Update app.py

app.py

@@ -183,9 +183,7 @@ with st.sidebar.expander("More on Energy Mix"):
     "Demand response" (or data center flexibility) could theoretically pull gigawatts "out of thin air" by matching AI training jobs to times when the grid has spare capacity.$^{5}$ However, many experts remain skeptical of the true magnitude of this solution, as large-scale implementation faces significant technical hurdles and pushback from major grid operators like PJM.$^{5,6}$
 
     **What about Solar?**
-    While solar prices have dropped ~88% since 2009, it faces physical limits:$^{5}$
-    * 2 GW of solar requires a land area roughly the size of Manhattan (approx. 60 km²).$^{5}$
-    * Solar requires massive battery storage for 24/7 reliability, adding complexity for off-grid "island" data centers that cannot draw on spare grid capacity at night.$^{5}$
+    While solar prices have dropped ~88% since 2009, it faces physical limits. 2 GW of solar requires a land area roughly the size of Manhattan (approx. 60 km²). Solar requires massive battery storage for 24/7 reliability, adding complexity for off-grid "island" data centers that cannot draw on spare grid capacity at night.$^{5}$
       
     *Sources:*
     1. [Latitude Media (Jan 2026)](https://www.latitudemedia.com/news/what-the-michigan-stargate-site-says-about-todays-ai-market)
@@ -209,7 +207,7 @@ turbine_eff_percent = st.sidebar.slider(
 
 with st.sidebar.expander("More on Turbine Tech"):
     st.markdown("""
-    **Trade-offs: Efficiency vs. Speed**: The efficiency of gas turbines, which determines their carbon emissions, varies from 30-60%. The most efficient, are, generally, the slowest to build. In frontier data centers, AI chips cost roughly 10x more than the power infrastructure, driving companies to prioritize deployment speed over energy efficiency.
+    **Trade-offs: Efficiency vs. Speed**: The efficiency of gas turbines, which determines their carbon emissions, varies from 35-60%. The most efficient, are, generally, the slowest to build. In frontier data centers, AI chips cost roughly 10x more than the power infrastructure, driving companies to prioritize deployment speed over energy efficiency.
     
     * **Aeroderivative (35-40%):** Modified jet engines (e.g., GE LM2500). They are less efficient but can be deployed in weeks via truck. xAI used these to bypass multi-year grid delays.
     * **Reciprocating Engines (40-50%):** Modular internal combustion engines (e.g., Wärtsilä). They maintain high efficiency even at partial loads, making them ideal for flexible "demand response" strategies.