De la même façon que les présidents #d’Afghanistan et de #Syrie ont quitté leur pays avec des sommes d’argent colossales à la suite du retrait de leurs alliés #Américains et #Russes, les dirigeants de #l’AES feront exactement de même en cas d’effondrement de leur pouvoir.

🚨 $LAES 🚨

Why is SEALSQ Corp trending today? 🤔

#LAES #stocks #stockmarket

MIT ChemEng first author Shaylin Cetegen with Barton/MIT & Gundersen/NTNU:
Liquid air energy storage #LAES levelized cost of storage #LCOS for long-duration energy storage: ⅓ cost of lithium-ion batteries & ½ pumped hydro.
#LDES of 1day/1wk/1month, but not seasonal storage.
IMO looks like hydrogen still wins for seasonal storage.
#BESS #LIB #PumpedHydro #H2 #hydrogen

🚨 $LAES 🚨

Why is Sealsq Corp trending today? 🤔

#LAES #stocks #stockmarket

🚨 $LAES 🚨

Why is Sealsq Corp trending today? 🤔

#LAES #stocks #stockmarket

An even newer MUSE paper by Yohana Herrero Alonso on LAE clustering in MUSE deep and not-so-deep fields appeared today. (I had nothing to do with this one...) https://arxiv.org/abs/2301.04133 #musevlt #LAEs #clustering
Clustering dependence on Lyman-$α$ luminosity from MUSE surveys at $3<z<6$

[Abbreviated] We investigate the dependence of Lyman-$α$ emitter (LAE) clustering on Lyman-$α$ luminosity. We use 1030 LAEs from the MUSE-Wide survey, 679 LAEs from MUSE-Deep, and 367 LAEs from the to-date deepest ever spectroscopic survey, the MUSE Extremely Deep Field. All objects have spectroscopic redshifts of $3<z<6$ and cover a large dynamic range of Ly$α$ luminosities: $40.15<\log (L_{\rm{Ly}α}/[\rm{erg \:s}^{-1}])<43.35$. We apply the Adelberger et al. K-estimator as the clustering statistic and fit the measurements with state-of-the-art halo occupation distribution (HOD) models. From the three main data sets, we find that the large-scale bias factor, the minimum mass to host one central LAE, $M_{\rm{min}}$, and (on average) one satellite LAE, $M_1$, increase weakly with an increasing line luminosity. The satellite fractions are $\lesssim10$% ($\lesssim20$%) at $1σ$ ($3σ$) confidence level, supporting a scenario in which DMHs typically host one single LAE. We next bisected the three main samples into disjoint subsets to thoroughly explore the dependence of the clustering properties on $L_{\rm{Ly}α}$. We report a strong ($8σ$) clustering dependence on $L_{\rm{Ly}α}$, where the highest luminosity LAE subsample ($\log(L_{\rm{Ly}α}/[\rm{erg \:s}^{-1}])\approx42.53$) clusters more strongly ($b_{\rm{high}}=3.13^{+0.08}_{-0.15}$) and resides in more massive DMHs ($\log(M_{\rm{h}}/[h^{-1}\rm{M}_{\odot}])=11.43^{+0.04}_{-0.10}$) than the lowest luminosity one ($\log(L_{\rm{Ly}α}/[\rm{erg \:s}^{-1}])\approx40.97$), which presents a bias of $b_{\rm{low}}=1.79^{+0.08}_{-0.06}$ and occupies $\log(M_{\rm{h}}/[h^{-1}\rm{M}_{\odot}])=10.00^{+0.12}_{-0.09}$ halos. We discuss the implications of these results for evolving Ly$α$ luminosity functions, halo mass dependent Ly$α$ escape fractions, and incomplete reionization signatures.

arXiv.org

WINNER! 🚀🚀🚀

phelas wins Best CleanTech startup at Gobal EnergyTech Awards by Publicis Sapient. 🏆

We are grateful for the recognition that all climate tech innovators receive. The urgency of this topic was discussed long enough – now is the time to act!💥

#GlobalEnergyTechAwards2022 #publicissapient #BestCleanTech #winner #energystorage #liquidair #innovation #startup #phelas #cleantechnology #renewable #liquidairenergystorage #longdurationenergystorage #laes #renewableenergy #sustainable

Our cofounders @jmovs and Masoud are going to Bilbao to South Summit this week - see you there!
#startups #energystorage #laes #renewableEnergy