Portable Quartz Crystal Resonator Sensor for Characterising the Gelation Kinetics and Viscoelastic Properties of Hydrogels

Miranda-Martínez, Andrés; Yan, Hongji; Silveira, Valentin; Olmedo, José Javier Serrano; Crouzier, Thomas

doi:10.3390/gels8110718

Cited by 3 publications

(2 citation statements)

References 34 publications

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“…Differences in the extraction and purification processes of manufacturers lead to differences in the purity of Phytagel. Commercially, Phytagels are sold with trace amounts of monovalent or divalent cations such as sodium, magnesium, potassium, or calcium, which have been proven to affect the transparency and mechanical properties of the resultant hydrogels [31][32][33][34]. The lower transparency of TS produced using Phytagel from Biodee and Coolaber may be associated with lower purity.…”

Section: Mechanical Propertiesmentioning

confidence: 99%

Characterizing a Cost-Effective Hydrogel-Based Transparent Soil

Li,

Ma,

Gao

et al. 2023

Gels

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Transparent soil (TS) was specifically designed to support root growth in the presence of air, water, and nutrients and allowed the time-resolved phenotyping of roots in vivo. Nevertheless, it is imperative to further optimize the reagent cost of TS to enable its wider utilization. We substituted the costly Phytagel obtained from Sigma with two more economical alternatives, namely Biodee and Coolaber. TS beads from each brand were prepared using 12 different polymer concentrations and seven distinct crosslinker concentrations. A comprehensive assessment encompassing transparency, mechanical characteristics, particle size, porosity, and stability of TS was undertaken. Compared to the Sigma Phytagel brand, both Biodee and Coolaber significantly reduced the transparency and collapse stress of the TS they produced. Consequently, this led to a significant reduction in the allowable width and height of the growth box, although they could still simultaneously exceed 20 cm and 19 cm. There was no notable difference in porosity and stability among the TS samples prepared using the three Phytagel brands. Therefore, it is feasible to consider replacing the Phytagel brand to reduce TS production costs. This study quantified the differences in TS produced using three Phytagel brands at different prices that will better promote the application of TS to root phenotypes.

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Section: Mechanical Propertiesmentioning

confidence: 99%

Characterizing a Cost-Effective Hydrogel-Based Transparent Soil

Li,

Ma,

Gao

et al. 2023

Gels

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“…Within the ViSQCT project of the Universidad Politécnica de Madrid (UPM), we developed a prototype sensor whose operation is based on the use of QCR. Its use in characterizing the viscosity of hydrogel formation has been previously demonstrated [ 30 ]. In a previous study [ 11 ], its operation was detailed, and its usefulness in measuring samples of artificial synovial fluid was tested.…”

Section: Introductionmentioning

confidence: 99%

ANN-Based Discernment of Septic and Inflammatory Synovial Fluid: A Novel Method Using Viscosity Data from a QCR Sensor

Miranda-Martínez

Sufrate-Vergara

Fernández-Puntero

et al. 2022

Sensors

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The synovial fluid (SF) analysis involves a series of chemical and physical studies that allow opportune diagnosing of septic, inflammatory, non-inflammatory, and other pathologies in joints. Among the variety of analyses to be performed on the synovial fluid, the study of viscosity can help distinguish between these conditions, since this property is affected in pathological cases. The problem with viscosity measurement is that it usually requires a large sample volume, or the necessary instrumentation is bulky and expensive. This study compares the viscosity of normal synovial fluid samples with samples with infectious and inflammatory pathologies and classifies them using an ANN (Artificial Neural Network). For this purpose, a low-cost, portable QCR-based sensor (10 MHz) was used to measure the viscous responses of the samples by obtaining three parameters: Δf, ΔΓ (parameters associated with the viscoelastic properties of the fluid), and viscosity calculation. These values were used to train the algorithm. Different versions of the ANN were compared, along with other models, such as SVM and random forest. Thirty-three samples of SF were analyzed. Our study suggests that the viscosity characterized by our sensor can help distinguish infectious synovial fluid, and that implementation of ANN improves the accuracy of synovial fluid classification.

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