Exploring additives for improving the reliability of zinc nitrate hexahydrate as a phase change material (PCM)

Kumar, Navin; Banerjee, Debjyoti; Chavez, Reynaldo

doi:10.1016/j.est.2018.09.005

Cited by 34 publications

(29 citation statements)

References 12 publications

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“…Models suggest that the similarity of the lattice structures of both the salt and the catalyst (or nucleators) determine their effectiveness on mitigating supercooling [21]. Zinc hydroxyl nitrate and zinc oxide nucleators were observed to reduce the supercooling in zinc nitrate trihydrate by 8.8 o C to 8.2 o C. respectively [22]. The effect of various nucleating agents on the degree of supercooling in salt hydrates has been extensively reviewed in multiple reports in the literature [23,24].…”

Section: Supercooling In Pcmsmentioning

confidence: 99%

Machine Learning (ML) based Thermal Management for Cooling of Electronics Chips by Utilizing Thermal Energy Storage (TES) in Packaging that Leverage Phase Change Materials (PCM)

Chuttar¹,

Banerjee²

2021

Preprint

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Miniaturization of electronics devices is often limited by the concomitant high heat fluxes (cooling load) and maldistribution of temperature profiles (hot spots). Thermal energy storage (TES) platforms providing supplemental cooling can be a cost-effective solution, that often leverages phase change materials (PCM). Although salt hydrates provide higher storage capacities and power ratings (as compared to that of the organic PCMs), they suffer from reliability issues (e.g., supercooling). ‘Cold Finger Technique (CFT)’ can obviate supercooling by maintaining a small mass fraction of the PCM in solid state for enabling spontaneous nucleation. Optimization of CFT necessitates real-time forecasting of the transient values of the melt-fraction. In this study artificial neural network (ANN) is explored for real-time prediction of the time remaining to reach a target value of melt-fraction based on the prior history of the spatial distribution of the surface temperature transients. Two different approaches were explored for training the ANN model, using: (1) transient PCM-temperature data; or (2) transient surface-temperature data. When deployed in a heat sink that leverages PCM based passive thermal management systems for cooling of electronic chips and packages, this maverick approach (using the second method) affords cheaper costs, better sustainability, higher reliability and resilience.

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Section: Supercooling In Pcmsmentioning

confidence: 99%

Machine Learning (ML) based Thermal Management for Cooling of Electronics Chips by Utilizing Thermal Energy Storage (TES) in Packaging that Leverage Phase Change Materials (PCM)

Chuttar¹,

Banerjee²

2021

Preprint

Self Cite

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show abstract

“…Models suggest that the similarity of the lattice structures of both the salt and the catalyst (or nucleators) determine their effectiveness at mitigating supercooling [21]. Zinc hydroxyl nitrate and zinc oxide nucleators were observed to reduce the supercooling in zinc nitrate trihydrate by 8.8 • C to 8.2 • C, respectively [22]. The effect of various nucleating agents on the degree of supercooling in salt hydrates has been extensively reviewed in multiple reports in the literature [23,24].…”

Section: Supercooling In Pcmsmentioning

confidence: 99%

Machine Learning (ML) Based Thermal Management for Cooling of Electronics Chips by Utilizing Thermal Energy Storage (TES) in Packaging That Leverages Phase Change Materials (PCM)

Chuttar

Banerjee

2021

Electronics

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Miniaturization of electronics devices is often limited by the concomitant high heat fluxes (cooling load) and maldistribution of temperature profiles (hot spots). Thermal energy storage (TES) platforms providing supplemental cooling can be a cost-effective solution, that often leverages phase change materials (PCM). Although salt hydrates provide higher storage capacities and power ratings (as compared to that of the organic PCMs), they suffer from reliability issues (e.g., supercooling). “Cold Finger Technique (CFT)” can obviate supercooling by maintaining a small mass fraction of the PCM in a solid state for enabling spontaneous nucleation. Optimization of CFT necessitates real-time forecasting of the transient values of the melt-fraction. In this study, the artificial neural network (ANN) is explored for real-time prediction of the time remaining to reach a target value of melt-fraction based on the prior history of the spatial distribution of the surface temperature transients. Two different approaches were explored for training the ANN model, using: (1) transient PCM-temperature data; or (2) transient surface-temperature data. When deployed in a heat sink that leverages PCM-based passive thermal management systems for cooling electronic chips and packages, this maverick approach (using the second method) affords cheaper costs, better sustainability, higher reliability, and resilience. The error in prediction varies during the melting process. During the final stages of the melting cycle, the errors in the predicted values are ~5% of the total time-scale of the PCM melting experiments.

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“…In such applications, viable use of latent-heat-based TES requires the phase-transition temperature T sf of phase-change material (PCM) to be in the temperature band of 25–35 °C. This represents the median range of diurnal average daily temperature variations in arid regions. , A representative set of PCMs are mapped in Figure with respect to their specific latent heat capacity h sf and T sf as reported in the literature. − With the exception of a few organic compounds, inorganic salt hydrates are primarily highlighted. A notable feature of the plot in Figure is that a triad of salt hydrates have relatively large h sf , and thus have a greater potential for TES applications in the stipulated temperature band.…”

Section: Introductionmentioning

confidence: 99%

“…Selected inorganic salt hydrate and organic compound PCMs and their specific latent heat in the temperature range of 25–35 °C reported in the literature; average values are graphed with error bands spanning minimum and maximum reported − values.…”

Section: Introductionmentioning

confidence: 99%

“…The former case is essentially a self-seeding phenomenon, − where the residual PCM in a salt system that is not fully melted acts as the seed crystal facilitating crystal growth during cooling. This has also been ascribed as a “cold finger” process in some recent literature. − ,, Alternatively, different additives that serve as nucleating agents to reduce subcooling have been employed. ,,,, The selection of the appropriate additive has typically been carried out by the method of lattice matching. ,, The underlying principle is based on the ability of surfaces with low lattice disregistry to form a coherently strained nucleus with less surface energy contribution, thereby decreasing the degree of subcooling . Nucleating agents are typically selected from materials with <20% deviation in lattice lengths in low index planes compared to the crystals that have to be nucleated during refreezing.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Phase-Transition Efficacy and Material Compatibility with Thermal Cycling of Lithium Nitrate Trihydrate as a Phase-Change Material

Kannan

Kumar

Jog

et al. 2022

Ind. Eng. Chem. Res.

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Experimental results for phase transition during extended thermal cycling of the salt hydrate LiNO 3 •3H 2 O in both heterogeneous nucleation and self-seeding as well as material compatibility are presented. Controlled samples of this phasechange material (PCM) are heated and cooled between 20 and 40 °C. This was carried out for 1000 thermal cycles to ascertain the long-term suppression of subcooling without degradation in latent heat (h sf ). In heterogeneous nucleation, two additives were considered, Zn 3 OH 4 (NO 3 ) 2 , and Zn(NO 3 ) 2 •6H 2 O, whereas the self-seeding was induced by less than complete (90%) melting. The two nucleating agents were selected by the minimal latticemismatch method for salt crystals. Self-seeding was found to completely suppress subcooling without any loss in the latent heat of fusion, whereas heterogeneous nucleation with additives resulted in a subcooling of ΔT s > 3 °C and an up to 8−52% loss in latent heat after 1000 thermal cycles. Additionally, the material compatibility test was conducted with Al 3003, Al 1100, and SS 304 for 4000 heating−cooling cycles. The corrosion rates observed without nucleating agents were insignificant and less than typically observed in similar metals exposed to marine conditions.

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Exploring additives for improving the reliability of zinc nitrate hexahydrate as a phase change material (PCM)

Cited by 34 publications

References 12 publications

Machine Learning (ML) based Thermal Management for Cooling of Electronics Chips by Utilizing Thermal Energy Storage (TES) in Packaging that Leverage Phase Change Materials (PCM)

Machine Learning (ML) based Thermal Management for Cooling of Electronics Chips by Utilizing Thermal Energy Storage (TES) in Packaging that Leverage Phase Change Materials (PCM)

Machine Learning (ML) Based Thermal Management for Cooling of Electronics Chips by Utilizing Thermal Energy Storage (TES) in Packaging That Leverages Phase Change Materials (PCM)

Phase-Transition Efficacy and Material Compatibility with Thermal Cycling of Lithium Nitrate Trihydrate as a Phase-Change Material

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