was available until March 31st, 2016, allowing 1 year of follow-up in all study children. The institutional review board determined that the study was not human subjects research as defined by US federal regulations.This was a retrospective cohort study. Data were analyzed during March-December 2020. Two primary endpoints, post-transplant in-hospital mortality and graft loss during the first post-transplant year (time to death or re-transplant) were evaluated. The primary predictor was donor-recipient PLM ratio (=donor PLM divided by recipient PLM). This report follows the Strengthening the Reporting of Observational Studies in Epidemiology reporting guideline .0.3964 x weight (kg)0.5378] for both the recipient and the donor. This was used to generate PLM for all recipients and for donors up to 18 years old as follows 1.25Predicted LV mass = 53.02 × BSAFor donors >18 years old , we calculated donor PLM using the MRI-derived adult equation :0.54 (m) × Weight0.61 (kg)), where a = 6.82 for women, 8.25 for men.PLM (>18 years) = a × Height2) using serum creatinine and the modified Schwartz equation , Black (non-Hispanic Black), Hispanic or Other. Renal function was analyzed as estimated glomerular filtration rate , health insurance , dialysis and the dates of transplant, death or re-transplant. For children with missing values of serum creatinine (2%) or bilirubin (7%), we used a multiple imputation technique to impute their GFR and serum bilirubin respectively using clinical variables at transplant and 10 imputations for each missing value .Baseline characteristics are presented as median  or number (percent). Study subjects were divided into five groups with increasing donor-recipient PLM ratio consisting of 10%, 10%, 60% (reference group), 10% and 10%, respectively of study subjects. This distribution was chosen to evaluate both ends of the size match spectrum (undersized and oversized donors including possible U-shaped relationship), with a reasonable sample size in exposure groups to detect the association of DR size mismatch with outcomes if present and to detect any trends with outcomes on either end, assuming the middle 60% would be the best matched group by size. The groups were compared for the distribution of baseline demographic and clinical (recipient and donor) variables as well as the distribution of DR weight-, height- and BSA ratio using chi-square tests or Kruskal-Wallis tests, as appropriate.A multivariable logistic regression model using variables at HT and forward selection was developed for post-transplant in-hospital mortality retaining variables significant at the 0.10 level based on a likelihood ratio test; all variables in Kaplan Meier curves and log rank test were used to compare cumulative 1st year post-HT graft loss (death or re-HT) among the five groups. Multivariable Cox models were built to assess the association of size match using different metrics for 1 year graft survival, adjusted for baseline characteristics and year of transplant. For each model, the middle 60% subjects for the specific metric were used as the reference group.We performed a sensitivity analysis by repeating/limiting all multivariable analyses only in recipients who received a heart from a donor up to 18 years old so that both the recipient and the donor PLM were derived using the PHN equation.p < 0.05 defined statistical significance. The authors had full access to the data and take responsibility for its integrity. All authors have read and agree to the manuscript as written.Data were analyzed using SAS version 9.4  and Stata version 15 . All statistical tests were two-sided and a During the 15 year study period, 4,797 children <18 years old underwent primary HT in the US. Of these, 23 received a multi-organ transplant and 57 had missing weight or height for the recipient or the donor and were excluded. The remaining 4,717 children in whom PLM could be estimated for both the recipient and the donor formed the study cohort. Of these, 30% were infants <1 year old, 55% were male, 52% had cardiomyopathy, 44% had congenital heart disease and 21% were on a mechanical support  at transplant. Overall, 85% of these recipients received a heart from a pediatric donor <18 years old, the remaining being adult donors.p for trend<.001 for all, Donor-recipient PLM ratio ranged from 0.55 to 3.40 in the study cohort and was 0.55–0.90 in group 1 (most undersized donors), 0.91–1.00 in group 2, 1.01–1.60 in group 3 (reference group), 1.61–1.83 in group 4 and 1.84–3.40 in group 5 (most oversized donors), respectively. The distribution of baseline recipient and donor characteristics among the five groups with increasing donor-recipient PLM ratio is illustrated in p = .10). In multivariable analysis, recipient age, cardiac diagnosis, ventilator or mechanical support, renal dysfunction, hepatic dysfunction and donor ischemic time were all significantly associated with in-hospital mortality (p = .03] compared to the reference group (PLM group 3) whereas HT recipients in PLM group 2 , group 4  or group 5  were not at higher risk of in-hospital mortality. There was no significant interaction of PLM group 1 with any risk factor in the multivariable model.Overall, 283 (6%) children died prior to hospital discharge. In-hospital mortality was 8.3%, 4.9%, 5.5%, 7.0% and 6.8%, respectively in PLM Groups 1–5 , defined as initiation of extra-corporeal membrane oxygenation support within 2 days following transplant . The area under the receiver operating characteristic curve for the multivariable models for in-hospital mortality was identical (c statistic = 0.81) whether donor-recipient PLM ratio, weight ratio or BSA ratio was used in the multivariable model.There was a borderline increased risk of in-hospital mortality in adjusted analysis in recipients in the lowest decile of DR weight ratio defined as <0.88 , weight ratio  or BSA ratio  were used to define the most undersized decile of donors.p = .14, log rank test).In a multivariable Cox model, risk factors associated with graft loss during the first post-transplant year included recipient age, cardiac diagnosis, black race, hemodynamic support at transplant, renal or hepatic dysfunction and donor ischemic time . PRA wasA longstanding wisdom when evaluating a donor for HT is to avoid undersized donors due to the risk of primary graft failure in the recipient. In this study of US children who received primary HT in the US during a 15 year period, we calculated donor and recipient PLM using a recently described equation in normal US children. We found that 10% of HT recipients received a heart with donor-recipient PLM ratio of ≤.90. These children were at 55% higher risk of post-transplant in-hospital mortality compared to the reference group in adjusted analysis. When size match was assessed with PLM ratio as a continuous variable, the adjusted risk of in-hospital mortality was higher the more undersized the donor heart. Recipients who received oversized hearts were not at increased risk. There was no association of DR size mismatch with 1 year graft survival suggesting that the risk associated with using hearts from undersized donors is short-term. The association of undersized donors with in-hospital mortality was also demonstrable to a comparable degree when size match was assessed using DR weight ratio or donor-recipient BSA ratio. These findings are different from analyses in adult HT recipients where use of predicted heart mass formula to assess DR size match is superior to using body measurements. Considering the lack of superiority of PLM ratio and the simplicity in using DR weight ratio or BSA ratio when evaluating size match, it is difficult to justify a routine use of donor-recipient PLM ratio when evaluating donors. DR height ratio was not associated with post-transplant in-hospital mortality or 1 year graft loss.We were inspired to ask the study question after several studies in adult heart transplantation , 15 and p = .09) , the association when expressed as donor-recipient PLM ratio - while performing similar to the DR body size ratios—provides a physiologic correlate for the risk associated with undersized donors. If the ultimate goal is to match the donor and the recipient for their predicted heart mass, the weight ratio and BSA ratio appear to be reasonable surrogates in children unlike in adults. The difference between adults and children in this regard may be best explained by the gender difference in calculation of predicted heart mass. It is notable that the pediatric LV mass equation is the same in boys and girls with similar BSA. In contrast, there is a significant difference in values for the predicted LV (and RV) mass by gender such that with the same body weight and height as that of a man, the LV mass in a woman calculates to 82.7% of that man, thus explaining the increased risk of mortality in adult male recipients when receiving HT from a female donor. This is the likely explanation for a much superior performance of heart mass calculation in adult HT recipients over body measurements whereas they appear to perform no differently in children.The size match categories in our analysis were chosen to understand if either undersized or oversized donors were associated with worse outcomes compared to the reference group and were guided in part by an adult study where seven equal-size groups were analyzed for size match with just the middle group being the reference group . With a This study has several limitations. First, this was a retrospective study using registry data with inherent limitations of such data. However, submission of these data to UNOS by centers is required, the data are used on an ongoing basis for organ allocation and are periodically audited by UNOS, thus allowing safeguards to data quality. Second, although we describe increased mortality risk in 10% of HT recipients with the most undersized donors, the category as defined is somewhat arbitrary and the risk is continuous with a higher risk the more undersized the donor rather than present at a specific PLM ratio. Third, donor-recipient size mismatch may be clinically reasonable in a cachectic or an overweight recipient in whom ideal body weight, such as the 50th percentile weight for current height, instead of the current weight, may be considered. We did not analyze such examples in this study for statistical reasons.Pediatric HT recipients who receive hearts from donors with donor-recipient PLM ratio ≤.9 are at significantly increased risk of early post-transplant mortality. However, this metric is not superior to donor-recipient weight ratio or BSA ratio when assessing size match as this association is also seen when evaluating donors and recipients using weight ratio or BSA ratio. These findings should be considered during decision making when assessing potential donors for HT candidates.A longstanding wisdom when evaluating a donor heart for a heart transplant candidate is to avoid undersized donors due to the risk of primary graft failure. However, previous analyses in pediatric heart transplant recipients using weight or height have not found donor-recipient size-mismatch to be associated with post-transplant mortality. A recent study in healthy US children using echocardiography described an equation for LV mass using body surface area. We assessed if donor-recipient size mismatch assessed using predicted LV mass ratio is associated with post-transplant mortality. In a study of 4,717 pediatric heart transplants in the US over 15 years study duration, we found that children with donor-recipient predicted LV mass ratio <.9 (10% with most undersized donor hearts) were at higher risk of post-transplant in-hospital mortality adjusted for other risk factors. The metric was not superior to donor-recipient weight ratio or BSA ratio for assessing size match however because recipients in the lowest decile of donor-recipient weight ratio or body surface area ratio were also at increased risk of in-hospital mortality.


Attention to graphenedispersions in water with the aid of naturalpolymers is increasing with improved awareness of sustainability.However, the function of biopolymers that can act as dispersing agentsin graphene dispersions is not well understood. In particular, theuse of starch to disperse pristine graphene materials deserves furtherinvestigation. Here, we report the processing conditions of aqueousgraphene dispersions using unmodified starch. We have found that thegraphene content of the starch–graphene dispersion is dependenton the starch fraction. The starch–graphene sheets are few-layergraphene with a lateral size of 3.2 μm. Furthermore, topographicalimages of these starch–graphene sheets confirm the adsorptionof starch nanoparticles with a height around 5 nm on the graphenesurface. The adsorbed starch nanoparticles are ascribed to extendthe storage time of the starch–graphene dispersion up to 1month compared to spontaneous aggregation in a nonstabilized graphenedispersion without starch. Moreover, the ability to retain water bystarch is reduced in the presence of graphene, likely due to environmentalchanges in the hydroxyl groups responsible for starch–waterinteractions. These findings demonstrate that starch can dispersegraphene with a low oxygen content in water. The aqueous starch–graphenedispersion provides tremendous opportunities for environmental-friendlypackaging applications. Owingto these properties, graphene is extensively studied for various applicationsin a wide range of fields, such as composites, energy storage, datacommunication, electronics, sensors, and biomedical technologies.1 Since the first isolation of graphene,2 there has been a growing interest in understandingits surface chemistry4 and developing more sustainabledispersing systems.6 For instance, dispersing graphenein water instead of organic solvents can offer safer handling andgreater biocompatibility, while diminishing adverse impact on humanhealth and the environment. However, graphene is not dispersible inwater due to a large mismatch between the low surface energy of grapheneand the high surface tension of water.7 Moreover, attractive interactions  that can exist between adjacentgraphene sheets lead to restacking and eventually aggregation. Therestacking of graphene sheets can be prevented by introducing electrostaticor steric repulsion between the sheets using amphiphilic dispersingagents.10 Great efforts have been devoted to the stabilizationof graphene by noncovalent modifications using surfactants, whichis a relatively simple process from an industrial perspective. However,the use of surfactant systems introduces challenges such as foamingand surface migration in polymer matrices that can complicate mostgraphene applications.12 Furthermore, the increasing occurrenceof commercial synthetic surfactants and their degraded products inthe environment is receiving more attention due to their adverse effectson the ecosystem.14 Attractive alternatives to surfactantsfor the preparation of aqueous graphene dispersions are biopolymers.Among biopolymers, starch is one of the most abundant polymers innature with attractive rheological, adhesive, and film properties.For decades, starch has been industrially extracted from plant-basedsources on a large scale and processed for a wide range of industrysectors, such as food, cosmetics, paper, textiles, and pharmaceuticals.15 Furthermore, the renewable and biodegradablenature of starch makes it an attractive candidate as a green dispersingagent. In addition, graphene is an excellent filler that can be usedto strengthen the electrical, mechanical, and barrier properties ofthe starch. The combination of these two materials provides tremendousopportunities for various novel applications. In general, starch iscomposed of two polysaccharides known as amylose and amylopectin.The amylose is a linear polysaccharide of d-glucose unitsthat are joined by repeating glycosidic α-1,4 links and canthereby self-associate into helical conformations with a hydrophobiccore. In contrast, the amylopectin is a branched polysaccharide withdouble helical side chains that are joined to its backbones by glycosidicα-1,6 links. The ratio of amylose and amylopectin contributesto the semicrystalline nature of the starch granule and its properties.Grapheneis a two-dimensional (2D) network of sp16 andamylopectin17 in aqueous systems. The stabilizationof the SWCNTs was assigned to hydrophobic interactions between theSWCNT surface and the helical core of the amylose, while that of thehydrophobic sites on the amylopectin. Furthermore, the SWCNTs werefunctionalized by different polar groups via acid treatments to increasetheir hydrophilicity and interfacial adhesion with starch.19 However, since the rise of graphene,20 the interest shifted toward graphene and its derivatives. In thepioneer work of Li et al. and Ma et al., an aqueous solution of grapheneoxide (GO) was directly added to starch21 and starch/chitosan mixture,22 respectively.A distribution of oxygen groups on the GO surface increases its hydrophilicityand thereby eliminates the need for dispersing agents in aqueous systems.However, impurities on the surface substantially limit the propertiesof graphene. To restore these properties, starch was also exploredas a reducing agent to remove these oxygen groups and functionalizethe resulting reduced GO (RGO).24 In general, the interfacialinteraction between GO/RGO and starch is commonly attributed to hydrogenbonding via their oxygen groups, respectively.27 While theseoxygen-rich GO/RGOs resemble the geometric dimension of pristine graphene,their surface properties and functions are substantially differentcompared to the pristine graphene. Therefore, the same stabilizationmechanism is not expected in the dispersion of graphene. To the bestof our knowledge, the earliest work on graphene as a starting materialreported starch-based films that showed improved properties at lowgraphene loading, while the opposite trend was observed at high loadingas a result of graphene aggregation.29 In the lastfew years, the increasing availability of commercial graphene powdershas sparked interest to explore graphene as a filler in starch-basedformulations for various applications. However, the dispersion ofgraphene in the presence of starch is not well understood, in particular,the processing conditions and long-term stability.In the last 20 years, starches from a wide range of botanical sourceshave been explored as dispersing agents for allotropes of carbon.For instance, the earliest work focused on the stabilization of single-walledcarbon nanotubes (SWCNTs) by amyloseIn this paper,we report the dispersion of graphene in water usingstarch nanoparticles as a dispersing agent. These starch nanoparticleswere prepared from unmodified starch granules via gelatinization andultrasonication. To gain a better understanding of the stabilizationmechanism, we used a starting graphene powder with a low oxygen contentwith close resemblance to that of pristine graphene and studied theeffects of different processing conditions to obtain stable starch–graphenesheets. The findings demonstrate that starch can disperse graphenein water without oxygen-rich functional groups.230 Similarly,starch references  also give featureless absorptionspectra, thus demonstrating that the features in the absorption spectraare exclusively originated from the dispersed graphene. As can beseen in the corresponding photographs in The relative amount of graphene that couldbe stabilized by starchnanoparticles was estimated via ultraviolet–visible (UV–vis)absorbance. UV–vis absorption spectra along with digital photographsof the starch–graphene dispersion are shown in The processing conditions of thestarch–graphene dispersionwere then systematically investigated in three steps, as summarizedin 32 The starch granules (gray dashed) and the starch nanoparticles (graysolid), in contrast, show three decomposition regions. First, 25–170°C (I) is the weight loss associated with the dehydration ofweakly adsorbed water. Second, 200–400 °C (II) is thedepolymerization of the starch backbone and accounts for the largestweight loss. Finally, third, 400 °C (III) and higher is the formationof carbonaceous residues, such as aromatic, aliphatic, and aldehydegroups.38 Among these carbonaceous residues, the aromatic groups are the dominantresiduals after 600 °C.39 Therefore,we estimated the graphene content based on the residual weight differencebetween the starch–graphene and the starch nanoparticles inthe flat region of the derivative thermogravimetric (DTG) curves at700 °C. This yields a graphene concentration of 0.48 mg/mL inthe purified starch–graphene dispersion and an attenuationcoefficient of 3384 m–1 mg–1 mL.To find the graphene concentration via optical absorbance, theattenuation coefficient for graphene is needed. For graphene, thiscoefficient was experimentally determined by constructing a calibrationcurve (S1) of the graphene absorbance against the graphene concentration.The total graphene concentration in this dispersion was then determinedby thermal analysis 3. As shoTmax) was lower for both thestarch–graphene sheets (312 °C) and the starch nanoparticles(312 °C) compared to the starch granules (318 °C). In general,the shift to lower Tmax is attributedto the higher surface area in the starch nanoparticles compared tothe native starch granules, thus making the nanoparticles more susceptibleto thermal decomposition. In the thermal studies by Aggarwal et al.,starch granules revealed lower Tmax afterhydrolyzation to more porous granules with a higher surface area.40 The higher surface area was also responsiblefor lower Tmax among different starchnanoparticles compared to their native granules. Qin et al. attributedthe higher surface area to reduced particle sizes and disordered polysaccharidestructures.41 At the molecular level, thelinear polysaccharide amylose is known to decompose at lower temperaturesthan the branched amylopectin due to lower molecular weight and structuraldifferences.43 As demonstrated, the maximumpeak at Tmax can provide insights intothe starch surface characteristics and composition. In our work, themaximum peak of the starch–graphene is broader and asymmetric,which indicates that the decomposition of the starch nanoparticlesoccurs over a wider temperature range and is less volatile. This asymmetryis not observed in peak of the pure starch nanoparticles despite thesame processing conditions. The processing of ultrasonication is knownto cleave the polysaccharide chains, in particular, debranch the amylopectin,thus increasing the relative content of free amylose. However, boththese polysaccharides can self-associate into more ordered structuresand gradually larger networks, thus increasing the dissociation temperature.Therefore, the different thermal behavior of starch nanoparticleson the graphene surface implies that the starch–graphene interactionmay sterically inhibit the free polysaccharides to self-associateinto otherwise preferred polymorphic structures.In addition, TGA can also offer insight into the compositionandthermal stability of the starch–graphene sheets. In the correspondingDTG curves 3B, we no45 The starch nanoparticles show a reduced the maximum peak to 40 nm,with a broader peak shape in the nanometer range. Furthermore, thestarch nanoparticles also show weaker peaks in the larger particlesize range. These weaker peaks could originate from various polymorphsof self-associated and cocrystallized starch polysaccharides. However,the starch–graphene sheets have a sharper size distributionwith a maximum peak at 3.2 μm, as expected after 30 min ultrasonication.The particlesize distribution of the starch–graphene sheetsand the starch references were studied by a laser diffraction method(LDM), as shown in 49 Interestingly, the nonstabilized graphene reference(black dashed) showed a negative zeta potential of −36 mV,while that of the starch–graphene sheets (black solid) wasslightly increased to −32 mV. As demonstrated, the adsorptionof starch nanoparticles reduced the net negative surface charge ofgraphene slightly. To the best of our knowledge, the zeta potentialof starch-stabilized graphene materials of such low oxygen contents(O < 1.5%) has not been extensively investigated. Up to this point,Zhu et al. first reported starch-stabilized RGO with a negative zetapotential that gradually increased from a minimum of −27.8up to −15.6 mV with a higher starch loading, in which the latterzeta potential value was close to that of the pure starch.50 Furthermore, Narayanan et al. also reportedstarch-stabilized RGO with a negative zeta potential that increasedfrom −30.5 up to −21.5 mV with a higher starch loading.51 As reported by the authors, the higher starchloading gradually reduced the surface charge of RGO and consequentlyweakened the electrostatic repulsion between the sheets, thus resultingin aggregation. In our work, the graphene reference was easily aggregateddespite its high magnitude of the zeta potential at −36 mV,while the starch–graphene sheets of lower magnitude of −32mV remained stable. This implies that the stabilization of graphenewas mainly attributed to the adsorbed starch nanoparticles ratherthan the surface charge, from which the magnitude of the zeta potentialalone proved insufficient. The understanding of the surface chargeson the RGO and other graphene derivatives is established, in whichthe negative charges are primarily attributed to different ionizableoxygen groups present on the sheets.52 However,the origin of the negative charges on the graphene sheets is stillan ongoing debate. Several authors have also reported graphene inwater with a negative zeta potential within the range of −45and −30 mV at neutral pH.56 The authors have explained theorigin to possible oxygen groups introduced at the graphene edgesduring ultrasonication54 and asymmetric adsorption ofwater ions on hydrophobic surfaces.57 Overall, wedemonstrate that the adsorbed starch nanoparticles play a more importantrole in the stabilization of graphene than the net negative surfacecharge.To understand the stability of the starch–graphenesheets,the zeta potential of the starch–graphene dispersion is comparedto that of the control samples that were prepared and measured underthe same conditions 4B. The s58 A similar morphology of starch nanoparticles has also beenobserved by means of other preparation methods, such as chemical hydrolysis,59 enzymatic hydrolysis,60 and precipitation.61The preparation of the starch nanoparticles via ultrasonicationwas systematically studied on glass substrates by optical microscopy,as displayed in The morphology of thestarch–graphene sheets was then studiedby high-resolution imaging using SEM, as shown in –1 (D peak), 1580 cm–1 (G peak), and 2700 cm–1 (2D peak). Among these peaks, the D peak is relatedto the breathing vibrations of aromatic carbon rings and becomes activeby disorders, such as grain boundaries, vacancies, or sp3-related defects. The G peak from the in-plane stretching of carbonatoms in the ring is a common feature in all sp2-hybridizedcarbon systems.62 Therefore, the degreeof structural disorders in the graphene sheets can be estimated bythe intensity ratio I(D)/I(G) ofthese two peaks. In our work, the I(D)/I(G) of the graphene reference is 0.4 and that of the starch–graphenesheets increased to 0.6 after stabilization by starch nanoparticles,thus indicating an increased degree of disorders. Furthermore, thenature of these disorders can be probed by the intensity ratio ofthe D peak over a weak shoulder peak around 1620 cm–1 (D′ peak). These two peaks are both disorder-related peaksfrom the intervalley scattering and intravalley scattering, respectively.62 The values of the intensity ratio I(D)/I(D′) from these two peaks can be usedto describe different types of disorders that are distinguished at3.5 (boundaries), 7 (vacancies), and 13 (sp3 defects).63 In our work, the height of the D′ peakwas read from a deconvoluted peak shape using the Voigt function.As a result, the intensity ratio I(D)/I(D′) of the starch–graphene and the graphene referenceis 2.4 and 2.6, respectively. The relatively low intensity ratio impliesthat the disorders on the graphene sheets mostly originate from boundaryeffects, such as various surface features and edges. The density ofsuch disorders can increase during ultrasonication, in particular,the density of edges as a result of reduced particle size. Moreover,the effects of disorders are known to change the peak positions andshape of both the G peak and the 2D peak.65 Therefore, the use of these two peaks to estimate the number ofgraphene layers requires caution. Gupta et al. and Wang et al. reporteda shift toward the higher frequencies up to 1587 cm–1 for a defect-free monolayer graphene sheet.67 According to this concept, the peak position of the starch–graphenesheets at 1584 cm–1 reflects bilayer graphene. Alternatively,the 2D peak is the overtone of the D peak and does not require anydisorders to be activated. In this concept, the number of graphenelayers can instead be estimated based on the full width at half-maximum(FWHM).68 For the starch–graphenesheets, the 2D peak was fitted into a Lorentzian peak with an FWHMof 66 cm–1, thus reflecting five-layer graphene.As demonstrated, the estimation can vary as a result of various typesof disorders. Overall, the Raman spectra indicate that starch–graphenesheets are within the range of few-layered graphene sheets with arelatively low amount of disorders from boundaries.The structural changes in thestarch–graphene sheets bythe stabilization of starch nanoparticles were studied by Raman spectroscopy.For this purpose, the Raman spectra of starch–graphene arecompared to a nonstabilized graphene reference that was prepared withoutstarch under the same conditions 6B. UnderThe morphologyof the starch–graphene sheets was furtherstudied in STEM using both the bright-field (BF) and the HAADF detectors,respectively. In the BF image 6C, a repThe starch nanoparticles on the graphene surface were further studiedat the nanoscale using AFM. The topographical image of a starch–graphenesheet 7A iscom–1 in the starch–graphene,which is not present in the spectra of the starch references. Thisadditional peak is tentatively attributed to C=C stretchingvibrations in conjugated aromatic rings, thus indicating the presenceof graphene.51 Second, the presence ofgraphene also changed the vibrational modes of starch. The peaks ofstarch are typically identified at 1149 cm–1 ,1078 cm–1, and 995 cm–1 (C—O—Hbending).69 Among these peaks, the maximumpeak at 995 cm–1 was substantially narrowed andshifted to the higher wavenumber of 1019 cm–1 bygraphene. To investigate this peak further, we performed deconvolutionand observed three overlapping peaks centered around 1047, 1022, and995 cm–1, as shown in –1 is sensitive to the degree of orderedstarch structures, whereas the peak at 1022 cm–1, in contrast, is associated with amorphous structures. Finally,the peak at 995 cm–1 is mainly associated with theintra- and intermolecular hydrogen bonding of the hydroxyl group atC(6)—O—H, which makes this peak sensitive to the watercontent.70 Commonly, the intensity ratioof 1047/1022 and 995/1022 are indicators of the short-range crystallineand hydrated starch structures, respectively , the amountof bound water is substantially lower in starch–graphene thanthat in the starch references, thus supporting the reduced abilityof starch nanoparticles to retain water. In addition, a prominentpeak at 3307 cm–1 is mainly attributed to the vibrationalstretching of free, intramolecular, and intermolecular interactionsof the hydroxyl groups in starch. Therefore, peak shift and broadeningto the shorter wavenumbers have been used as an indicator to detecthydrogen bonding between graphene derivatives and starch. However,in our work, the peak position varied only slightly in the increasingorder: starch granules (3307 cm–1), starch–graphene(3316 cm–1), and starch nanoparticles (3318 cm–1). Similarly, the peak shapes remained the same withan average FWHM of 363 ± 4 cm–1. As demonstrated,the interfacial interaction between the graphene and the starch isnot evident for hydrogen bonding, thus pointing to other noncovalentinteractions that are more thermodynamically favored.72Further evidence to support this observation canbe found at thepeak of 1640 cmBased on the overall findings, we conjecture a mechanismfor starch-stabilizedgraphene dispersions, as illustrated in 3–1. These findingsconclude that graphene without oxygen-rich functional groups can bedispersed in water, thus providing more insights into the preparationof environmental-friendly aqueous graphene dispersions. The combinationof these two excellent materials, graphene and starch, in the aqueousdispersion provides tremendous opportunities for packaging applications.Aqueousgraphene dispersions were prepared using starch nanoparticlesas a dispersing agent. The effectiveness of these starch nanoparticleswas mainly dependent on the initial starch concentration and sonicationtime. The optimal concentration between starch and graphene was identifiedat 20 times more starch than graphene and the sonication time of 30min. The obtained starch–graphene sheets were mainly few-layergraphene in the size range of 3.2 μm with a relatively low amountof defects. At the nanoscale, the topographical images of the starch–graphenesheets confirmed that starch nanoparticles decorated on the graphenesurface with a height around 5 nm. These starch nanoparticles on thegraphene surface provided steric repulsion against the attractivevan der Waals forces acting on adjacent graphene sheets and consequentlyextended the dispersion stability for up to 1 month. Moreover, thestarch–graphene interaction revealed that the graphene inhibitedthe ability of starch to retain water. The reduced starch–waterinteraction was reflected in the substantial changes to the C—O—Hvibrational modes of starch at 995 cm44.1Graphene powderswere purchased from Avanzare Innovacion Tecnologica, S.L. . This graphene grade is an RGO with a low oxygen content (<1.5%),as provided by the supplier. Unmodified corn starch powder (S4126)containing 27% amylose and 73% amylopectin was purchased from Sigma-Aldrich.Milli-Q water was used for all the aqueous dispersions.4.2The starch nanoparticles were prepared from nativecorn starch via gelatinization and ultrasonication. In general, allaqueous suspensions of starch powder in the concentration range of0.5–20 mg/mL were heated at 95 °C for 20 min under constantstirring at 1000 rpm. After the gelatinization step, the suspensionswere treated by ultrasonication  for 3 minand then cooled to room temperature. Subsequently, the suspensionswere centrifuged at 1500 rpm for 5 min, whereby the supernatants containingthe starch nanoparticles were extracted. After obtaining more clearstarch solutions, a fixed amount of 5 mg starting graphene powderwas added and then dispersed by ultrasonication. The optimal processingconditions of the starch–graphene dispersion were investigatedby varying both the initial starch concentration  and the ultrasonication time . The final starch–graphene dispersion was purifiedfrom residual graphene aggregates by centrifugation at 1000 rpm for10 min and then collecting the supernatant. The resulting graphenedispersion was used for all characterizations.4.3A =αlc. In this expression, the absorbance (A) is proportional to the attenuation coefficient (α),light path length (l), and the concentration (c). The unknown attenuation coefficient, α, for starch–graphenedispersions was extracted by plotting the absorbance per light pathlength at the wavelength of 660 nm, (A660/l) against the graphene concentration, c, and then reading the slope from a fitted linear regression.For this purpose, an aliquot of the starch–graphene dispersionwas serially diluted into six samples, from which the absorbance ofeach sample was measured. To find the graphene concentration in thesedilutions, a known volume of the aliquot was first dried in an ovenat 105 °C for 24 h. The remaining solid content from this aliquotwas weighed after cooling to calculate the total concentration ofboth the starch and the graphene, in which the graphene part was estimatedby TGAUV–visabsorption spectra were recorded in quartz cuvettes using a UV–visspectrophotometer (PerkinElmer LAMBDA 650). Each sample was dilutedby a factor of 100 and then equilibrated for 30 s prior to measurement.The concentration of graphene was determined from the optical absorbance,according to Beer–Lambert’s law 4.4TGA analysiswas performed in 70 μL alumina crucibles usinga thermogravimetric analyzer .The dried sample size varied between 0.5 and 1 mg for graphene and2–6 mg for starch. Each sample was equilibrated in an ovenat 105 °C for 24 h prior to measurements. The measurements wererecorded under a nitrogen atmosphere at a constant heating rate of10 °C/min.4.5Particlesize analysis was performed using a laser-diffraction-based particlesize analyzer with a Hydro SV measurement cuvette . Each wet sample was added to the cuvette under constantstirring at 500 rpm until the obscuration level reached 5–10%for the starch–graphene and starch granules, while below 5%for the starch nanoparticles. For each sample, a total of five measurementswere recorded and then averaged. The volume-weighed particle sizedistributions are calculated by the software using Mie theory. Inthe calculations, different optical properties were used for the graphene(refractive index of 2.73 and absorption of 1.36) and the starch .4.6The surface chargeof the starch–graphene sheets was determined using a Zetasizer. The starch–graphene dispersion was dilutedby a factor of 100 (∼0.01 mg/mL) and then equilibrated in afolded capillary cell for 30 s prior to measurements. A total of sixmeasurements were recorded with a minimum of 20 runs and then averaged.Each sample measured a pH of 6.8 using a standard lab bench pH meter.4.72 substrates. The substrates were cleanedby ultrasonication in ethanol for 5 min followed by rinsing with waterand finally oxygen plasma treatment for 1 min to increase the surfacewettability. For the STEM sample, the same starch–graphenedilution was drop-cast onto a 200-mesh copper grid and then driedunder ambient conditions overnight prior to imaging. All images werecaptured in both BF and dark field (DF) by the STEM detector at 10kV.The opticalimages of the starch morphology were captured on glass slides usinga reflected light microscope  equipped witha Nikon 50× microscope objective. High-resolution images of thestarch–graphene morphology were captured using an SEM/STEM. For the SEM samples, the starch–graphenedispersion was diluted by a factor of 100 and then spin-coated oncleaned Si/SiO4.82 substrates under the same conditionsas the SEM samples. For each sample, the Raman spectra were recordedusing the excitation wavelength of 532 nm and a low laser power of2.5 mW. The typical integration time was in the range of 2 s. Theacquired spectra of an area were then corrected for cosmic rays andthen averaged in the WITec Project 5.1 software . Finally, the baseline corrections were performed using thePeak Analyzer in the OriginPro 2020 software.Raman analysisof the starch–graphene sheets was performed using a WITec alpha300 system . The starch–graphene sheetsand the nonstabilized graphene reference were prepared via spin-coatingon cleaned Si/SiO4.9Topographicalimaging of the starch–graphene sheets was performed in thePeakForce tapping mode using a MultiMode 8 (Nanoscope V controller)atomic force microscope . The surface ofthe starch–graphene sheets was scanned using a cantilever ScanAsyst-Air(Bruker) with a nominal spring constant of 0.4 N/m and a nominal tipradius of 2 nm. For comparison with the starch–graphene sheets,the control samples of both a starch reference and a nonstabilizedgraphene reference were prepared under the same conditions. The graphenereference without starch was prepared in an aqueous solution of 70vol % ethanol to obtain partially suspended graphene. For each sample,25 μL of a dilution (by a factor of 100) was drop-cast on afreshly cleaved mica surface and then spin-coated at 3500 rpm for1 min.4.10–1 and then averaged. The average spectrawere baseline corrected and then deconvoluted by Gaussian fit in theOriginPro 2020 software.Spectroscopic analysis of the chemical interactions between grapheneand starch was performed using an FTIR spectrometer in the ATR mode. Dried samples were first equilibratedin an oven at 105 °C for 24 h prior to measurements. For eachsample, a total of 16 scans were collected at a resolution of 4 cm