The kind of power that is based upon the ability to give or…
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The kind оf pоwer thаt is bаsed upоn the аbility to give or rescind things perceived to be of value is called
CEE 582 – Industriаl Ecоlоgy аnd Design fоr Sustаinability Applied Project 2 Instructions To ensure proper grading, please upload any handwritten material (through a scan or photo in PDF format), Python files, and Excel files to Canvas when submitting Applied Project 2 Please make sure you provide a detailed explanation of the steps you used to arrive at the answer Use of AI and the internet is not allowed. Collaboration not allowed. Access to the lecture slides and the Python and MS Excel files provided in the modules is allowed. You will be given four hours to complete this. Use full uniform distributions for Parts C, D, and E unless otherwise specified. Do not use mean values for uncertainty or sensitivity analysis. Lithium-Ion Battery Supply Chain Analysis Background and Context Lithium-ion batteries are central to the electrification of transportation. The supply chain behind them is not a simple linear process: lithium is extracted from natural deposits, converted into lithium carbonate, transported to manufacturing facilities, and assembled into battery materials. Material is lost at each stage, some scrap is recycled internally, and a fraction of material accumulates as stock at the manufacturing site. Two commercially important extraction pathways exist and are compared in this project: Brine-based extraction – lithium is recovered from brine deposits and requires processing very large quantities of brine to obtain a relatively small amount of lithium carbonate. Hard-rock extraction – lithium is mined and processed from solid ore rather than extracting it from solution. Your task is to evaluate and compare both pathways using Material Flow Analysis (Part A), matrix-based Life Cycle Assessment (Part B), and Uncertainty Analysis (Part C). System Description Functional Unit and System Basis The functional unit for Parts C, D and E is 1 lithium-ion battery. Use the following fixed relationships: One lithium-ion battery requires approximately 0.13 kg of elemental lithium per battery Lithium is supplied in the form of lithium carbonate 5.32 kgs of lithium carbonate are required per kg of lithium For both MFA and LCA, assume that this lithium carbonate is the material used in battery manufacturing (and not elemental lithium) Extraction Pathways Brine-based pathway: On average,1112 kg of brine must be processed to produce 1 kg of lithium carbonate [kg brine/kg Li2CO3]. This reflects both the low lithium concentration in natural brine deposits and inefficiencies in the extraction process. Hard-rock pathway: On average, approximately 55.32 kg of hard-rock ore must be processed to produce 1 kg of lithium carbonate [kg ore/kg Li2CO3]. This reflects both the lithium content of the ore and losses during beneficiation and chemical processing. Compared to the brine pathway, the mass of material processed per unit of product is significantly lower, but the processing steps are more energy intensive. As a result, environmental impacts in the hard-rock pathway are more strongly driven by energy use and associated emissions, rather than by the total mass of material handled. Energy Consumption and CO2 Emission Factor Energy consumption is expressed per unit mass (kg) of lithium carbonate produced: In the brine pathway, energy use has a mean value of 15 megajoules per kg of lithium carbonate, with an uncertainty of ±30%, corresponding to a uniform distribution between 10.5 and 19.5 MJ/kg Li₂CO₃. In the hard-rock pathway, energy use is higher, with a mean value of 45 MJ/kg Li₂CO₃, also with ±30% uncertainty, corresponding to a uniform distribution between 31.5 and 58.5 MJ/kg Li₂CO₃. The emission factor represents the carbon intensity of the energy supply and is defined as kg of carbon dioxide emitted per megajoule of energy consumed. The mean value is 0.07 kg CO₂/MJ, with an uncertainty of ±0.01 kg CO₂/MJ, corresponding to a uniform distribution ranging between 0.06 and 0.08 kg CO₂/MJ. Battery Manufacturing During battery manufacturing, approximately 5% of Li2CO3 becomes scrap and 7% of the Li2CO3 is retained as stock in the manufacturing site. The total Li2CO3 required for battery production (Q) should account for this additional scrap and stock requirements. Water Consumption Water consumption arises from multiple subprocesses and differs between the two pathways. In the brine pathway, water use is dominated by evaporation and chemical treatment processes. Evaporation becomes less efficient as recovery decreases, leading to a nonlinear increase in water consumption. In addition, unrecovered lithium must still undergo treatment, contributing water demand proportional to the unrecovered fraction. In the hard-rock pathway, water is used in washing and mineral separation processes. Lower beneficiation yield increases the amount of material that must be processed per unit output, increasing water use. However, a fraction of the water is recycled internally, reducing net consumption. For this analysis, water consumption per battery (W brine_per_battery, W hard_per_battery) and total water consumption (Wbrine_total, Whard_total) should be calculated using the following expressions: W brine_per_battery=WevapRb2+Wtreat(1-Rb) Wbrine_total=Q x Wbrine_per_batteryWhard_per_battery=Wwashh+Wreuse(1-h) Whard_total=Q x Whard_per_battery where Qis the lithium carbonate requirement per battery, Rbis brine recovery rate, his hard-rock yield, and his the fraction of water recycled internally. The parameters Wevap,Wtreat,Wwash,and Wreuseare water intensities expressed in cubic meters per kilogram of lithium carbonate and should be assumed to be uniformly distributed within the following ranges: Wevap=12–18m³/kg Li₂CO₃ Wtreat=2–5m³/kg Li₂CO₃ Wwash=3–6m³/kg Li₂CO₃ Wreuse=1–2m³/kg Li₂CO₃ h=0.70–0.90kg/kg h=0.80–0.95kg/kg Rb = 0.40 to 0.70
Nоmurа, K., Andreаzzа, F., Cheng, J., Dоng, K., Zhоu, P., & He, S. Y. (2023). Bacterial pathogens deliver water- and solute-permeable channels to plant cells. Nature, 621(7979), 586–591. This is the reference information for an article that appeared recently in the journal Nature about a protein secreted by pathogenic bacteria that cause brown spot in beans, bacterial spec in tomatoes and fire blight in fruit trees. These bacteria infect plant cells, causing them to drain cell material, leading to cell death. Because of the economic impact of this infection, groups have worked for years to elucidate the mechanism by which this protein infects plant cells. The Nature article reports that alpha fold in combination with cryo EM produced a protein structure that led to the solution of this 30-year-old puzzle. Below is a portion of the text from the article along with one of the figures depicting the structure of the protein. Based upon the structural information in the text and the figure, suggest a possible way this protein could drain a plant cell such that cell death results. Image Description AlphaFold2 analysis and cryo-EM imagingTo gain functional insights into the AvrE family of bacterial effectors, we constructed their three-dimensional models predicted by AlphaFold226 using the fast homology search of MMseqs2 (ColabFold) 27. The predicted AlphaFold2 models of DspE from E. amylovora, DspE from P. carotovorum, AvrE from P. syringae pv. tomato (Pst) DC3000 and WtsE from P. stewartii (Fig. 1 and Extended Data Figs. 1 and 2) all reveal an overall similar architecture resembling a mushroom, with a prominent central ẞ-barrel forming the stem, which is surrounded by a globular amino-terminal domain (E. amylovora DspE: K298-H672), a WD40 repeat domain (H673-P912) and two perpendicularly arranged helix bundles (E998-T1222 and A1567-H1647) on the top. The predicted domain arrangement is supported by our cryo- EM imaging of E. amylovora DspE, for which the two-dimensional class averages clearly reveal an overall similar top view to that of the AlphaFold model, with circularly arranged globular domains surrounding a central pore (Fig. 1a,b). Image Description Fig. 1: Model and cryo-EM images of E. amylovora DspE. (a) Three-dimensional model generated by AlphaFold2 using MMseqs2 (ColabFold). DspE (residues 298–1838) is shown in a rainbow color gradient, with the N terminus in blue and the C terminus in red. (b) Cryo-EM two-dimensional class averages of DspE, revealing a circular arrangement of domains around a pore. Scale bars, 5 nm. (c) Surface representation of DspE. (d) Sliced view of DspE. In (c, d), residues are colored based on hydrophobicity.