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Review and Summary of Corrosion Behavior for Aluminum-Clad Spent Nuclear Fuel in Dry Storage

A description of the corrosion behavior of aluminum alloys used for cladding on aluminum-based, aluminum-clad nuclear fuel used in research reactors under potential dry storage conditions has been compiled. An evaluation is made of potential additional corrosion with water postulated to not be removed during drying. The relative humidity (RH) produced by typical dryness criteria for industrial spent nuclear fuel drying is expected to be low, particularly at high temperatures (17% RH at 20°C and lower at higher temperatures). Existing corrosion data suggests that negligible vapor-phase corrosion of aluminum is expected at such low humidity, even for nominally bare aluminum surfaces. That is, the predicted relative humidity from free water (e.g., 17% RH) is below the “critical” relative humidity (~40% RH at room temperature), below which no significant corrosion is observed for bare aluminum. Furthermore, reported room-temperature corrosion rates are very low even under saturated (100% RH) water vapor. Existing results showing significant vapor-phase corrosion corresponded specifically to conditions of high temper ed with high relative humidity. A relatively large reservoir of (chemically bound) water exists in the aluminum (oxy)hydroxide films on the SNF cladding surface. The estimated total could saturate the gas even at relatively high temperature if fully released; however, its release as molecular water is expected to only be plausible if the temperature during storage exceeds both the drying temperature and the threshold for thermal decomposition of the trihydroxides (~220°C). Therefore, the combination of high temperature and high relative humidity that could drive significant corrosion is considered implausible during sealed dry storage following an appropriate drying process, to include >220°C drying for canisters that may approach or exceed this temperature during storage. Vapor-phase testing of aluminum samples with an adherent (oxy)hydroxide layer, prepared in liquid water to resemble those on actual aluminum-clad spent nuclear fuel (ASNF), did not observe evidence of additional corrosion even at combined high-temperature (up to 180°C) and high-humidity (up to 100% RH) conditions. Instead, small net mass losses were observed for most specimens and attributed to dehydration of the samples, which had been air-dried only prior to testing. ASNF being moved to dry storage is expected to have an existing (oxy)hydroxide film, suggesting that the cladding will be less prone to additional corrosion than bare aluminum metal. If significant corrosion did occur during sealed storage, the overall extent and impact is expected to be small. Corrosion post-closure of a canister would not alter the total amount of hydrogen in the canister, so it would have no effect on the maximum H 2 release already assessed in existing bounding calculations. In addition, the total amount of water in a 25-µm dense bayerite film would consume a only ~8 µm additional aluminum metal on average, if fully consumed by oxidizing aluminum metal to Al 2 O 3 . These conclusions are consistent with ASNF-in-canister simulations to date, which included a reaction pathway for corrosion using kinetics from previous literature and believed to be conservative and predicted very low rates of corrosion.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Heterosynaptic plasticity in biomembrane memristors controlled by pH

Abstract In biology, heterosynaptic plasticity maintains homeostasis in synaptic inputs during associative learning and memory, and initiates long-term changes in synaptic strengths that nonspecifically modulate different synapse types. In bioinspired neuromorphic circuits, heterosynaptic plasticity may be used to extend the functionality of two-terminal, biomimetic memristors. In this article, we explore how changes in the pH of droplet interface bilayer aqueous solutions modulate the memristive responses of a lipid bilayer membrane in the pH range 4.97–7.40. Surprisingly, we did not find conclusive evidence for pH-dependent shifts in the voltage thresholds ( V* ) needed for alamethicin ion channel formation in the membrane. However, we did observe a clear modulation in the dynamics of pore formation with pH in time-dependent, pulsed voltage experiments. Moreover, at the same voltage, lowering the pH resulted in higher steady-state currents because of increased numbers of conductive peptide ion channels in the membrane. This was due to increased partitioning of alamethicin monomers into the membrane at pH 4.97, which is below the pKa (~5.3–5.7) of carboxylate groups on the glutamate residues of the peptide, making the monomers more hydrophobic. Neutralization of the negative charges on these residues, under acidic conditions, increased the concentration of peptide monomers in the membrane, shifting the equilibrium concentrations of peptide aggregate assemblies in the membrane to favor greater numbers of larger, increasingly more conductive pores. It also increased the relaxation time constants for pore formation and decay, and enhanced short-term facilitation and depression of the switching characteristics of the device. Modulating these thresholds globally and independently of alamethicin concentration and applied voltage will enable the assembly of neuromorphic computational circuitry with enhanced functionality. Impact statement We describe how to use pH as a modulatory “interneuron” that changes the voltage-dependent memristance of alamethicin ion channels in lipid bilayers by changing the structure and dynamical properties of the bilayer. Having the ability to independently control the threshold levels for pore conduction from voltage or ion channel concentration enables additional levels of programmability in a neuromorphic system. In this article, we note that barriers to conduction from membrane-bound ion channels can be lowered by reducing solution pH, resulting in higher currents, and enhanced short-term learning behavior in the form of paired-pulse facilitation. Tuning threshold values with environmental variables, such as pH, provide additional training and learning algorithms that can be used to elicit complex functionality within spiking neural networks. Graphical abstract

36 MATERIALS SCIENCE↗