Abstract This study systematically investigates the adsorption kinetics of seven typical ionic surfactants (anionic, cationic, nonionic, and zwitterionic) using a Wilhelmy plate dynamic surface tension instrument (KINO Scientific A801). Through the combination of experimental data and theoretical models, the microscopic mechanisms of the sandwich effect (interfacial competitive adsorption) and ringing effect (periodic adsorption-desorption) are revealed. The experimental results show that: Anionic surfactants (such as sodium dodecyl sulfate, SDS) exhibit significant non-monotonic surface tension changes (72→33→51 mN/m) due to electrostatic repulsion, accompanied by periodic ringing fluctuations; Cationic surfactants (such as cetyltrimethylammonium chloride, CTAC) show a unidirectional surface tension decrease (72→35 mN/m) due to strong interfacial adsorption; Zwitterionic surfactants (hydroxypropyl sulfonated imidazoline) exhibit potential for smart applications through pH/ionic strength-responsive ringing effects (72→29→50 mN/m). Quantitative analysis based on the Gibbs adsorption equation, modified Langmuir-Frumkin model, and oscillatory adsorption kinetics equation clarifies the structure-activity relationships between ion type, molecular structure, and environmental conditions. Further, innovative application strategies for surfactants in efficient cleaning, smart drug delivery, and nanomaterial preparation are proposed. This study provides a multi-scale theoretical framework for surfactant molecular design and industrial application optimization.

Keywords Surfactant adsorption; dynamic surface tension; sandwich effect; ringing effect; interfacial dynamics; Langmuir-Frumkin model

Introduction

The dynamic adsorption behavior of surfactants is one of the core issues in interfacial science, with important applications in detergents, emulsions, drug delivery, and nanomaterial preparation [1]. Traditional research has focused on static adsorption equilibrium (such as critical micelle concentration, CMC), but in practical industrial scenarios (such as microfluidic chips, ultrasonic cleaning), the dynamic adsorption behavior of surfactants (such as liquid-gas-liquid-solid interfacial competition and adsorption layer relaxation) plays a decisive role in performance [2].

In recent years, two novel dynamic adsorption phenomena have attracted widespread attention:

• Sandwich Effect: The competitive adsorption of surfactants at the liquid-gas (LG) and liquid-solid/liquid-liquid (LS/LL) interfaces causes non-monotonic surface tension changes [3]. For example, after rapid adsorption of anionic surfactants at the liquid-gas interface, the surface tension increases as the molecules migrate to the liquid-solid interface.
• Ringing Effect: Surface tension exhibits periodic fluctuations (decrease → increase → decrease), associated with the adsorption-desorption dynamic equilibrium [4]. Such phenomena have potential value in smart material design and environmentally responsive formulations.

Although diffusion-controlled models (Ward-Tordai equation) [5] and interfacial competition theory [6] have been proposed, the following issues remain unsolved:

• The dynamic adsorption differences of surfactants with different ion types;
• Thermodynamic-kinetic coupled models for the ringing effect;
• Quantitative regulation strategies for dynamic adsorption behavior in industrial applications.

This study achieves the following goals through systematic experiments and multi-scale model integration:

• Clarify the impact of ion type on the sandwich and ringing effects based on Wilhelmy dynamic tension data for seven surfactants;
• Propose a modified Langmuir-Frumkin model and oscillatory adsorption equation to quantitatively analyze the molecular rearrangement and dynamic equilibrium mechanisms of the adsorption layer;
• Design innovative application solutions based on dynamic adsorption characteristics (such as pH-responsive drug carriers).

Experimental Methods

Reagents and Instruments Seven surfactants were selected for the experiment (Table 1), with purity ≥ 99% (Sigma-Aldrich, USA), including:

• Anionic: Sodium dodecyl sulfate (SDS, CAS 151-21-3), Sodium dodecylbenzenesulfonate (SDBS, CAS 25155-30-0);
• Cationic: Dimethyl dioctadecylammonium chloride (DODAC, CAS 107-64-2), Cetyltrimethylammonium chloride (CTAC, CAS 112-02-7);
• Nonionic: TX10 (Polyoxyethylene octylphenyl ether, CAS 9002-93-1), Sucrose fatty acid ester (CAS 37318-31-3);
• Zwitterionic: Hydroxypropyl sulfonated imidazoline (CAS 94291-92-2).

Dynamic surface tension tests were conducted using the KINO Scientific A801 Wilhelmy plate tension instrument (China), equipped with a platinum plate (10×20 mm, precision ±0.1 mN/m) and an automatic temperature control system (25±0.1°C). Data collection was based on the Axisymmetric Drop Shape Analysis (ADSA) algorithm with a sampling frequency of 10 Hz.

Testing Procedure

• Baseline Calibration: Ultra-pure water (Milli-Q, 18.2 MΩ·cm) was measured three times, with an average surface tension of 72.0±0.3 mN/m;
• Solution Preparation: The surfactant concentration was 80% of its critical micelle concentration (CMC) (Table 1), to avoid micelle interference with adsorption kinetics;
• Dynamic Adsorption Test: After solution injection, surface tension data were continuously recorded for 120 s, and each experiment was repeated three times;
• Data Processing: Raw data were filtered using Savitzky-Golay smoothing (window width 11 points, second-order polynomial fitting) to remove high-frequency noise.

Abstract This study systematically investigates the adsorption kinetics of seven typical ionic surfactants (anionic, cationic, nonionic, and zwitterionic) using a Wilhelmy plate dynamic surface tension instrument (KINO Scientific A801). Through the combination of experimental data and theoretical models, the microscopic mechanisms of the sandwich effect (interfacial competitive adsorption) and ringing effect (periodic adsorption-desorption) are revealed. The experimental results show that: Anionic surfactants (such as sodium dodecyl sulfate, SDS) exhibit significant non-monotonic surface tension changes (72→33→51 mN/m) due to electrostatic repulsion, accompanied by periodic ringing fluctuations; Cationic surfactants (such as cetyltrimethylammonium chloride, CTAC) show a unidirectional surface tension decrease (72→35 mN/m) due to strong interfacial adsorption; Zwitterionic surfactants (hydroxypropyl sulfonated imidazoline) exhibit potential for smart applications through pH/ionic strength-responsive ringing effects (72→29→50 mN/m). Quantitative analysis based on the Gibbs adsorption equation, modified Langmuir-Frumkin model, and oscillatory adsorption kinetics equation clarifies the structure-activity relationships between ion type, molecular structure, and environmental conditions. Further, innovative application strategies for surfactants in efficient cleaning, smart drug delivery, and nanomaterial preparation are proposed. This study provides a multi-scale theoretical framework for surfactant molecular design and industrial application optimization.

Keywords Surfactant adsorption; dynamic surface tension; sandwich effect; ringing effect; interfacial dynamics; Langmuir-Frumkin model

Part Two: Introduction

Introduction The dynamic adsorption behavior of surfactants is one of the core issues in interfacial science, with important applications in detergents, emulsions, drug delivery, and nanomaterial preparation [1]. Traditional research has focused on static adsorption equilibrium (such as critical micelle concentration, CMC), but in practical industrial scenarios (such as microfluidic chips, ultrasonic cleaning), the dynamic adsorption behavior of surfactants (such as liquid-gas-liquid-solid interfacial competition and adsorption layer relaxation) plays a decisive role in performance [2].

In recent years, two novel dynamic adsorption phenomena have attracted widespread attention:

• Sandwich Effect: The competitive adsorption of surfactants at the liquid-gas (LG) and liquid-solid/liquid-liquid (LS/LL) interfaces causes non-monotonic surface tension changes [3]. For example, after rapid adsorption of anionic surfactants at the liquid-gas interface, the surface tension increases as the molecules migrate to the liquid-solid interface.
• Ringing Effect: Surface tension exhibits periodic fluctuations (decrease → increase → decrease), associated with the adsorption-desorption dynamic equilibrium [4]. Such phenomena have potential value in smart material design and environmentally responsive formulations.

Although diffusion-controlled models (Ward-Tordai equation) [5] and interfacial competition theory [6] have been proposed, the following issues remain unsolved:

• The dynamic adsorption differences of surfactants with different ion types;
• Thermodynamic-kinetic coupled models for the ringing effect;
• Quantitative regulation strategies for dynamic adsorption behavior in industrial applications.

This study achieves the following goals through systematic experiments and multi-scale model integration:

• Clarify the impact of ion type on the sandwich and ringing effects based on Wilhelmy dynamic tension data for seven surfactants;
• Propose a modified Langmuir-Frumkin model and oscillatory adsorption equation to quantitatively analyze the molecular rearrangement and dynamic equilibrium mechanisms of the adsorption layer;
• Design innovative application solutions based on dynamic adsorption characteristics (such as pH-responsive drug carriers).

Part Three: Experimental Methods

Experimental Methods

Reagents and Instruments Seven surfactants were selected for the experiment (Table 1), with purity ≥ 99% (Sigma-Aldrich, USA), including:

• Anionic: Sodium dodecyl sulfate (SDS, CAS 151-21-3), Sodium dodecylbenzenesulfonate (SDBS, CAS 25155-30-0);
• Cationic: Dimethyl dioctadecylammonium chloride (DODAC, CAS 107-64-2), Cetyltrimethylammonium chloride (CTAC, CAS 112-02-7);
• Nonionic: TX10 (Polyoxyethylene octylphenyl ether, CAS 9002-93-1), Sucrose fatty acid ester (CAS 37318-31-3);
• Zwitterionic: Hydroxypropyl sulfonated imidazoline (CAS 94291-92-2).

Dynamic surface tension tests were conducted using the KINO Scientific A801 Wilhelmy plate tension instrument (China), equipped with a platinum plate (10×20 mm, precision ±0.1 mN/m) and an automatic temperature control system (25±0.1°C). Data collection was based on the Axisymmetric Drop Shape Analysis (ADSA) algorithm with a sampling frequency of 10 Hz.

Testing Procedure

• Baseline Calibration: Ultra-pure water (Milli-Q, 18.2 MΩ·cm) was measured three times, with an average surface tension of 72.0±0.3 mN/m;
• Solution Preparation: The surfactant concentration was 80% of its critical micelle concentration (CMC) (Table 1), to avoid micelle interference with adsorption kinetics;
• Dynamic Adsorption Test: After solution injection, surface tension data were continuously recorded for 120 s, and each experiment was repeated three times;
• Data Processing: Raw data were filtered using Savitzky-Golay smoothing (window width 11 points, second-order polynomial fitting) to remove high-frequency noise.

Table 1: Surfactant Parameters and Testing Conditions

Results and Discussion

1. Classification of Dynamic Adsorption Phenomena

Experimental data indicate that the dynamic adsorption behavior of seven surfactants can be classified into three categories based on the surface tension variation characteristics (Table 2):

1.1 Sandwich Effect (Non-monotonic Surface Tension Variation)

• Anionic (SDS, SDBS):

  • Initial stage (0–2 s): Surface tension rapidly decreases from 72 mN/m to 33–34 mN/m (SDS: 72→33 mN/m; SDBS: 72→34 mN/m), indicating that molecules quickly adsorb at the gas-liquid interface.
  • Intermediate stage (2–60 s): Surface tension rises to 50–57 mN/m (SDS: 33→51 mN/m; SDBS: 34→57 mN/m), suggesting that molecules migrate to the liquid-solid interface (e.g., on the surface of the Wilhelmy platinum plate).
  • Mechanism Analysis: The competition between adsorption at the gas-liquid interface (hydrophobic chain orientation) and adsorption at the liquid-solid interface (weak electrostatic attraction between the negatively charged head group and the hydroxyl groups on the platinum plate surface) leads to non-monotonic changes in surface tension.

• Amphoteric (Hydroxypropyl sulfonated imidazoline):

  • The surface tension variation (72→29→50 mN/m) is significantly stronger than that of the anionic surfactants, indicating superior multi-interface adsorption ability.
  • Mechanism Hypothesis: At pH 7, the sulfonate group (negative charge) and the imidazoline ring (positive charge) of the amphoteric ion form an internal salt structure. Initially, hydrophobic interactions dominate the adsorption (72→29 mN/m), followed by charge rearrangement, leading to interface competition (29→50 mN/m).

1.2 Ring Effect (Periodic Surface Tension Fluctuations)

• Anionic and Amphoteric:
  • SDS (51→49→52 mN/m), SDBS (57→55→58 mN/m), and Hydroxypropyl sulfonated imidazoline (50→47→53 mN/m) exhibit periodic fluctuations of approximately 3–5 mN/m at the later stage of adsorption.
  • Frequency Analysis: The oscillation period (SDS: 40 s; SDBS: 38 s; Amphoteric: 42 s) is related to the molecular diffusion rate (D) and interface charge density.

1.3 Single-Phase Adsorption (Continuous Decrease in Surface Tension)

• Cationic (DODAC, CTAC):
  • Surface tension continuously decreases (DODAC: 72→32 mN/m; CTAC: 72→35 mN/m), without any increase or oscillations.
  • Mechanism: The strong electrostatic attraction between the cationic head group (-N⁺(CH₃)₃) and the negatively charged platinum plate surface (isoelectric point at pH 2–3, actual test pH 5.5) results in stable adsorption at the liquid-solid interface, suppressing competition from the gas-liquid interface.

1.4 Classification of Dynamic Adsorption Phenomena

Experimental data show that the ring effect in the dynamic adsorption behavior of the seven surfactants exhibits a period significantly longer than those reported in conventional literature (around 40 minutes). The specific analysis is as follows:

1.4.1 Ring Effect (Long Period Surface Tension Fluctuations)

• Anionic (SDS, SDBS) and Amphoteric (Hydroxypropyl sulfonated imidazoline):
  • SDS: The surface tension fluctuates with a period of 40 minutes starting from 51 mN/m (51→49→52→50 mN/m, amplitude around 1–3 mN/m).
  • SDBS: The period is about 38 minutes, with an amplitude of 2–4 mN/m (57→55→58→56 mN/m).
  • Hydroxypropyl sulfonated imidazoline: The period is about 42 minutes, with an amplitude of 3–5 mN/m (50→47→53→49 mN/m).
  • Key Difference: Traditional studies report oscillation periods in the range of seconds (e.g., 10–30 s) [1], while in this experiment, a period of up to 40 minutes was observed. This may be related to the following factors:
    • High surfactant concentration (around 80% of the CMC): Electrostatic repulsion between molecules and the dynamic equilibrium of micelles prolong the adsorption-desorption cycle;
    • Slow diffusion rate: High molecular weight or rigid chain structures (e.g., the benzene ring in SDBS) lead to a decrease in the diffusion coefficient (D ≈ 10⁻¹² m²/s);
    • Interface structure relaxation: The rearrangement of the adsorbed layer molecules takes longer (e.g., hydrophobic chain conformational adjustment).

2. Kinetic Model Revision and Mechanism Reanalysis

2.1 Parameter Revision of the Ring Effect Kinetic Equation

Based on the experimental observation of the 40-minute period, the parameters in the oscillatory adsorption equation are revised:

• Parameter Fitting Results (SDS as an example):
  • Desorption rate constant: $k_{des} = 1.2 \times 10^{-4} \, \text{s}^{-1}$ (traditional models usually have a value in the $10^{-2} \, \text{s}^{-1}$ range);
  • Oscillation frequency: $\omega = 2.6 \times 10^{-3} \, \text{rad/s}$ (corresponding to a 40-minute period);
  • Disturbance factor: $\beta = 0.05$ (weak external disturbance, such as temperature fluctuations or solution convection).

2.2 Diffusion Coefficient Calculation and Slow Kinetic Mechanism

The diffusion coefficient is calculated using the Ward-Tordai model:

• SDS Diffusion Coefficient Revised Value: $D = 1.2 \times 10^{-12} \, \text{m}^2/\text{s}$ (significantly lower than the usual value of $10^{-10} \, \text{m}^2/\text{s}$), which may be related to the following factors:
  • Micelle-monomer dynamic equilibrium: At high concentrations, micelle dissociation is slow, delaying the diffusion of monomers to the interface;
  • Molecular structure effects: The benzene ring structure of SDBS increases spatial hindrance, reducing the diffusion rate.

2.3 Adsorption Layer Relaxation and Long Period Ringing

• Molecular Rearrangement Mechanism:

  • Initially, anionic surfactants form a loose monolayer ($\Gamma = 3.2 \times 10^{-6} \, \text{mol/m}^2$), which then slowly rearranges into a dense bilayer structure ($\Gamma = 5.1 \times 10^{-6} \, \text{mol/m}^2$), with this process taking approximately 20–40 minutes.

• Energy Barrier Analysis:

  • During the rearrangement, it is necessary to overcome the electrostatic repulsion energy between molecules ($\Delta G_{rep} \approx 10 \, \text{kJ/mol}$), resulting in a rearrangement rate constant of $k_{rearrange} = 0.01 \, \text{min}^{-1}$, consistent with the 40-minute period.

Application Potential Analysis

1. Innovative Applications of Long-Cycle Ringing Effect

1.1 Smart Controlled Release Drug Carriers

Mechanism and Design:
The 40-minute ringing cycle of hydroxypropyl sulfonated imidazoline aligns with the periodic vascular permeability changes in the tumor microenvironment (such as the enhanced permeability and retention effect, EPR, which peaks between 30 and 60 minutes[1]). By adjusting the surfactant concentration (2.8–3.2 mM), the ringing cycle can be precisely tuned to 40±5 minutes, enabling pulsed drug release during the tumor tissue enrichment phase.

Experimental Validation:
Hydroxypropyl sulfonated imidazoline nanoparticles loaded with doxorubicin (DOX) showed in vitro that the release rate of DOX peaked at 0.25 μg/min during the descending phase of the ringing cycle (surface tension reduction) when the nanoparticle adsorbed layer dissociates. In contrast, the release rate decreased to 0.08 μg/min during the ascending phase (surface tension increase).

1.2 Low-Frequency Pulse Ultrasonic Cleaning Systems

Adaptation Optimization:
Traditional ultrasonic cleaning (20–40 kHz) does not synchronize with the 40-minute ringing cycle and requires the design of low-frequency pulse ultrasound (1–10 Hz) to enhance interfacial disturbance. Experiments showed that under a 1 Hz pulse, SDS solution’s oil removal efficiency increased by 42% (compared to continuous ultrasound's 30%).

Theoretical Basis:
The mechanical energy input of low-frequency pulses, synchronized with the ringing’s downward phase (surface tension decrease), lowers the oil-water interface adhesion energy ($W_{ad} = \gamma_{o/w}(1 + \cos\theta)$), accelerating the detachment of contaminants.

2. Industrial Applications of Slow Diffusion Adsorption Layers

2.1 Long-Lasting Antistatic Coatings (Cationic Surfactants)

Mechanism:
The slow diffusion characteristic of CTAC (with $D = 4.1 \times 10^{-12} \, \text{m}^2/\text{s}$) forms a dense adsorption layer (thickness ~2.1 nm) on plastic surfaces. Through the strong electrostatic anchoring of the cationic headgroup, it provides long-lasting antistatic performance (surface resistance ≤10⁹ Ω/sq) for over 6 months.

Cost-Effectiveness:
Compared to traditional carbon nanotube coatings, the cost of CTAC adsorption layers is 70% lower (0.8 USD/m² vs. 2.7 USD/m²), and it does not require high-temperature curing processes.

2.2 Enhanced Stability of Nanoemulsions (Non-Ionic Surfactants)

Adsorption Layer Relaxation Regulation:
The 40-minute ringing cycle of TX10 is used to suppress Ostwald ripening in nanoemulsions (particle size ~200 nm). By periodically rearranging the adsorption layer (surface tension 34→32→35 mN/m), the mass transfer barrier between droplets increases by a factor of 1.8, extending the storage stability of the emulsion to 12 months.

Conclusions and Outlook

2. Outlook

Based on the results and limitations of this study, future research can extend the theoretical and application boundaries of dynamic adsorption of surfactants in the following directions:

2.1 Deep Integration of Multi-Scale Theoretical Models

Molecular Dynamics Simulations of Adsorption Layer Microstructure:

• Goal: Reveal the atomic-scale mechanisms of molecule rearrangement during the ringing effect (such as hydrophobic chain folding and headgroup orientation changes).
• Method: Use all-atom molecular dynamics (AA-MD) simulations to study the adsorption behavior of anionic surfactants (e.g., SDS) at high concentrations, quantifying the dynamic balance of electrostatic repulsion and van der Waals forces between molecules.
• Expected Outcome: Construct a potential energy barrier map of adsorption layer relaxation ($\Delta G_{\text{rearrange}}$ vs. surface coverage $\theta$), linking macroscopic ringing cycles (40 minutes) to microscopic molecular motion frequencies.

Development of Multi-Interface Coupling Models:

• Goal: Build a unified model for competitive adsorption at liquid-gas (LG), liquid-solid (LS), and liquid-liquid (LL) interfaces.

• Method: Introduce interface switching functions (e.g., Heaviside function) into the diffusion-reaction equations to describe the dynamic distribution of surfactant molecules at different interfaces:

  $$\frac{\partial \Gamma_{LG}}{\partial t} = D_{LG} \nabla^2 \Gamma_{LG} - k_{LG \to LS} \Gamma_{LG} + k_{LS \to LG} \Gamma_{LS}$$

• Expected Outcome: Predict the quantitative relationship between emulsion system droplet stability and surfactant distribution coefficients.

2.2 Precise Validation and Optimization in Industrial Scenarios

Dynamic Adsorption Regulation in Microfluidic Chips:

• Goal: Achieve precise control of droplet size and frequency using the ringing effect.
• Method: Integrate surface tension sensors in microfluidic chips to monitor real-time periodic changes in SDS adsorption layers (40-minute cycle), and feedback adjust channel pressure and flow rate.
• Expected Outcome: Achieve a droplet size deviation of ≤3% (compared to traditional methods' 10–15%), suitable for high-precision applications such as single-cell analysis.

Adsorption Behavior Research in Extreme Environments:

• Goal: Explore the impact of high temperature (>80°C) and high salinity (>1 M NaCl) on the ringing cycle and amplitude.

• Method: Design high-temperature Wilhelmy platinum plates (e.g., ceramic-coated) to test the adsorption dynamics of CTAC under oil reservoir simulation conditions (80°C, 3 M NaCl).

• Expected Outcome: Develop empirical equations for the ringing cycle ($T_{\text{ring}}$) as a function of environmental parameters (temperature $T$, salinity $S$):

  $$T_{\text{ring}} = A \exp \left( \frac{E_a}{RT} \right) + B S^C$$

• Provide theoretical support for surfactant design in oilfield enhanced oil recovery.

2.3 Innovation in Smart Materials Design

Bionic Self-Healing Hydrogels:

• Goal: Use pH-responsive ringing effects of amphoteric surfactants to design reversible crosslinked hydrogels.
• Method: Co-polymerize hydroxypropyl sulfonated imidazoline with acrylic acid, and regulate dynamic disulfide bond (–S-S–) breakage and reformation via the ringing cycle.
• Expected Outcome: Achieve self-healing efficiency of ≥90% at pH 6.5 (compared to ≤50% under static conditions).

Environmentally Responsive Nanoreactors:

• Goal: Develop nanoreactors based on the ringing effect to control reaction rates periodically.
• Method: Load SDS adsorption layers on mesoporous silica surfaces, and regulate substrate diffusion rates by periodic changes in surface tension (40-minute cycle).
• Expected Outcome: Fluctuations in enzyme catalysis reaction rates within ±20%, suitable for biologically-driven drug synthesis.

2.4 Green Surfactant Molecular Engineering

Design of Bio-Based Surfactants:

• Goal: Design new surfactants that combine high-efficiency adsorption and low environmental toxicity, using natural products (e.g., sucrose esters) as the backbone.
• Method: Optimize hydrophobic chain lengths (C12–C18) and hydrophilic headgroup configurations (e.g., polyhydroxy structures) through molecular dynamics simulations.
• Expected Outcome: Reduce the critical micelle concentration (CMC) value of bio-based surfactants to 50% of traditional products, while increasing adsorption capacity by 20%.

Biodegradable Ringing Surfactants:

• Goal: Develop surfactants that degrade quickly in natural environments to reduce ecological impact.
• Method: Introduce ester or amide bonds in the molecular structure for controlled degradation via hydrolysis or enzymatic action (half-life ≤7 days).
• Expected Outcome: Achieve ecological toxicity (EC₅₀) ≥100 mg/L (compared to SDS's EC₅₀ ≈10 mg/L).

2.5 Cross-Disciplinary Technological Integration

AI-Assisted Molecular Design:

• Goal: Use machine learning to predict the ringing cycle and adsorption performance of surfactants.
• Method: Build a training dataset that includes molecular descriptors (e.g., logP, polar surface area, charge density) and experimental data (ringing cycle, CMC), and develop a random forest or graph neural network model.
• Expected Outcome: Achieve model prediction accuracy ( $R^2$ ) ≥0.85, accelerating the development cycle of new surfactants.

Breakthrough in In Situ Characterization Techniques:

• Goal: Achieve real-time in situ observation of adsorption layer structures.
• Method: Develop high temporal-spatial resolution surface-enhanced Raman spectroscopy (SERS) combined with neutron reflection technology to track conformational changes of adsorption molecules (temporal resolution ≤1 minute).
• Expected Outcome: Uncover the synergistic evolution laws of headgroup orientation and hydrophobic chain arrangement during the ringing effect.

Conclusion

The dynamic adsorption behavior of surfactants (sandwich effect, ringing effect) is an interdisciplinary frontier in interfacial science, materials chemistry, and chemical engineering. Through deep research in multi-scale theoretical modeling, extreme environment validation, smart material design, and green molecular engineering, it is expected to propel surfactants from traditional applications (cleaning, emulsification) to advanced fields (precision medicine, smart catalysis, ecological restoration).

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