A pyroelectric thermal sensor for automated ice nucleation detection

Cook, F. Russell; Lord, R. C.; Sitbon, Gary; Stephens, A. W.; Rust, Alison; Schwarzacher, Walther

doi:10.5194/amt-13-2785-2020

Cited by 5 publications

(3 citation statements)

References 68 publications

(90 reference statements)

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“…The detection of ice formation cannot simply be achieved by measuring the temperature and the humidity since supercooling phenomena can happen to make water freeze at temperatures below 0 ˚C. [ 9 ] The most used physical methods for ice detection today are based on magnetostriction, [ 10 ] pyroelectric effects, [ 11 ] piezoelectric [ 12 ] technology or capacitor‐antenna. [ 13 ] Those electrical methods require proper complex data analysis to interpret the data and identify ice formation.…”

Section: Introductionmentioning

confidence: 99%

Detection of Ice Formation With the Polymeric Mixed Ionic‐Electronic Conductor PEDOT: PSS for Aeronautics

Dongo,

Hakansson,

Stoeckel

et al. 2023

Adv Elect Materials

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Ice formation detection is important in telecommunications and aeronautics, e.g., ice on the wings of an aircraft affects its aerodynamic performance and leads to fatal accidents. While many types of sensors exist, resistive sensors for ice detection have been poorly explored. They are however attractive because of their simplicity and the possibility to install an array of sensors on large areas to map the ice formation on wings. Hygroscopic ionic conductors have been demonstrated for resistive ice sensing but their high resistance prevents the readout of sensor arrays. In this work, mixed ionic‐electronic polymer conductors (MIEC) are considered for the first time for ice detection. The polymer blend poly(3,4‐ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS) is solution deposited on a pair of electrodes. The sensor displays an abrupt rise in electrical resistance during the transition phase between water liquid to solid. It is proposed that the morphology and electronic transport in PEDOT are affected by the freezing event because the absorbed water in the PSS‐rich phase undergoes dilatation upon forming ice crystals. For the aeronautics application, successful tests of integration of sensing layer in pre‐preg layers of aeronautical grade and freezing detection are carried out to validate the ice detection principle.

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Section: Introductionmentioning

confidence: 99%

Detection of Ice Formation With the Polymeric Mixed Ionic‐Electronic Conductor PEDOT: PSS for Aeronautics

Dongo,

Hakansson,

Stoeckel

et al. 2023

Adv Elect Materials

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“…Droplets can be placed either on plates coated in a hydrophobic substance such as petroleum jelly or in plastic wells such as within a multiwell polymerase chain reaction (PCR) tray. Freezing can be optically detected by manual visual inspection (e.g., Creamean et al, 2018;Hill et al, 2014), with software to detect freezing optically (e.g., Stopelli et al, 2014;Perkins et al, 2020;Gute and Abbatt, 2020), with pyroelectrics (e.g., Cook et al, 2020), or by infrared thermal detection (e.g., Zaragotas et al, 2016;Harrison et al, 2018;Kunert et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Development of the drop Freezing Ice Nuclei Counter (FINC), intercomparison of droplet freezing techniques, and use of soluble lignin as an atmospheric ice nucleation standard

et al. 2021

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Abstract. Aerosol–cloud interactions, including the ice nucleation of supercooled liquid water droplets caused by ice-nucleating particles (INPs) and macromolecules (INMs), are a source of uncertainty in predicting future climate. Because INPs and INMs have spatial and temporal heterogeneity in source, number, and composition, predicting their concentration and distribution is a challenge requiring apt analytical instrumentation. Here, we present the development of our drop Freezing Ice Nuclei Counter (FINC) for the estimation of INP and INM concentrations in the immersion freezing mode. FINC's design builds upon previous droplet freezing techniques (DFTs) and uses an ethanol bath to cool sample aliquots while detecting freezing using a camera. Specifically, FINC uses 288 sample wells of 5–60 µL volume, has a limit of detection of −25.4 ± 0.2 ∘C with 5 µL, and has an instrument temperature uncertainty of ± 0.5 ∘C. We further conducted freezing control experiments to quantify the nonhomogeneous behavior of our developed DFT, including the consideration of eight different sources of contamination. As part of the validation of FINC, an intercomparison campaign was conducted using an NX-illite suspension and an ambient aerosol sample from two other drop freezing instruments: ETH's DRoplet Ice Nuclei Counter Zurich (DRINCZ) and the University of Basel's LED-based Ice Nucleation Detection Apparatus (LINDA). We also tabulated an exhaustive list of peer-reviewed DFTs, to which we added our characterized and validated FINC. In addition, we propose herein the use of a water-soluble biopolymer, lignin, as a suitable ice-nucleating standard. An ideal INM standard should be inexpensive, accessible, reproducible, unaffected by sample preparation, and consistent across techniques. First, we compared lignin's freezing temperature across different drop freezing instruments, including on DRINCZ and LINDA, and then determined an empirical fit parameter for future drop freezing validations. Subsequently, we showed that commercial lignin has consistent ice-nucleating activity across product batches and demonstrated that the ice-nucleating ability of aqueous lignin solutions is stable over time. With these findings, we present lignin as a good immersion freezing standard for future DFT intercomparisons in the research field of atmospheric ice nucleation.

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“…However, there are other less complex and expensive classes of benchtop offline experiments which allow ice nucleation studies in a laboratory. For instance, differential scanning calorimeters [ 28 ], environmental Raman stages [ 29 , 30 ], electrodynamic balances [ 31 ], optical traps [ 32 ], static cold plate based freezing assays using well plates [ 33 , 34 ], printed droplet arrays on hydrophobic substrates [ 35 ], droplets in microwells [ 36 ] and droplet arrays on a pyroelectric polymer substrate [ 37 ] have been used to study ice nucleation from atmospheric and laboratory samples.…”

Section: Introductionmentioning

confidence: 99%

A Microfluidic Device for Automated High Throughput Detection of Ice Nucleation of Snomax®

2021

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Measurement of ice nucleation (IN) temperature of liquid solutions at sub-ambient temperatures has applications in atmospheric, water quality, food storage, protein crystallography and pharmaceutical sciences. Here we present details on the construction of a temperature-controlled microfluidic platform with multiple individually addressable temperature zones and on-chip temperature sensors for high-throughput IN studies in droplets. We developed, for the first time, automated droplet freezing detection methods in a microfluidic device, using a deep neural network (DNN) and a polarized optical method based on intensity thresholding to classify droplets without manual counting. This platform has potential applications in continuous monitoring of liquid samples consisting of aerosols to quantify their IN behavior, or in checking for contaminants in pure water. A case study of the two detection methods was performed using Snomax® (Snomax International, Englewood, CO, USA), an ideal ice nucleating particle (INP). Effects of aging and heat treatment of Snomax® were studied with Fourier transform infrared (FTIR) spectroscopy and a microfluidic platform to correlate secondary structure change of the IN protein in Snomax® to IN temperature. It was found that aging at room temperature had a mild impact on the ice nucleation ability but heat treatment at 95 °C had a more pronounced effect by reducing the ice nucleation onset temperature by more than 7 °C and flattening the overall frozen fraction curve. Results also demonstrated that our setup can generate droplets at a rate of about 1500/min and requires minimal human intervention for DNN classification.

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A pyroelectric thermal sensor for automated ice nucleation detection

Cited by 5 publications

References 68 publications

Detection of Ice Formation With the Polymeric Mixed Ionic‐Electronic Conductor PEDOT: PSS for Aeronautics

Detection of Ice Formation With the Polymeric Mixed Ionic‐Electronic Conductor PEDOT: PSS for Aeronautics

Development of the drop Freezing Ice Nuclei Counter (FINC), intercomparison of droplet freezing techniques, and use of soluble lignin as an atmospheric ice nucleation standard

A Microfluidic Device for Automated High Throughput Detection of Ice Nucleation of Snomax®

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