surface active agent for flexible polyester foam to control cell openness
abstract
this comprehensive article explores the critical role of surface active agents (surfactants) in controlling cell openness during the production of flexible polyester foam. the discussion covers fundamental principles, mechanism of action, product parameters, performance characteristics, and comparative analysis with alternative technologies. special emphasis is placed on the chemical structure-performance relationship, with detailed tables presenting technical specifications and performance data from leading commercial products. the article incorporates findings from numerous international studies and provides practical recommendations for surfactant selection based on application requirements.
keywords: flexible polyester foam, surfactant, cell openness, foam stabilization, polyurethane chemistry
1. introduction
flexible polyester foams represent a significant segment of the polyurethane foam market, valued for their superior mechanical properties, durability, and versatility in applications ranging from automotive seating to furniture and bedding. the cellular structure of these foams—particularly the degree of cell openness—directly influences critical performance characteristics such as air permeability, comfort factor, compression set, and acoustic absorption properties.
surface active agents play a pivotal role in governing foam morphology during the polymerization and expansion processes. these specialized additives perform multiple functions:
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control of cell nucleation and growth
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stabilization of the expanding foam structure
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regulation of cell win opening (cell openness)
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prevention of cell coalescence and collapse
the global market for polyurethane foam surfactants was valued at approximately $1.2 billion in 2022, with projected growth to $1.8 billion by 2030 (grand view research, 2023). this growth reflects increasing demand for high-performance flexible foams with precisely tuned cellular structures.

2. chemistry of polyester foam surfactants
2.1 molecular structure and classification
surface active agents for flexible polyester foams typically belong to several chemical classes:
table 1: major classes of surfactants for flexible polyester foams
| class | representative structure | hlb range | primary function |
|---|---|---|---|
| polysiloxane-polyether copolymers | (ch₃)₃sio[si(ch₃)(r)o]ₙsi(ch₃)₃ r = -(ch₂)₃o(eo)ₓ(po)ᵧh |
2-14 | cell size control, stabilization |
| polyether-modified polysiloxanes | similar to above with higher eo content | 8-18 | cell openness regulation |
| organic surfactants | alkyl phenol ethoxylates, fatty alcohol ethoxylates | 10-16 | co-surfactants, processing aids |
| fluorosurfactants | fluorinated alkyl esters | 6-12 | ultra-fine cell creation |
source: modified from herrington & hock (2017), polyurethane foams
the most effective surfactants for cell openness control in polyester foams are typically polyether-modified polysiloxanes with carefully balanced hydrophilic-lipophilic properties. the molecular architecture—particularly the ratio of ethylene oxide (eo) to propylene oxide (po) units and their distribution along the siloxane backbone—determines the surfactant’s performance characteristics.

2.2 mechanism of action
surfactants influence cell openness through three primary mechanisms:
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nucleation control: lowering surface tension promotes formation of numerous small bubbles rather than fewer large ones. the nucleation density directly affects final cell structure (kim et al., 2021).
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film stabilization: during foam rise, surfactants adsorb at the gas-liquid interface, providing marangoni elasticity that prevents premature bubble rupture (saint-michel et al., 2018).
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win control: as cells approach their maximum expansion, surfactants facilitate controlled opening of cell wins through interfacial tension modulation. this process is particularly crucial for achieving desired openness percentages (figure 1).
figure 1. mechanism of cell win opening influenced by surfactant action
(concept adapted from zhang et al., 2020, journal of cellular plastics)
3. critical performance parameters
3.1 product specifications
leading commercial surfactants for flexible polyester foams exhibit the following typical parameters:
table 2: technical specifications of representative surfactants
| parameter | unit | range | test method |
|---|---|---|---|
| appearance | – | clear to slightly hazy liquid | visual |
| viscosity @25°c | mpa·s | 200-1500 | astm d445 |
| density @25°c | g/cm³ | 1.01-1.08 | astm d4052 |
| flash point | °c | >100 | astm d93 |
| water content | % wt | <0.5 | karl fischer |
| hydroxyl value | mg koh/g | 10-80 | astm d4274 |
| silicon content | % wt | 2-8 | icp-oes |
| ph (5% solution) | – | 6-8 | astm e70 |
*data compiled from product datasheets of , , and shin-etsu (2023)*

3.2 performance characteristics
the effectiveness of surfactants in controlling cell openness can be quantified through several key metrics:
table 3: performance characteristics versus surfactant type
| surfactant type | cell size (μm) | open cell content (%) | air flow (cfm) | foam density (kg/m³) |
|---|---|---|---|---|
| standard polysiloxane | 300-500 | 60-75 | 2.0-3.5 | 25-35 |
| high-openness grade | 400-600 | 80-95 | 4.0-6.5 | 24-32 |
| fine-cell grade | 150-300 | 50-65 | 1.0-2.0 | 28-38 |
| fluorosurfactant blend | 100-200 | 40-60 | 0.5-1.5 | 30-40 |
*test conditions: conventional polyester polyol system, water-blown, index=105, 1.5 pphp surfactant*
source: internal testing data from major foam producers (2022)
the relationship between surfactant structure and foam properties has been extensively studied. research by mondal & khakhar (2019) demonstrated that:
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longer polyether chains increase cell openness (r²=0.87)
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higher eo content improves air flow (r²=0.92)
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branched architectures enhance stabilization (p<0.05)
4. advanced formulation considerations
4.1 interaction with other formulation components
surfactant performance is significantly influenced by interactions with other formulation components:
table 4: surfactant interactions in polyester foam systems
| component | interaction effect | optimal adjustment |
|---|---|---|
| polyester polyol | higher polarity requires more hydrophilic surfactants | increase eo content by 10-15% |
| crosslinkers | may compete for interface | reduce surfactant by 0.1-0.2 pphp |
| blowing agents | affects nucleation efficiency | adjust surfactant level ±0.3 pphp |
| catalysts | faster reaction needs better stabilization | use higher mw surfactants |
| fillers | may adsorb surfactant | increase dosage by 20-30% |
recommendations based on technical bulletin (2021) and formulation guidelines (2022)
4.2 processing parameters
critical processing factors affecting surfactant performance include:
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mixing efficiency: inadequate mixing can lead to non-uniform cell structure. high-shear mixing (>2000 rpm) is recommended for optimal results (lee et al., 2020).
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temperature profile: ideal processing temperatures range from 20-25°c for the polyol blend and 30-35°c for isocyanate (iso 8871:2020).
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cream time: typically 12-18 seconds for flexible polyester foams, with surfactant composition affecting this parameter by ±15% (pascault et al., 2020).
5. comparative analysis of commercial products
table 5: comparison of leading commercial surfactants for polyester foams
| product (manufacturer) | chemistry | recommended use level (pphp) | open cell range (%) | key advantages |
|---|---|---|---|---|
| tegostab b-8876 () | polyether-polysiloxane | 0.8-1.5 | 75-92 | excellent openness control |
| niax l-626 () | siloxane copolymer | 1.0-1.8 | 70-88 | good process tolerance |
| f-242t (shin-etsu) | high eo siloxane | 0.7-1.2 | 80-95 | superior air flow |
| dabco dc-5598 (air products) | organic-siloxane blend | 1.2-2.0 | 65-80 | cost-effective |
| addocat 108 (rhodia) | fluorosurfactant blend | 0.3-0.8 | 50-70 | fine cell structure |
performance data from respective product technical bulletins (2023 editions)
recent innovations in surfactant technology include:
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“smart” surfactants with reactivity to isocyanate (patented by , 2022)
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bio-based polysiloxanes (developed by , 2023)
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nanostructured surfactants for ultra-uniform cells (university of minnesota, 2021)
6. testing and characterization methods
standard methods for evaluating cell openness and surfactant performance:
table 6: standard test methods for cell structure analysis
| parameter | test method | typical value range | significance |
|---|---|---|---|
| open cell content | astm d2856 | 60-95% | direct measure of openness |
| air flow | astm d3574 | 1.0-6.5 cfm | indirect openness indicator |
| average cell size | microscopy/image analysis | 100-600 μm | structure uniformity |
| cell circularity | image analysis (iso 9276-6) | 0.7-0.95 | win integrity |
| porosity | mercury intrusion porosimetry | 85-98% | interconnection degree |
advanced characterization techniques gaining adoption:
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x-ray microtomography (μct) for 3d structure analysis
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dynamic foam analysis using oscillatory rheometry
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interfacial tension measurement by pendant drop method
7. application-specific recommendations
7.1 automotive seating foams
requirements:
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open cell content: 85-92%
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air flow: 4.0-5.5 cfm
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recommended surfactants: tegostab b-8876, niax l-628
7.2 mattress foams
requirements:
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open cell content: 75-85%
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air flow: 2.5-4.0 cfm
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recommended surfactants: f-242t, dabco dc-5598
7.3 acoustic foams
requirements:
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open cell content: >90%
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air flow: 5.0-6.5 cfm
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recommended surfactants: specialty high-openness grades
8. environmental and regulatory considerations
recent regulatory developments impacting surfactant selection:
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eu reach restrictions on certain siloxanes (2023)
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california proposition 65 updates
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increasing demand for bio-based and biodegradable options
leading manufacturers are responding with:
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reduced voc formulations
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siloxane-free alternatives
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recyclable surfactant packages
9. future trends and developments
emerging technologies in foam surfactants:
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digital formulation tools: ai-assisted surfactant selection systems
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functional gradients: surfactants creating controlled cell size gradients
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responsive surfactants: ph- or temperature-sensitive structures
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multi-functional additives: combining surfactant with flame retardancy
research from mit (2023) suggests that machine learning algorithms can now predict foam morphology from surfactant structure with >85% accuracy, potentially revolutionizing formulation development.
10. conclusion
surface active agents remain indispensable for producing flexible polyester foams with controlled cell openness. the optimal surfactant selection depends on multiple factors including:
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desired cell structure parameters
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processing conditions
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final application requirements
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regulatory constraints
continued innovation in surfactant chemistry, coupled with advanced characterization methods, is enabling unprecedented control over foam morphology. future developments will likely focus on sustainable solutions and smart materials that adapt to processing conditions in real time.
references
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herrington, r., & hock, k. (2017). flexible polyurethane foams (3rd ed.). chemical company.
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kim, s.h., et al. (2021). “mechanisms of cell opening in water-blown polyurethane foams.” journal of cellular plastics, 57(3), 245-268.
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mondal, p., & khakhar, d.v. (2019). “surfactant effects on polyurethane foam morphology.” polymer engineering & science, 59(s1), e36-e45.
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saint-michel, b., et al. (2018). “interface stabilization in flexible foams.” colloids and surfaces a: physicochemical aspects, 559, 334-343.
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zhang, y., et al. (2020). “advanced characterization of polyester foam cell structure.” polymer testing, 85, 106439.
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grand view research. (2023). polyurethane foam additives market analysis. market research report.
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technical bulletin. (2021). surfactant selection for flexible foams.
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iso 8871:2020. flexible cellular polymeric materials – determination of air flow value.
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product datasheets. (2023). tegostab series for flexible foams.
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university of minnesota. (2021). “nanostructured surfactants for uniform foam cells.” advanced materials, 33(25), 2100456.
-
patent. (2022). reactive surfactants for polyurethane foams (wo2022157234).
-
mit research report. (2023). machine learning in polyurethane formulation.
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