Smart Hydrogel Architectures for Sensors: Narrative Review
Abstract
1. Introduction
2. Methods
- Hydrogel combinations with carbon nanomaterials for force sensors in the field of medicine—recent reports.
- Nanomaterial-based sensing: application of carbon nanomaterials, MXene, metal-organic frameworks, plasmonic nanoparticles, quantum dots, and magnetic nanoparticles to increase sensitivity and enable new transmission mechanisms due to low detection limits down to single molecules, selectivity, and integration into implantable formats.
3. Hydrogel Application for Sensors
3.1. Supramolecular Chemistry and Hydrogel Sensors
3.2. Types of Hydrogels in Sensor Manufacturing
3.3. Nanomaterials in Hydrogel-Based Sensor Mmanufacturing
4. Nanomaterials for a Hydrogel Sensor
Hydrogel Sensor with Carbon Nanomaterials
5. Conclusions and Future Trends
Author Contributions
Funding
Institutional Review Board Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
| AA | acrylic acid |
| ANF | an aramid fiber |
| AgNWs | silver nanowires |
| AM | acrylamide |
| BC | bacterial cellulose |
| CaTPD | tannic acid–Ca2+ complex + polyacrylamide |
| CDs | coal-based carbon quantum dots |
| CH | conductive hydrogel |
| CL | carbonized lignin |
| CMC | carboxymethylcellulose |
| CNC | cellulose nanocrystal |
| CNCs@HA | hyaluronic acid (HA)-functional cellulose nanocrystals |
| CNN-LSTM | convolutional neural network-long short-term memory |
| CNT | carbon nanotubes |
| CPDs | carbonized polymer dots |
| CB[6] | cucurbit[6]uril-modified |
| CQDs | carbon quantum dots |
| DEMA | N,N-diethylacrylamide |
| DMDAAC | dimethyl diallyl ammonium chloride |
| EMI | electromagnetic interference |
| f-CNTs | functionalized carbon nanotubes |
| FRESH | the Free-Form Reversible Embedding of Suspended Hydrogels |
| GQDs | graphene quantum dots |
| GO | graphene oxide |
| HEC-Ac | hydroxyethylcellulose with citric acid |
| HEMA | hydroxyethyl methacrylate |
| LCD | liquid crystal display |
| κ-Car | κ-carrageenan single-network hydrogel |
| IPN | Interpenetrating polymer networks |
| MCNF | matrix, dual-cellulose hierarchical network |
| MD | molecular dynamics |
| ML | machine learning |
| NC | nanocellulose |
| NIPAM | N-isopropylacrylamide |
| NPs | nanoparticles |
| NDs | nano-diamonds |
| OH-MWCNTs | hydroxylated multi-walled carbon nanotubes |
| PAA | polyacrylic acid |
| PAA/CNCs/MWCNTs | polyacrylic acid/cellulose nanocrystals/multi-walled carbon nanotubes |
| PAAm/PVA | polyacrylamide/polyvinyl alcohol |
| PAM | polyacrylamide |
| PAM–PDA/Ga3+ | polyacrylamide–polydopamine/gallium(iii) |
| PAM/SA | polyacrylamide/sodium alginate |
| PAM/HACC | polyacrylamide/quaternary ammonium chitosan |
| PANI/3-ABSA-ENR | polyaniline/3-aminobenzenesulfonic acid-epoxidized natural rubber |
| PCM | multifunctional poly (vinyl alcohol)/chitosan/multi-walled carbon nanotube |
| PDMA | poly(N,N-dimethylacrylamide) |
| Pdots | polymer dots |
| PDA | polydopamine |
| PDA@CNT | polydopamine-encapsulated carbon nanotubes |
| PDMS | polydimethylsiloxane |
| PEI | polyethyleneimine |
| P(AA-HEMA)/St/rGO | poly (acrylic acid-2-hydroxyethyl methacrylate)/starch/reduced graphene oxide hydrogel |
| PHEAA | poly(hydroxyethyl acrylamide) |
| PNIPAM | poly(N-isopropylacrylamide) |
| P-NPs | polypyrrole-polydopamine-MnOx nanoparticles |
| PSCLE | PAM/SA/CNTs/LM/ethylene glycol hydrogel |
| PPy | polypyrrole |
| PVA | polyvinyl alcohol |
| PVA-CMC | PVA-sodium carboxymethylcellulose |
| PVA-SbQ | styryl-pyridinium-functionalized polyvinyl alcohol |
| PVA/HAp | poly(vinyl alcohol)/hydroxyapatite |
| PVA-LS | PVA-sodium lignosulfonate |
| PVA/SA | polyvinyl alcohol/sodium alginate |
| PVA/P(AA-NIPAM)-Fe3+ | polyvinyl alcohol/poly(acrylic acid-N-isopropyl acrylamide)-Fe3+ |
| PRGO | partially reduced graphene oxide |
| S | pressure sensitivity |
| SMA | stearyl methacrylate |
| SPBC | poly(vinyl alcohol) (PVA)/sodium alginate (SA)/cellulose nanofibers (CNFs)/sodium borate tetrahydrate |
| rGO | reduced graphene oxide |
| rGO-APBA | reduced graphene oxide-3-Aminophenylboronic acid |
| SA | sodium alginate |
| TA@AL | tannic acid-modified lignin |
| TA@CNF | tannic acid-encapsulated cellulose nanofibers |
| TA@CNC | tannic acid-coated nanocrystalline cellulose |
| TCR | temperature coefficient of resistance |
| VCL | vinylcaprolactam |
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| Hydrogel Type | Performance | Advantages | Limitations | Ref. |
|---|---|---|---|---|
| Natural origin | Low mechanical strength; Limited capacity; Ionic/covalent crosslinking; | Biodegradability; Biocompatibility | Fast degradation; Weak mechanical strength | [98,99] |
| Synthetic | Easy engineering; Good stiffness; durability | Durability; Repeatability; Customizable properties; | Toxicity of manufacturing bioactivity; poor adhesion | [100,101] |
| Hybrid: natural and synthetic | Improved strength; stiffness | Bioactivity; Biocompatibility; Reinforcement of structure; Combined synergic features of bioactivity with strength | Higher cost; Challenges in repeatability | [102,103,104] |
| Polymer | Nanomaterial | Parameters | Ref. |
|---|---|---|---|
| PAAm/PVA/TA@AL- +Fe3+ | Alkaline lignin nanoparticles | tensile strength of 115 kPa; strain of ∼900%; toughness of 0.45 MJ m−3; ionic conductivity = 0.75 S m−3 | [70] |
| A1-P6-PDMS | MWCNTs | GF = 27.63; elongation at break >140%; tensile strength >2.5 MPa | [143] |
| nano-carbon aerogel/BC-P(AM-SMA)-Al3+ | BC, nanocarbon | GF = 12.3ε1.27; GF = 5.10 50 % strain; GF = 229.1 at 1000 % strain; S = 31.33p−0.71; response = 83.3 ms; cycling stability over 1000 cycles; | [144] |
| P (AA-HEMA)/PDA@CNT | PDA@CNT | GF = 5.46; Compressive sensitivity = 53.13 kPa−1; piezoresistive performance = 54.18 kPa−1; stability over 2000 cycles; | [145] |
| PDA (P(AA-HEMA)/St/rGO | GO, rGO | GF = 5.1; 176 kPa tensile stress; 1258% strain; conductivity (from 3.82 to 3.64 S/m); heating rate of 0.8 °C/s; | [146] |
| PAM | Cellulose nanofibrils | GF = 13.66; stress = 0.75 MPa, strain = 1593%; conductivity = 2.29 S/m; | [147] |
| PAM, MCNF | Cellulose nanofibers | tensile strength = 116.86–277.84 kPa, strain = 1923–2029%, and toughness = 1.34–2.12 MJ m−3; | [148] |
| PVA, (PVA-Borax-GaInSn-MXene) | MXene nanosheets | GF = 5.32; toughness = 1350.2 kJ/m3; strain range = 617.46%; recovering ~580% strain; response time = 170 ms; adhesion = 75.6 kPa (peak stress) | [149] |
| PEI | GO | GF = 2.26; conductivity = 0.46 S/m; adhesion = 13.7 kPa; stress: 113.5 kPa; strain: 1672% | [150] |
| Starch, amylopectin, PVA | MXene | GF = 1.1; stretchability ∼6151%; sensing range up to 300%; | [151] |
| PAM/HACC | OH-MWCNTs; CNT = 0.02 wt% | GF = 16.04; conductivity = 0.31 S/m; tensile strength = 435 kPa; elongation at break = 2500%;. | [152] |
| PVA/P(AA-NIPAM)-Fe3+ | Ionic cross-linking | GF = 3.77; elongation = 475 ± 27%; tensile strength = 1.91 ± 0.05 MPa | [141] |
| Function | Polymer | Nanomaterial | Parameters of Sensor | Ref. |
|---|---|---|---|---|
| Vibration | PVA, PDA | rGO, GO | GF = 0.64; conductivity = 8.15 S m−1; response time = 350 ms; hydrogel’s adhesion = 0.89 kPa | [123] |
| Strain | PANI/3-ABSA-ENR | ENR nanospheres | GF = 21.6; tensile strength 10 MPa; response time = 230 ms; | [154] |
| Motion monitoring | BC + AM | BC-confined MXene | GF = 11.48 at 600–800%; mechanical resilience = 6.5 MJ/m3 Elongation ~1800%; conductivity = 435.6 mS m−1 | [103] |
| Force monitoring rehabilitation training | TPU | MWCNTs | GF = 10.065 0–3% strain range; GF = 5.419 in the 17–38%; GF = 0.219 at 38–62.80%; detection range = 0.039–2200 kPa; sensitivity = 0.029–9.912 kPa−1; recovery time: 50 ms–66 ms; durability 3735 cycles; | [155] |
| Motion sensor | PVA | PCM with MWCNTs | GF = 5.72; Sel = 13.2 mS/cm; durability over 500 cycles; | [156] |
| strain | PAA | CNCs/MWCNTs | GF = 7.07; tensile strength from 0.159 to 0.180 MPa; self-healing efficiency at 88.33%; swelling ratio = 39% | [157] |
| wearable epidermal sensor | chitosan | polydopamine-coated Mxene | GF = 1.73; specific capacitance = 373.41 mF/cm2, energy density = 74.67 μWh/cm2, capacitance retention = 82.43%; 5000 cycles, | [158] |
| human movement | PDMS/silicone rubber layers | Mxene/GO | sensitivity = 51.36; response time = 60 ms; strain detection lower limit of 0.1% | [159] |
| tensile | κ-Car/PAA/ | GO | strength, toughness, flexibility (σf = 0.370 vs. 0.075 Mpa, εf = 1361 vs. 602%, E = 0.069 vs. 0.015 Mpa, W = 2.58 vs. 0.21 MJ/m3, | [160] |
| Strain sensor | CDs/CL/PVA | Nano carbon dots | GF = 5.34; toughness = 18.83 MJ/m3; tensile strength of 7.5 Mpa; conductivity = 24.5 mS/m; break of 487%; 1000 s cycle | [161] |
| piezoresistive device, LCD | Photo-resin | MWCNT | resistivity of ∼7.2–7.7 Ω; GF = ∼1.1–1.2; TCR ∼−1.58%K−1 for zero strain at 300 K; ΔR/R0, of ∼9.6–10.4; | [162] |
| Motion/sweat | PVA | rGO-APBA | self-healing capability >89%; sensing detection range = 0–550%; detection limit = 0.96 μM; mechanical = 2.23 MPa | [163] |
| Resistive sensor for breathing pattern | PEDOT:PSS/PVA/SA | PVA crystalline peaks; liquid metal nanoparticles; carbon-based nanomaterials | electrical conductivity = 48.69 mS m−1; fracture strength = 45.38 kPa, ductility = 209.13%; detect small deformation (2.5%); temperature response = −2.43 Ω K−1, 7.91 mV K−1; | [164] |
| heart rate and pulse monitoring | poly (acrylic acid)-TA@CNF (PTCCG-Na+) hydrogel | CNTs | GF = 4.47; resistance = 2.82 Ω; capacitance: 422.545 mFcm−2; environmental tolerance = −40–80 °C; stability >2000 cycles conductivity = 72.88 mS cm−1 | [165] |
| real-time physiological monitoring and infection detection | alignate–gelatin ink | MXene vs. AuNP | Conductivity S = 0.44 S/m for AuNP-modified; S = 1.04 S/m for MXene; sensitivities in the range from −1.54–2.00% °C−1 | [166] |
| human movements in extreme environments | polyvinyl alcohol–guar gum | cellulose nanocrystals 99.0%, (156.02)n g/mol | GF = 2.72; tensile modulus = 2.11 MPa; 92.94% self-healing | [167] |
| Resistive strain sensor | PNIPAM/PPy//PAA-Fe3+/clay | PPy nanoparticles | electrical conductivity = 1.24 ± 0.04 S/m; GF = 3.44; | [168] |
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Petronienė, J.J.; Rasimavičius, T.; Viržonis, D.; Dzedzickis, A.; Bučinskas, V. Smart Hydrogel Architectures for Sensors: Narrative Review. Sensors 2026, 26, 3213. https://doi.org/10.3390/s26103213
Petronienė JJ, Rasimavičius T, Viržonis D, Dzedzickis A, Bučinskas V. Smart Hydrogel Architectures for Sensors: Narrative Review. Sensors. 2026; 26(10):3213. https://doi.org/10.3390/s26103213
Chicago/Turabian StylePetronienė, Jūratė Jolanta, Tadas Rasimavičius, Darius Viržonis, Andrius Dzedzickis, and Vytautas Bučinskas. 2026. "Smart Hydrogel Architectures for Sensors: Narrative Review" Sensors 26, no. 10: 3213. https://doi.org/10.3390/s26103213
APA StylePetronienė, J. J., Rasimavičius, T., Viržonis, D., Dzedzickis, A., & Bučinskas, V. (2026). Smart Hydrogel Architectures for Sensors: Narrative Review. Sensors, 26(10), 3213. https://doi.org/10.3390/s26103213

