The authors thank Marc Steen and Lois Brett for their excellent support reviewing this manuscript.

NOTE: The protocol followed in this work is explained in detail in Ruiz et al.39. A summary of the important steps is described below.
1. Pouch Cell Preparation and Formation
<ol>
	<li>Fabricate pouch cells in the format B5, having the dimension of 250 mm x 164 mm with an approximately 4-mm thickness, with artificial graphite as anode material, lithium iron phosphate (LFP) as cathode material, and a 25-&#181;m thick polypropylene separator.</li>
	<li>Use 80 g of electrolyte: 1 M LiPF6 in ethylene carbonate:diethyl carbonate (2:3 w/w) containing 1% vinylene carbonate. 
	&#8203;NOTE: The pouch cell fabrication was performed in a semi-automatic industrial pilot line consisting of the following steps: i) a slurry preparation containing the following active material: graphite for the anode and LFP for the cathode, a binder, and conductive additives in a lab-scale mixer, ii) a slurry coating on the current collectors (aluminum foil and copper foil, for cathode and anode electrodes, respectively), iii) a calendering for an optimized electrode performance in terms of, e.g., electrode density, porosity, thickness, electronic conductivity, and impedance, followed by iv) the assembly, electrolyte filling and sealing.</li>
	<li>Carry out the formation of the cell. Create a cycling protocol with the battery cycler software using the following steps.
	<ol>
		<li>Use the Build Test function of the battery cycler software. Click on the new file icon (see the blue arrow in Supplementary File 1a).</li>
		<li>Each line in the protocol code refers to a parameter of the cycling (e.g., rest time and cut-off voltage) (Supplementary File 1b).Fill out each step as required to perform a two-step constant current-constant voltage (CC-CV) charging at 0.1 C until 3.6 V, with a 10-mA cutoff current and a CC discharge at 0.1 C until 2.5 V. After the formation step, charge the battery cells at a 30% state of charge (SOC). Click the save button and provide a file name.</li>
		<li>Select the cell to be cycled by clicking on its corresponding channel (see blue arrow No. 1 in Supplementary File 2). That channel is marked in the &#34;State&#34; column as &#34;selected&#34;. Then click on the run button (see blue arrow No. 2 in Supplementary File 2) on the top of the toolbar.</li>
		<li>Select the protocol (see blue arrow No. 1 in Supplementary File 3), set the capacity (Ah) of the cell (see blue arrow No. 2 in Supplementary File 3) and assign a chamber (see blue arrow No. 3 in Supplementary File 3). Define a valid filename and click on the start button.</li>
	</ol>
	</li>
</ol>
2. Cell Fixture Prior to Electrochemical Testing
<ol>
	<li>Place each cell in corresponding holders consisting of two rigid plates (with a width and length of 300 mm x 300 mm, respectively, and a thickness of 12 mm) made of polycarbonate.</li>
	<li>Place a thermocouple in the center of one of the sides of each cell inside the holders to monitor the surface temperature variations.</li>
	<li>Place the cells and fixtures inside a temperature chamber to control the environmental temperature throughout the experiment. Place two cells following an identical protocol in the same temperature chamber.</li>
	<li>Connect the cells via a 4-wire connection to the cycler.</li>
</ol>
3. Electrochemical Cycling
<ol>
	<li>Cell conditioning
	<ol>
		<li>Set the temperature at 25 &#176;C in the environmental chamber. Allow at least 12 h to ensure a thermal equilibration.</li>
		<li>Perform three charge/discharge cycles using a battery cycler.
		<ol>
			<li>Create a protocol for the battery cycler, following steps 1.3.1 and 1.3.2. In this case, adjust the protocol steps to a CC-CV charging at 0.1 C (from the rated capacity) up to 3.7 V (the CV phase until 0.01 C or 1 h), then CC discharge at 0.1 C until 2.7 V. Use a 30 min rest time after each cycling step.</li>
			<li>Follow steps 1.3.3 and 1.3.4 for the channel and protocol selection.</li>
			<li>When two cells are placed in the same temperature chamber (two cells following the same protocol), select the two corresponding channels at the same time. This guarantees the synchronization of the cycling and chamber temperature condition for the two cells.</li>
		</ol>
		</li>
		<li>Perform a reference cycle (step 3.2) and use it for assessing the initial capacity (Ci) (Table 1).</li>
	</ol>
	</li>
	<li>Reference cycling
	<ol>
		<li>Perform the reference cycling as part of the cell conditioning (step 3.1.3) and at periodic intervals (i.e., following 25 long-term aging cycles, see below).</li>
		<li>Set the temperature of the chamber at 25 &#176;C, when testing is performed at a different temperature, and allow sufficient time for a thermal stabilization (&#60; 1 Kh-1).</li>
		<li>Perform two CC charge/discharge cycles using a battery cycler.
		<ol>
			<li>Create a protocol for the battery cycler with the software, following steps 1.3.1. and 1.3.2. In this case, adjust the protocol steps to a CC charging-discharging at 0.3 C (e.g., IEC 62660-1:2011)13. After each cycling step, allow extra time for a temperature stabilization (&#60; 1 Kh-1).</li>
			<li>Follow steps 1.3.3 and 1.3.4 for the channel and protocol selection.</li>
			<li>When two cells are placed in the same temperature chamber (two cells following the same protocol), select the two corresponding channels at the same time. This guarantees synchronization of the cycling and chamber temperature condition for the two cells.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Long-term (aging)
	<ol>
		<li>Perform 100 charge/discharge cycles. Create a protocol for the battery cycler with the software, following steps 1.3.1 and 1.3.2. In this case, adjust the protocol steps to a CC-CV charging of 1 C up to 3.7 V (CV phase until 0.1 C or 1 h) and a CC discharging of 1 C current up to 2.7 V with a constant temperature during charge (Tc) and during discharge (Td).</li>
		<li>Follow steps 1.3.3 and 1.3.4 for the channel and protocol selection.</li>
		<li>Carry out the long-term aging at several temperature combinations (10) for the 100 charge/discharge cycles from step 3.3.1, in the temperature range from -20 &#176;C to 30 &#176;C (see the test matrix in Table 1) developed through DOE D-optimization36 (a minimum error of prediction). Set a rest time in the testing protocol of 30 min after each charging or discharging step when Tc and Td are the same (tests No. 1 and 2, 3 and 4, 9 and 10, 13 and 14, and 19 and 20, Table 1). However, when Tc and Td are different (tests No. 11 and 12, 5 and 6, 7 and 8, 15 and 16, and 17 and 18, Table 1), set a rest time until the temperature is stable within 1 Kh-1.</li>
		<li>Perform a reference cycle after each set of 25 cycles (see step 3.2).</li>
		<li>Repeat each test once on a different fresh cell to assess its repeatability.</li>
	</ol>
	</li>
	<li>Degradation rate
	<ol>
		<li>Assess the cell degradation [Capacity Retention (CR)] using: i) the latest reference cycle and the first reference cycle, CRref (see step 3.2) and ii) the long-term Capacity Retention comparing with the first cycle, CRlong-term (see step 3.3) and the following equations (1 and 2): 
		(1)&#160; &#160;<img alt="Equation 1" src="/files/ftp_upload/57501/57501eq1.jpg" /> 
		&#8203;(2)&#160; &#160;<img alt="Equation 2" src="/files/ftp_upload/57501/57501eq2.jpg" />
		<ol>
			<li>Use the battery cycler Client software to access the cycling data. First, select the template for visualization (file open in Supplementary File 4), and select the filename defined in step 3.1.2 or 3.2.3 where appropriate. 
			NOTE: Supplementary File 5 shows an example of the cycling data, with the capacity retention as a function of the cycle number (Supplementary File 5, top graph) and the variation of potential, and the current and temperature as a function of time (Supplementary File 5, bottom graph). Equations (1) and (2) can be determined directly from the plots using the software capabilities.</li>
		</ol>
		</li>
		<li>Fit the degradation rates (DR) by using CRref and the total number of cycles (i.e., the reference cycles and long-term cycles) assuming that the DR depends on the charge Tc and discharge Td temperatures up to the quadratic term and interaction between those temperatures as follows in equation (3): 
		(3)&#160; &#160;<img alt="Equation 3" src="/files/ftp_upload/57501/57501eq3.jpg" /> 
		NOTE: Parameters Ai and their statistical significance are determined by a least-square fitting and an ANOVA, assuming that the measurement uncertainty (err) with a &#963; variance follows a normal distribution. The latter should be confirmed from the distribution of the residual of the fit.
		<ol>
			<li>For this purpose, use a software with the &#39;Fit model&#39; function. Select the Stepwise option (blue arrow No. 1 in Supplementary File 6) and choose the Max K-Fold RSquare function (blue arrow No. 2 in Supplementary File 6) and click on GO. This splits the dataset to an equivalent training subset and the fitting is done on each subset separately. Select the best overall RSquare value to avoid overfitting.</li>
			<li>Click on Make model. Supplementary File 7 shows the results of the fitting. It also calculates the significance (PValue) of each parameter (Ai). In the &#39;Effect Summary&#39; table, delete the least significant parameters. In this case, A4 (the quadratic dependence of the discharge temperature) was shown as not significant. Therefore, it was removed from further analysis. Supplementary File 8 shows the final fit with the actual data.</li>
		</ol>
		</li>
	</ol>
	</li>
</ol>
4. Post-mortem Analysis
<ol>
	<li>Disassemble the cells. Carry out this step inside a glove box (&#60; 5 ppm for O2 and H2O) to avoid contamination in the air. Cut the pouch cells using ceramic scissors. Cut small parts of the anode and cathode electrodes (5 mm x 5 mm) and mount them on scanning electron microscope (SEM) sample stubs.</li>
	<li>Avoid contamination by placing the SEM sample holder in a sealed container and directly transfer it to the SEM sample chamber through, for example, the use of a glove bag attached to the entrance of the chamber which is filled with inert gas.
	<ol>
		<li>In order to reduce the exposure to the air, maintain an overpressure of inert gas in the glove bag.</li>
	</ol>
	</li>
	<li>To examine in-depth the morphology of the electrodes before and after cycling, perform SEM imaging using two detectors for the secondary electrons: an in-lens detector and a standard secondary electron detector. Use as accelerating voltage for the in-lens detector and the secondary electron detector 1 kV and 15 kV, respectively.</li>
	<li>For each sample, characterize at least five different locations of the sample&#39;s surface to have representative SEM micrographs and to identify potential inhomogeneities of the surface. For each location, execute SEM imaging at the following magnifications: 1 kX, 3 kX, 5 kX, 10 kX, 20 kX, 50 kX, 75 kX, 100 kX, 150 kX, and 200 kX.</li>
	<li>Analyze the chemical composition of each electrode using an Energy Dispersive X-ray (EDX) spectrometer, with an 80-mm2 silicon drift detector (SDD).
	<ol>
		<li>Use an accelerating voltage of 15 kV and a working distance of 13 mm to perform the elemental analysis using secondary electron images.</li>
		<li>Select for each material at least five different locations on the specimen surface and analyze a minimum of 5 points to generate spectra.</li>
		<li>Use different magnifications, ranging from 2 kX to 25 kX, to perform a semi-quantitative analysis and also to better target any specific particles or structural changes. As a result, for each sample, collect a minimum of 25 EDX spectra to investigate the elemental composition.</li>
		<li>Before starting the chemical analysis on a given location of a specimen, use copper for the spectral calibration. Finally, average values measured at different locations of each sample, with respect to the EDX mapping, use 2 h of acquisition time.</li>
	</ol>
	</li>
</ol>

The authors Matteo Destro and Daniela Fontana are employees of Lithops S.r.l that produces the battery cells used in this article. The other authors have nothing to disclose.

The behavior for cycling at (-20 &#176;C, -20 &#176;C) (Figure 1a) can be attributed to (i) kinetic restrictions during charging (a reduced ion diffusion, a deprived charge transfer resistance at the interface of electrode/electrolyte41, a reduced ion conductivity, a charge imbalance, etc.) and/or (ii) lithium plating when charging at low temperatures can quickly diffuse when cycling at high temperatures42. When the temperature is back to 25 &#176;C, the ion diffusion is increased and there is an equilibration of the unbalanced state. This would lead to a capacity recovery. A similar behavior was not found in the literature. For the type of cells under investigation, this temperature combination is not recommended for a continuous cycling due to fast capacity decay, although there is some partial recovery of capacity after a certain recovery time at room temperature.
On the other hand, cells cycled at (12 &#176;C, 30 &#176;C) were undesirably affected by the interruption to cycle the reference evaluation (this undoubtedly prolongs the overall testing time) (Figure 1a). These samples suffered from degradation since the beginning of the cycling and they could be more susceptible to additional degradation when comparing them with the samples cycled at &#60; 12 &#176;C.
The long-term aging with Tc = Td showed a close to second order polynomial relationship between the retention in capacity and the testing temperature (for the range of -5 &#176;C to 30 &#176;C, Figure 1b). Omar et al.12 showed a similar behavior (in the temperature range from -18 &#176;C to 40 &#176;C). The value at (-20 &#176;C, -20 &#176;C) was not taken into account as its behavior is drastically different from the general trend. From capacity measurements of CRref, it appears that cycling in the range -20 &#176;C to 15 &#176;C inflicts little degradation (Figure 1b). The different behavior demonstrated by CRref and CRlong-term can be explained as they are calculated on tests performed at different temperatures and different C-rates. Thus, they are sensitive to different processes: irreversible aging (the consequences of the degradation are perpetual)12,43 and reversible aging [the consequence of aging can be restored (e.g., extended rest times)]. It can be considered that, on the one hand, CRref&#160;is sensitive to irreversible degradation and, on the other hand, CRlong-term is sensitive to both reversible and irreversible degradation.
Discharge profiles during the long-term testing remain comparable (Figure 2a); the main difference is &#62; 3 Ah (a drop in the discharge capacity)8. For the reference cycling (Figure 2b), three plateaus can be observed in the range 3.15 - 3.30 V, corresponding to the voltage difference between the cathode (3.43 V corresponding to the redox couple Fe3+/Fe2+)44 and the intercalation phases of the anode45,46. When cycling, there is a displacement to lower capacity values, due to the consumption of cyclable lithium, or a material degradation due to aging47.
When cycling at a given Tc, it was found that the long-term stability is higher at a lower Td. This is consistent with the general trend that higher temperatures lead to a higher degradation. This was observed for the three pairs of combinations evaluated and displayed in Figures 3a - 3c. Thus, cycling at Td = 30 &#176;C leads to a higher degradation than Td = -5 &#176;C, Tc being the same. Similarly, Td = 12 &#176;C is more demanding than Td = -10 &#176;C when Tc is the same (12 &#176;C).
In some circumstances, the degradation trend found for the reference cycling is opposite to that shown for the long-term cycling. This is the case for (30 &#176;C, -5 &#176;C) vs. (30 &#176;C, 30 &#176;C) and (12 &#176;C, -10 &#176;C) vs. (12 &#176;C, 12 &#176;C) cycling. The reference cycle assessment only shows the irreversible degradation, whereas the long-term aging is influenced by both irreversible and reversible effects. Besides, 1 C cycling leads to higher ohmic drops (higher at lower temperatures). If the behavior of cells tested at (30 &#176;C, -5 &#176;C) is compared to cells tested at (-5 &#176;C, 30 &#176;C), it can be concluded that in both cases there is a comparable degradation [CRlong-term around 90% (Table 1)]. However, the CRref demonstrates a lower degradation at (-5 &#176;C, 30 &#176;C). Under these conditions (i.e., a given Td), a higher Tc means more degradation, as demonstrated by Figures 4a and 4b. Tc = 30 &#176;C cycling degrades the cells more compared to Tc = -5 &#176;C (when Td is the same). This is consistent with the interpretation of the data for other cycling conditions previously discussed.
As a summary, it can be concluded that cycling at (-5 &#176;C, -5 &#176;C), (0 &#176;C, -20 &#176;C), (5 &#176;C, 5 &#176;C), (12 &#176;C, -10 &#176;C) and (15 &#176;C, -20 &#176;C) over 100 cycles led to almost no degradation. The samples tested at Td = -20 &#176;C proved to be stable (recovery in capacity at +25 &#176;C, Figure 4c), making these samples suitable for sub-room temperature applications. This capacity recovery is less impressive when increasing Tc. The behavior shown by this set of samples indicates that there is a big component of reversible degradation at low temperatures (kinetic component).
The initial condition of the surface of the anode material (graphite) is typically smooth (Figures 6a and 6d). After cycling, the surface roughens, also observed by others48. The change in morphology is more obvious in the bumped zone (Figures6b and 6e) compared to the central part of the electrode (Figures 6c and 6f). When the magnification is increased, hemispherical particles are visible in the bumped zone (Figure 6e). These structures have an average diameter of 35 to 175 nm and have also been observed by others49,50,51. In these studies, they have been assigned to the plating of granular metallic Li particles49,50 on which the SEI layer grows50. A possible explanation for this platting can be assigned to: (i) some degree of overcharge as described by Lu et al.49 (10% overlithiation) or (ii) inhomogeneous compression on the electrodes as studied by Bach et al.52.
The secondary electron SEM depicts bright particles distributed in a cycled anode (Figure 6i). These particles are less visible in the rippled zone (supplementary data, FigureS1) and are not visible in the bump zone (Figure 6h). EDX investigations identified these particles as metallic Cu (see insert in Figure 6i and supplementary data in Figure S2). It is possible that Cu (current collector) dissolves and precipitates on the electrode (e.g., current collector corrosion occurs due to the reactivity with the electrolyte and when the anode potential is too positive vs. Li/Li+)28. In the bumped zone, traces of Cu having a concentration above the background signal have been also abserved. It is can be speculated that for some reason, the conditions in that zone do not favor the precipitation of Cu. Finally, traces of Fe have also been measured. This can be attributed to the dissolution of iron from cathode material (LiFePO4), as identified by others48,53,54. LiPF6 based electrolytes (HF traces)55, an evaluation of the cycled cathode showed no alterations compared to the fresh material (supplementary material, Figure S3). Further experiments are underway in order to characterize further these cathode materials.
The degradation rates (DRs) from Table 1 calculated from CRref were plotted vs. testing temperatures (charging and discharging), then fitted by the least-square method (2D). Figure 7 displays the surface-fitting generated, where the dots are the measured DRs. The dataset was divided into learning and verification datasets for the fitting. A polynomial function was selected (best R2). The red represents conditions with lower DRs and the blue represents conditions with higher DRs. The resultant model equation is:
(4)&#160; &#160;<img alt="Equation 4" src="/files/ftp_upload/57501/57501eq4.jpg" /><img alt="Equation 5" src="/files/ftp_upload/57501/57501eq5.jpg" />
The statistical significance of the polynomial coefficients, confirmed by ANOVA, leads to a quadratic relationship of DR with Tc and a linear relationship with Td.
Other observations that can be helpful if suitable applications need to be selected: when Tc is around15 &#176;C, DR is not-dependent of Td; when Tc &#60; 15 &#176;C, a higher degradation happens at a higher Td; when Tc &#62; 15 &#176;C, a lower degradation occurs at a higher Td; the lowest DR corresponds to (Tc = -7 &#176;C, Td = -20 &#176;C); the highest DR corresponds to (Tc = 30 &#176;C, Td = -20 &#176;C) or (Tc = -20 &#176;C, Td = 30 &#176;C).
The results presented in this work may be of relevance for the design of future standards and regulations in order to represent more realistic scenarios. Further experiments using other chemistries are needed to check the validity of these conclusions in order to find an optimum operating range depending on the application. Additional work will evaluate the effects of calendar aging.

Durability has become one of the pivotal topics of interest in lithium-ion batteries (LIB)1,2,3 research, not neglecting safety behavior, performance, and cost. Battery degradation is especially challenging for e-mobility applications as a relatively long lifetime is required4,5,6 compared to other applications (e.g., a few years for consumer electronics). The initial performance of LIBs (e.g., in terms of capacity and resistance) deteriorates over time due to electrochemical and calendar aging. Many factors (e.g., electrode material, environmental conditions, current loads, and cut-off voltages) can be decisive in degradation. The literature identifies temperature as one of the main factors affecting the degradation of electrode active materials and electrode-electrolyte side reactions7. Despite the vast amount of publications in the literature dealing with battery durability at different temperatures1,8,9,10,11,12, these studies only represent the specific cells, methods, and settings used. Hence, extrapolation to other cells is not trivial, making a quantitative comparison between different studies very difficult.
It can be anticipated that the cycling at different charging and discharging may have some influence on the degradation behavior of the battery because many of the processes causing degradation are temperature dependent. Moreover, in a number of applications, different charging and discharging temperatures represent a more convincing scenario [e.g., the battery of an e-bike charged in a temperature-controlled environment (indoor) and the e-bike cycled (i.e., discharged) at various temperatures (outdoor); seasonal and daily temperature fluctuations are experienced in many applications]. However, aging test results published in the literature usually study the same temperature for the charging and discharging steps. Also, relevant standards13,14,15,16,17 and test method manuals18,19,20 use the same temperature. We found in the literature one example of cycling at different temperatures (e.g., 45 &#176;C, 65 &#176;C)21 for the charge and discharge. The authors of this work described a higher fade in capacity at the higher temperature of discharge, which was attributed to solid electrolyte interface (SEI) layer growth and lithium plating21. The evaluation of battery degradation under conditions representative of realistic scenarios is desirable. Future standards and regulations might benefit from the results presented in this work on the testing of charge and discharge at different temperatures22.
As a general rule, higher testing temperatures accelerate degradation1,11,12, enhance the growth of the SEI11,23,24, and promote variations in the SEI11,23. On the other hand, low-temperature cycling results in unlikely challenges: plating and dendrite growth are facilitated (slow lithium-ion diffusion)25,26,27,28. Lithium metal can react further with the electrolyte leading to a reduced durability and reduced safety degree28,29.
Wang et al.8 published that the fade in capacity followed a power law relationship with the charge throughput (temperatures between 15 &#176;C and 60 &#176;C). Other authors have described a square-root of time relationship with fade in capacity10,30,31,32,33,34. This is supposed to represent the irreversible capacity loss attributed to the growth of SEI30,31 where active lithium is consumed. Capacity degradation also may have a share of linear degradation with time33,34,35. Finally, some simulations of the fade in capacity at various temperatures were validated with experimental results and the data showed an exponential dependency of degradation and temperature8,10.
In this work, the effect of different temperatures of charge and discharge on the degradation behavior of lithium iron phosphate (LFP)/graphite cells designed for sub-ambient temperatures is described. The number of possible temperature combinations was minimized using a design of experiment (DOE) method36; an approach used commonly in industrial optimization processes. This method was also applied by Forman et al.37 to study battery degradation, providing the minimum prediction error (D-Optimum). Alternatively, Muenzel et al.38 developed a multi-factor life prediction model reusing data from Omar et al.12. The data was fitted, and a degradation matrix was obtained.
In the current work, the data obtained was fitted by a non-linear least square fitting (polynomial) which includes first-order interactions between temperatures of charge and discharge. An analysis of variance (ANOVA) was used to evaluate the coefficients and the degree of polynomial. The method helps to understand the effect of temperatures of charge and discharge and their possible interactions. This information can be relevant to support the establishment of future fit for purpose and realistic protocols and standards.

<table><tbody><tr><td>artificial graphite&amp;nbsp;</td><td>IMERYS</td><td></td><td>D50 about 6&amp;nbsp;&amp;micro;m. Catalog number cannot be disclosed for propietary reasons</td></tr><tr><td>lithium iron phosphate</td><td>BASF</td><td></td><td>D50 about 11&amp;nbsp;&amp;micro;m. Catalog number cannot be disclosed for propietary reasons</td></tr><tr><td>Cu foil&amp;nbsp; &amp;nbsp;</td><td>Schlenk</td><td></td><td>16&amp;nbsp;&amp;micro;m thickness. Catalog number cannot be disclosed for propietary reasons&amp;nbsp;</td></tr><tr><td>Al foil</td><td>Showa Denko</td><td></td><td>20&amp;nbsp;&amp;micro;m thickness. Catalog number cannot be disclosed for propietary reasons&amp;nbsp;</td></tr><tr><td>separator&amp;nbsp;</td><td>Celgard</td><td></td><td>separator. Catalog number cannot be disclosed for propietary reasons</td></tr><tr><td>Maccor cycler</td><td>Maccor</td><td>Maccor Series 4000&amp;nbsp;</td><td>Battery cycler</td></tr><tr><td>BIA chamber</td><td>BIA</td><td>BIA MTH 4.46&amp;nbsp;</td><td>environmental temperature chambers</td></tr><tr><td>SEM</td><td>Carl Zeiss, Germany</td><td>ZEISS SUPRA 50</td><td>Scanning Electron Microscope</td></tr><tr><td>EDAX</td><td>Oxford Instruments, UK</td><td>&amp;nbsp;Oxford X-MaxN 80&amp;nbsp;</td><td>Energy Dispersive X-ray spectrometer</td></tr><tr><td>SDD</td><td>Oxford Instruments, UK</td><td>AZtec software</td><td>Drift detector&amp;nbsp;</td></tr></tbody></table>

the effect of charging and discharging lithium iron phosphate-graphite cells at different temperatures on degradation

The effect of charging and discharging lithium iron phosphate-graphite cells at different temperatures on their degradation is evaluated systematically. The degradation of the cells is assessed by using 10 charging and discharging temperature permutations ranging from -20 &#176;C to 30 &#176;C. This allows an analysis of the effect of charge and discharge temperatures on aging, and their associations. A total of 100 charge/discharge cycles were carried out. Every 25 cycles a reference cycle was performed to assess the reversible and irreversible capacity degradation. A multi-factor analysis of variance was used, and the experimental results were fitted showing: i) a quadratic relationship between the rate of degradation and the temperature of charge, ii) a linear relationship with the temperature of discharge, and iii) a correlation between the temperature of charge and discharge. It was found that the temperature combination for charging at +30 &#176;C and discharging at -5 &#176;C led to the highest rate of degradation. On the other hand, the cycling in a temperature range from -20 &#176;C to 15 &#176;C (with various combinations of temperatures of charge and discharge), led to a much lower degradation. Additionally, when the temperature of charge is 15 &#176;C, it was found that the degradation rate is nondependent on the temperature of discharge.

Watch this Scientific Journal Video about The Effect of Charging and Discharging Lithium Iron Phosphate-graphite Cells at Different Temperatures on Degradation at JoVE.com

The Effect of Charging and Discharging Lithium Iron Phosphate-graphite Cells at Different Temperatures on Degradation

This article describes the effect of dissimilar charging/discharging temperatures on the degradation of lithium iron phosphate-graphite pouch cells, aiming at simulating close to real case scenarios. In total, 10 temperature combinations are investigated in the range -20 to 30 &#176;C in order to analyze the impact of temperature on degradation.

The effect of charging and discharging lithium iron phosphate-graphite cells at different temperatures on their degradation is evaluated systematically. ...

the-effect-charging-discharging-lithium-iron-phosphate-graphite-cells

Universidad de Cartagena UDEC

Research

JoVE Journal

Chemistry

15.4K Views.  European Commission, Joint Research Centre (JRC). This article describes the effect of dissimilar charging/discharging temperatures on the degradation of lithium iron phosphate-graphite pouch cells, aiming at simulating close to real case scenarios. In total, 10 temperature combinations are investigated in the range -20 to 30 °C in order to analyze the impact of temperature on degradation.

Video: The Effect of Charging and Discharging Lithium Iron Phosphate-graphite Cells at Different Temperatures on Degradation

Isolation and Chemical Characterization of Lipid A from Gram-negative Bacteria

Lipopolysaccharide (LPS) is the major cell surface molecule of gram-negative bacteria, deposited on the outer leaflet of the outer membrane bilayer. LPS can be subdivided into three domains: the distal O-polysaccharide, a core oligosaccharide, and the lipid A domain consisting of a lipid A molecular species and 3-deoxy-D-manno-oct-2-ulosonic acid residues (Kdo). The lipid A domain is the only component essential for bacterial cell survival. Following its synthesis, lipid A is chemically modified in response to environmental stresses such as pH or temperature, to promote resistance to antibiotic compounds, and to evade recognition by mediators of the host innate immune response. The following protocol details the small- and large-scale isolation of lipid A from gram-negative bacteria. Isolated material is then chemically characterized by thin layer chromatography (TLC) or mass-spectrometry (MS). In addition to matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF) MS, we also describe tandem MS protocols for analyzing lipid A molecular species using electrospray ionization (ESI) coupled to collision induced dissociation (CID) and newly employed ultraviolet photodissociation (UVPD) methods. Our MS protocols allow for unequivocal determination of chemical structure, paramount to characterization of lipid A molecules that contain unique or novel chemical modifications. We also describe the radioisotopic labeling, and subsequent isolation, of lipid A from bacterial cells for analysis by TLC. Relative to MS-based protocols, TLC provides a more economical and rapid characterization method, but cannot be used to unambiguously assign lipid A chemical structures without the use of standards of known chemical structure. Over the last two decades isolation and characterization of lipid A has led to numerous exciting discoveries that have improved our understanding of the physiology of gram-negative bacteria, mechanisms of antibiotic resistance, the human innate immune response, and have provided many new targets in the development of antibacterial compounds.

Isolation and characterization of the lipid A domain of lipopolysaccharide (LPS) from gram-negative bacteria provides insight into cell surface based mechanisms of antibiotic resistance, bacterial survival and fitness, and how chemically diverse lipid A molecular species differentially modulate host innate immune responses.

Lipopolysaccharide (LPS) is the major cell surface molecule of gram-negative bacteria, deposited on the outer leaflet of the outer membrane bilayer. LPS ...

Solid-state Graft Copolymer Electrolytes for Lithium Battery Applications

Battery safety has been a very important research area over the past decade. Commercially available lithium ion batteries employ low flash point (&lt;80 &deg;C), flammable, and volatile organic electrolytes. These organic based electrolyte systems are viable at ambient temperatures, but require a cooling system to ensure that temperatures do not exceed 80 &deg;C. These cooling systems tend to increase battery costs and can malfunction which can lead to battery malfunction and explosions, thus endangering human life. Increases in petroleum prices lead to a huge demand for safe, electric hybrid vehicles that are more economically viable to operate as oil prices continue to rise. Existing organic based electrolytes used in lithium ion batteries are not applicable to high temperature automotive applications. A safer alternative to organic electrolytes is solid polymer electrolytes. This work will highlight the synthesis for a graft copolymer electrolyte (GCE) poly(oxyethylene) methacrylate (POEM) to a block with a lower glass transition temperature (Tg) poly(oxyethylene) acrylate (POEA). The conduction mechanism has been discussed and it has been demonstrated the relationship between polymer segmental motion and ionic conductivity indeed has a Vogel-Tammann-Fulcher (VTF) dependence. Batteries containing commercially available LP30 organic (LiPF6 in ethylene carbonate (EC):dimethyl carbonate (DMC) at a 1:1 ratio) and GCE were cycled at ambient temperature. It was found that at ambient temperature, the batteries containing GCE showed a greater overpotential when compared to LP30 electrolyte. However at temperatures greater than 60 &deg;C, the GCE cell exhibited much lower overpotential due to fast polymer electrolyte conductivity and nearly the full theoretical specific capacity of 170 mAh/g was accessed.

Lithium ion batteries employ flammable and volatile organic electrolytes that are suitable for ambient temperature applications. A safer alternative to organic electrolytes are solid polymer batteries. Solid polymer batteries operate safely at high temperatures (&gt;120 &deg;C), thus making them applicable to high temperature applications such as deep oil drilling and hybrid electric vehicles. This paper will discuss (a) the polymer synthesis, (b) the polymer conduction mechanism, and (c) provide temperature cycling for both solid polymer and organic electrolytes.

Battery safety has been a very important research area over the past decade. Commercially available lithium ion batteries employ low flash point (&lt;80 ...

Engineering

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Protein alignments are commonly used to evaluate the similarity of protein residues, and the derived consensus sequence used for identifying functional units (e.g., domains). Traditional consensus-building models fail to account for interpositional dependencies &#8211; functionally required covariation of residues that tend to appear simultaneously throughout evolution and across the phylogentic tree. These relationships can reveal important clues about the processes of protein folding, thermostability, and the formation of functional sites, which in turn can be used to inform the engineering of synthetic proteins. Unfortunately, these relationships essentially form sub-motifs which cannot be predicted by simple &#8220;majority rule&#8221; or even HMM-based consensus models, and the result can be a biologically invalid &#8220;consensus&#8221; which is not only never seen in nature but is less viable than any extant protein. We have developed a visual analytics tool, StickWRLD, which creates an interactive 3D representation of a protein alignment and clearly displays covarying residues. The user has the ability to pan and zoom, as well as dynamically change the statistical threshold underlying the identification of covariants. StickWRLD has previously been successfully used to identify functionally-required covarying residues in proteins such as Adenylate Kinase and in DNA sequences such as endonuclease target sites.

Synthetic protein sequences based on consensus motifs typically ignore co-evolving residues, that imply interpositional dependencies (IPDs). IPDs can be essential to activity, and designs that disregard them may result in suboptimal results. This protocol uses StickWRLD to identify IPDs and help inform rational protein design, resulting in more efficient results.

Protein alignments are commonly used to evaluate the similarity of protein residues, and the derived consensus sequence used for identifying functional ...

In Situ Neutron Powder Diffraction Using Custom-made Lithium-ion Batteries

Li-ion batteries are widely used in portable electronic devices and are considered as promising candidates for higher-energy applications such as electric vehicles.1,2 However, many challenges, such as energy density and battery lifetimes, need to be overcome before this particular battery technology can be widely implemented in such applications.3 This research is challenging, and we outline a method to address these challenges using in situ NPD to probe the crystal structure of electrodes undergoing electrochemical cycling (charge/discharge) in a battery. NPD data help determine the underlying structural mechanism responsible for a range of electrode properties, and this information can direct the development of better electrodes and batteries.
We briefly review six types of battery designs custom-made for NPD experiments and detail the method to construct the &#8216;roll-over&#8217; cell that we have successfully used on the high-intensity NPD instrument, WOMBAT, at the Australian Nuclear Science and Technology Organisation (ANSTO). The design considerations and materials used for cell construction are discussed in conjunction with aspects of the actual in situ NPD experiment and initial directions are presented on how to analyze such complex in situ data.

In Situ Neutron Powder Diffraction Using Custom-made Lithium-ion Batteries

We describe the design and construction of an electrochemical cell for the examination of electrode materials using in situ neutron powder diffraction (NPD). We briefly comment on alternate in situ NPD cell designs and discuss methods for the analysis of the corresponding in situ NPD data produced using this cell.

Li-ion batteries are widely used in portable electronic devices and are considered as promising candidates for higher-energy applications such as electric ...

Non-aqueous Electrode Processing and Construction of Lithium-ion Coin Cells

Research into new and improved materials to be utilized in lithium-ion batteries (LIB) necessitates an experimental counterpart to any computational analysis. Testing of lithium-ion batteries in an academic setting has taken on several forms, but at the most basic level lies the coin cell construction. In traditional LIB electrode preparation, a multi-phase slurry composed of active material, binder, and conductive additive is cast out onto a substrate. An electrode disc can then be punched from the dried sheet and used in the construction of a coin cell for electrochemical evaluation. Utilization of the potential of the active material in a battery is critically dependent on the microstructure of the electrode, as an appropriate distribution of the primary components are crucial to ensuring optimal electrical conductivity, porosity, and tortuosity, such that electrochemical and transport interaction is optimized. Processing steps ranging from the combination of dry powder, wet mixing, and drying can all critically affect multi-phase interactions that influence the microstructure formation. Electrochemical probing necessitates the construction of electrodes and coin cells with the utmost care and precision. This paper aims at providing a step-by-step guide of non-aqueous electrode processing and coin cell construction for lithium-ion batteries within an academic setting and with emphasis on deciphering the influence of drying and calendaring.

Non-aqueous electrode processing is central to the construction of coin cells and the evaluation of new electrode chemistries for lithium-ion batteries. A step-by-step guide to the basic practices needed as an electrochemical engineer working with batteries in an academic experimental setting is furnished.

Research into new and improved materials to be utilized in lithium-ion batteries (LIB) necessitates an experimental counterpart to any computational ...

Synthesis of Ionic Liquid Based Electrolytes, Assembly of Li-ion Batteries, and Measurements of Performance at High Temperature

The chemical instability of the traditional electrolyte remains a safety issue in widely used energy storage devices such as Li-ion batteries. Li-ion batteries for use in devices operating at elevated temperatures require thermally stable and non-flammable electrolytes. Ionic liquids (ILs), which are non-flammable, non-volatile, thermally stable molten salts, are an ideal replacement for flammable and low boiling point organic solvent electrolytes currently used today. We herein describe the procedures to: 1) synthesize mono- and di-phosphonium ionic liquids paired with chloride or bis(trifluoromethane)sulfonimide (TFSI) anions; 2) measure the thermal properties and stability of these ionic liquids by differential scanning calorimetry (DSC) and thermal gravimetric analysis (TGA); 3) measure the electrochemical properties of the ionic liquids by cyclic voltammetry (CV); 4) prepare electrolytes containing lithium bis(trifluoromethane)sulfonamide; 5) measure the conductivity of the electrolytes as a function of temperature; 6) assemble a coin cell battery with two of the electrolytes along with a Li metal anode and LiCoO2 cathode; and 7) evaluate battery performance at 100 &#176;C. We additionally describe the challenges in execution as well as the insights gained from performing these experiments.

Here, we describe protocols to prepare phosphonium-based ionic liquid and lithium bis(trifluoromethane)sulfonimide salt electrolytes, and assemble a non-flammable and high temperature functioning lithium-ion coin cell battery.

The chemical instability of the traditional electrolyte remains a safety issue in widely used energy storage devices such as Li-ion batteries. Li-ion ...

In Situ Monitoring of the Accelerated Performance Degradation of Solar Cells and Modules: A Case Study for Cu(In,Ga)Se2 Solar Cells

The levelized cost of electricity (LCOE) of photovoltaic (PV) systems is determined by, among other factors, the PV module reliability. Better prediction of degradation mechanisms and prevention of module field failure can consequently decrease investment risks as well as increase the electricity yield. An improved knowledge level can for these reasons significantly decrease the total costs of PV electricity. 
In order to better understand and minimize the degradation of PV modules, the occurring degradation mechanisms and conditions should be identified. This should preferably happen under combined stresses, since modules in the field are also simultaneously exposed to multiple stress factors. Therefore, two 'Combined Stress test with in situ measurement' setups have been designed and constructed. These setups allow the simultaneous use of humidity, temperature, illumination, and electrical biases as independently controlled stress factors on solar cells and minimodules. The setups also allow real-time monitoring of the electrical properties of these samples. This protocol presents these setups and describes the experimental possibilities. Moreover, results obtained with these setups are also presented: various examples about the influence of both deposition and degradation conditions on the stability of thin film Cu(In,Ga)Se2 (CIGS) as well as Cu2ZnSnSe4 (CZTS) solar cells are described. Results on the temperature dependency of CIGS solar cells are also presented.

In Situ Monitoring of the Accelerated Performance Degradation of Solar Cells and Modules: A Case Study for CuIn,GaSe2 Solar Cells

Two &#39;Combined Stress test with in situ measurement&#39; setups, which allow real-time monitoring of accelerated degradation of solar cells and modules, were designed and constructed. These setups allow the simultaneous use of humidity, temperature, electrical biases, and illumination as independently controlled stress factors. The setups and various experiments executed are presented.

The levelized cost of electricity (LCOE) of photovoltaic (PV) systems is determined by, among other factors, the PV module reliability. Better prediction ...

Accelerating Rate Calorimetry and Complementary Techniques to Characterize Battery Safety Hazards

The hazards associated with lithium-based battery chemistries are well-documented due to their catastrophic nature. Risk is typically qualitatively assessed through an engineering risk matrix. Within the matrix, potentially hazardous events are categorized and ranked in terms of severity and probability to provide situational awareness to decision makers and stakeholders. The stochastic nature of battery failures, particularly the lithium-ion chemistry, makes the probability axis of a matrix difficult to properly assess. Fortunately, characterization tools exist, such as accelerated rate calorimetry (ARC), that characterize degrees of battery failure severity. ARC has been used extensively to characterize reactive chemicals but can provide a new application to induce battery failures under safe, controlled experimental conditions and quantify critical safety parameters. Due to the robust nature of the extended volume calorimeter, cells may be safely taken to failure due to a variety of abuses: thermal (simple heating of cell), electrochemical (overcharge), electrical (external short circuit), or physical (crush or nail penetration). This article describes the procedures to prepare and instrument a commercial lithium-ion battery cell for failure in an ARC to collect valuable safety data: onset of thermal runaway, endotherm associated with polymer separator melting, pressure release during thermal runaway, gaseous collection for analytical characterization, maximum temperature of complete reaction, and visual observation of decomposition processes using a high temperature borescope (venting and cell can breach). A thermal &#8220;heat-wait-seek&#8221; method is used to induce cell failure, in which the battery is heated incrementally to a set point, then the instrument identifies heat generation from the battery. As heat generates a temperature rise in the battery, the calorimeter temperature follows this temperature rise, maintaining an adiabatic condition. Therefore, the cell does not exchange heat with the external environment, so all heat generation from the battery under failure is captured.

A method to characterize the potential failure hazards of lithium batteries is achieved with accelerating rate calorimetry. Heat and pressure release, visual observation of the failure event, and the capture of evolved gases are collected in this experiment to identify the worst credible threats of batteries taken to failure.

The hazards associated with lithium-based battery chemistries are well-documented due to their catastrophic nature. Risk is typically qualitatively ...

Identification and Quantification of Decomposition Mechanisms in Lithium-Ion Batteries; Input to Heat Flow Simulation for Modeling Thermal Runaway

The risks and possible accidents related to the normal use of lithium-ion batteries remain a serious concern. In order to get a better understanding of thermal runaway (TR), the exothermic decomposition reactions in anode and cathode were studied, using a Simultaneous Thermal Analysis (STA)/Gas Chromatography-Mass Spectrometry (GC-MS)/Fourier Transform Infrared (FTIR) spectrometer system. These techniques allowed the identification of the reaction mechanisms in each electrode, owing to the analysis of evolved gaseous species, the amount of heat released and mass loss. These results provided insight into the thermal events happening within a broader temperature range than covered in previously published models. This allowed the formulation of an improved thermal model to depict TR. The heat of reaction, activation energy, and frequency factor (thermal triplets) for each major exothermic process at material level were investigated in a Lithium Nickel-Manganese-Cobalt-Oxide (NMC (111))-Graphite battery cell. The results were analyzed, and their kinetics were derived. These data can be used to successfully simulate the experimental heat flow.

This work aims at determining the reaction kinetics of Li-ion battery cathode and anode materials undergoing thermal runaway (TR). Simultaneous Thermal Analysis (STA)/Fourier Transform Infrared (FTIR) spectrometer/Gas Chromatography Mass Spectrometry (GC-MS) were used to reveal thermal events and to detect evolved gases.

The risks and possible accidents related to the normal use of lithium-ion batteries remain a serious concern. In order to get a better understanding of ...

Optimizing Battery Heat Management in Dusty Environments

Optimization of An Air-Based Heat Management System for Dusty Particulate Matter-Covered Lithium-Ion Battery Packs

This study aims to solve the problem of the cell temperature rise and performance decline caused by dusty particulate matter covering the surface of the cell through the allocation of airflow velocities at the inlets of the battery cooling box under the goal of low energy consumption. We take the maximum temperature of the battery pack at a specified airflow velocity and dust-free environment as the expected temperature in a dusty environment. The maximum temperature of the battery pack in a dusty environment is solved at different inlet airflow velocities, which are the boundary conditions of the analysis model constructed in the simulation software. The arrays representing the different airflow velocity combinations of inlets are generated randomly through the optimal Latin hypercube algorithm (OLHA), where the lower and upper limits of velocities corresponding to the temperatures above the desired temperature are set in the optimization software. We establish an approximate QRSM between the velocity combination and the maximum temperature using the fitting module of the optimization software. The QRSM is optimized based on the ASAM, and the optimal result is in good agreement with the analysis result obtained by the simulation software. After optimization, the flow rate of the middle inlet is changed from 5.5 m/s to 5 m/s, and the total airflow velocity is decreased by 3%. The protocol here presents an optimization method simultaneously considering energy consumption and thermal performance of the battery management system that has been established, and it can be widely used to improve the life cycle of the battery pack with minimum operating cost.

Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption 

Here, we present the adaptive simulated annealing method (ASAM) to optimize an approximate quadratic response surface model (QRSM) corresponding to a dusty particulate matter-covered battery heat management system and fulfill the temperature drops back by adjusting the airflow velocities combination of system inlets.

This study aims to solve the problem of the cell temperature rise and performance decline caused by dusty particulate matter covering the surface of the ...

The Effect of Charging and Discharging Lithium Iron Phosphate-graphite Cells at Different Temperatures on Degradation

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Chapters in this video

Isolation and Chemical Characterization of Lipid A from Gram-negative Bacteria

Solid-state Graft Copolymer Electrolytes for Lithium Battery Applications

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

In Situ Neutron Powder Diffraction Using Custom-made Lithium-ion Batteries

Non-aqueous Electrode Processing and Construction of Lithium-ion Coin Cells

Synthesis of Ionic Liquid Based Electrolytes, Assembly of Li-ion Batteries, and Measurements of Performance at High Temperature

In Situ Monitoring of the Accelerated Performance Degradation of Solar Cells and Modules: A Case Study for Cu(In,Ga)Se₂ Solar Cells

Accelerating Rate Calorimetry and Complementary Techniques to Characterize Battery Safety Hazards

Identification and Quantification of Decomposition Mechanisms in Lithium-Ion Batteries; Input to Heat Flow Simulation for Modeling Thermal Runaway

Optimization of An Air-Based Heat Management System for Dusty Particulate Matter-Covered Lithium-Ion Battery Packs