Progress 07/01/24 to 06/30/25
Outputs
Target Audience:This project has provided opportunities to train one graduate student, one postdoctoral researcher, and one undergraduate student in interdisciplinary areas such as membrane design, polymer chemistry, nanostructure engineering, sensor membrane development, and machine learning-driven membrane design. Changes/Problems:No changes at this point What opportunities for training and professional development has the project provided?This project has provided valuable training opportunities for two graduate students, one postdoctoral researcher, and two undergraduate students in interdisciplinary fields including membrane design, polymer chemistry, nanostructure engineering, sensor membrane development, and machine learning-driven membrane design. How have the results been disseminated to communities of interest?The results have been disseminated to relevant communities of interest through conference presentations and publication in a peer-reviewed journal. What do you plan to do during the next reporting period to accomplish the goals? Cellulose nanofibril (CNF)-based membrane fabrication We will fabricate cellulose nanofibril (CNF)-based membranes and systematically characterize their chemical composition and structural features. Their performance will be evaluated through membrane porosity, hydrophilicity, wettability, surface morphologies, and regeneration efficiency. Surface morphology controlled triboelectric sensitivity To bring out the difference in porosity levels of the films, we will perform different drying techniques, such as slow drying (under ambient conditions), air flow drying, and heated drying (at 70 C) to prepare the films. Porosity calculations will be performed on a confocal microscope to better explain the sensitivity changes. Surface morphology changes will be controlled via homogenizing action between fibrils .The fibrillated surface of the fibers will be correlated with the change in the surface charge density. Evaluate CNF triboelectric responses for metal ion detection We will perform laboratory experiments to characterize the triboelectric response of sensor membranes in single-metal-ion solutions, with a focus on differentiating between various heavy metal ions. These membranes will be integrated into a triboelectric nanogenerator (TENG) setup, where films are placed on a Cu/PTFE substrate. Baseline (blank) measurements will be conducted to establish a reference for comparison with films exposed to heavy metal ions. The resulting sensitivity detection curves will serve as a basis for distinguishing among different heavy metal ions, enabling selective and reliable detection.
Impacts
What was accomplished under these goals?
We have successfully prepared cellulose acetate (CA)-based adsorptive membranes functionalized with hyperbranched polyethyleneimine (hPEI) as metal-chelating groups. Surface morphologies were characterized using scanning electron microscopy (SEM), while elemental distributions were analyzed through energy-dispersive spectroscopy (EDS). The presence of functional groups was confirmed by Fourier transform infrared spectroscopy (FTIR), and chemical bonding was further investigated using X-ray photoelectron spectroscopy (XPS). Collectively, these characterization techniques verified the successful grafting of hPEI onto the CA-based adsorptive membranes. We have conducted adsorption kinetics of the prepared adsorptive membranes under varying initial metal ion concentrations over time, revealing that metal uptake is concentration-dependent. Adsorption isotherm studies further suggested that adsorption occurs uniformly across the active sites on the membrane surface. In experiments assessing adsorption capacities toward various heavy metal ions, the membranes demonstrated strong selectivity for different metal species. Notably, the adsorption capacities remained consistent regardless of the accompanying anions, while adsorption kinetics varied depending on the specific metal ion, indicating distinct interaction mechanisms. We have systematically measured the triboelectric signals of the triboelectric membrane sensors (TEMS). Based on the voltage-resistance and current-resistance relationships, the TEMS exhibited a peak power density of 0.22 W m?² at an optimal resistance of 50 MΩ. Notably, an increase in output voltage was observed upon adsorption of metal salts, a trend further supported by Kelvin probe force microscopy (KPFM) measurements. Sensitivity testing revealed that the system could detect copper ions (Cu²?) at concentrations as low as 10 nM, based on a signal-to-noise ratio of 3, demonstrating the high sensitivity of the TEMS platform for heavy metal detection. We have calculated electrostatic potential maps of PEI-chelated metal ions, using density functional theory (DFT), revealed distinct differences in electronegativity among various heavy metal complexes. Open-circuit voltage measurements of the TEMS exposed to a range of metal cations (Cu²?, Cd²?, Pb²?, Zn²?, Co²?, Ni²?, Mg²?, Sr²?, Mn²?, Ca²?, Ba²?, K?, Na?, Li?, Fe³?) showed that Cu²? produced the highest voltage response, highlighting the superior sensitivity of the TEMS to copper ions. This enhanced response is attributed to both the selective adsorption capability of the PEI-modified membrane and the intrinsic electronegativity of the Cu²? ion. Furthermore, minimal changes in voltage output were observed when different anions (Cl?, SO?²?, NO??) were paired with Cu²?, indicating that the triboelectric effect in the TEMS is primarily governed by the identity of the cation rather than the accompanying anion. We have integrated the adsorption kinetic model, isotherm model, and mass balance principles, a quantitative relationship was established between the initial heavy metal ion concentration and the corresponding triboelectric voltage output. To validate this model, additional experiments were conducted comparing the predicted initial concentrations with those measured using inductively coupled plasma optical emission spectrometry (ICP-OES). The results demonstrated a strong agreement, with a Pearson correlation coefficient of 0.999. Despite the wide concentration range tested (approximately 0.1 to 60 ppm), the model maintained an average percentage error of only 5.14%, confirming its reliability and high sensitivity across varying conditions. We have demonstrated the robustness and applicability of our system across diverse environmental conditions, we evaluated the sensor's performance under varying metal concentrations, fluctuating temperatures, and different water matrices. Once calibrated, the sensor consistently delivered reliable results even under previously untested conditions. For the temperature variation study, 50 ppm Cu²? solutions were analyzed at 50?°C over multiple time intervals. Using a cross-validation approach to fit our proposed model, the predicted concentrations showed excellent agreement with experimental values measured by ICP-OES, yielding an average percentage error of just 2.24%, which remained stable across the measurement range. Similarly, for seawater conditions, Cu²? solutions of varying concentrations were prepared in a seawater matrix and analyzed using the same cross-validation method. The resulting average percentage error between predicted and experimental values was 2.92%, again demonstrating consistent accuracy. We have demonstrated ethylenediaminetetraacetic acid (EDTA) solutions significantly higher washing efficiency compared to hydrochloric acid (HCl), achieving nearly 100% desorption efficiency across three regeneration cycles. Furthermore, the system exhibited excellent regeneration capability, maintaining a consistent desorption rate over six cycles. SEM images revealed no noticeable changes in the membrane's microstructure after repeated use, indicating strong structural integrity. Additionally, the triboelectric signals remained stable after 15,000 cycles of contact-separation, confirming the long-term operational stability of the membrane during repeated metal detection processes. We have conducted a cost analysis to evaluate the economic feasibility of the proposed system, revealing that the cost of producing a new CA-PEI membrane is approximately $0.24, whereas regenerating the membrane using EDTA costs only $5.29 × 10?? per cycle. Given the membrane's ability to maintain high performance over multiple regeneration cycles, regeneration proves to be a significantly more cost-effective approach than continuously replacing membranes, further enhancing the system's practicality for large-scale and long-term applications.
Publications
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