"stochastic resonance in lunar mass drivers, computational helioseismology, dielectric HfO2/ZrO2 orbital compute, Fourier-Laplace symphonic transforms, topological insulators in regolith-derived silicon"

edony snaten agaza kda w hfo2

Si/HfO2間のIL層の薄膜化ってHfO2系強誘電メモリの性能向上にも応用できそうすね

GAA トランジスタが導入される2 nm世代以降で、ゲート絶縁膜の容量換算膜厚を0.9 nmに縮小することが必要 ↓ 現在主流のオゾン水を用いたウェットプロセスによる製法では、SiO2界面層の膜厚だけで0.8 nmあり、これだけでゲート絶縁膜の容量換算膜厚の大部分を占める。これに二酸化ハフニウム（HfO2）などの高誘電率膜層を成膜した場合、ゲート絶縁膜薄層化が1.4 nm程度で停滞 ↓ 新規のドライプロセスを適用し、ほぼ一原子層に近い厚さ0.2 nmの安定なSiO2膜を形成する方法を開発。 酸素分圧と温度の設定により、SiO2膜の成長が進行する領域と、酸素との反応によるエッチングが進行する領域の境界条件を利用することに特徴があります。SiO2膜の成長とエッチングを同時に進めることで、実質的にSiO2膜の成長の速度を遅くすることができ、きわめて薄い膜を高い制御性で形成できる ↓ SiO2界面相層を0.2nmと大幅に薄膜化できたことで、トランジスタのゲートスタックとして電気的に実測された容量換算膜厚0.9 nmを達成

⚛️ Mat3rials 3xplorer. 𝗜𝗻𝘁𝗲𝗿𝗳𝗮𝗰𝗲 𝗯𝗲𝘁𝘄𝗲𝗲𝗻 𝗚𝗮𝗹𝗹𝗶𝘂𝗺 𝗔𝗿𝘀𝗲𝗻𝗶𝗱𝗲 𝗮𝗻𝗱 𝗛𝗮𝗳𝗻𝗶𝘂𝗺 𝗢𝘅𝗶𝗱𝗲 (𝗚𝗮𝗔𝘀(𝟬𝟬𝟭)/𝗛𝗳𝗢𝟮(𝟬𝟬𝟭)), 𝟮𝘅𝟮𝘅𝟭 𝘀𝘁𝗿𝗮𝗶𝗻-𝗺𝗮𝘁𝗰𝗵𝗶𝗻𝗴 𝘀𝘂𝗽𝗲𝗿𝗰𝗲𝗹𝗹. This GaAs/HfO2 interface model is useful for studying semiconductor–oxide boundaries, including interface stability and electronic behavior. It can help explore effects relevant to microelectronic and optoelectronic devices. Available online in our materials bank: platform.mat3ra.com/bank/mat…" #mat3rials3xplorer #materialsscience #mat3ra

HfO2, diélectrique high-kappa, utilisé dans les stack d’oxide de grille pour les transistors avancés et d’autant plus pour les condensateurs des DRAM.

$DAL News, potentially $EUR no.2 The Greenland Government has formally approved licences M-516 & M-517 at Dalaroo’s Blue Lagoon Project The approvals significantly strengthen DAL’s exposure to: Rare Earth Elements (REE)✅️ Zirconium (Zr)✅️ Niobium (Nb)✅️ Hafnium (Hf)✅️ Upcoming exploration includes👇 🟢 Auger drilling 🟢 Geological mapping 🟢 Mineralogical studies 🟢 Van Veen offshore grab sampling 🟢 Bathymetric surveys dalaroometals.com.au/announc…… Special Adivisor, Greg Barnes (ex-Tanbreez/Kvanefjeld) on board✅️ Recent surface samples include👇 4.42% ZrO2 & 98ppm HfO2 4.09% ZrO2 & 99ppm HfO2 3.82% ZrO2 & 82ppm HfO2 3.58% ZrO2 & 61ppm HfO2 3.13% ZrO2 & 62ppm HfO2 2.85% ZrO2 & 73ppm HfO2 DYOR | $23m MC | ZrO2 | HfO₂ | $2.5m Cash #DAL #OTCQB #DALMF #CriticalMinerals #RareEarths #Greenland #Mining #Exploration #REE

I think the future of chemistry needs a theorem: composition is not identity anymore. Formula is only the seed. The realized material is composition plus crystallography plus geometry plus boundary chemistry plus gradient history plus measured response. Two samples can both be VO2, Bi2Te3, WO3, or HfO2−x and still be different chemical objects if oxygen history, strain, defect topology, surface state, thermal path, and transport tensor disagree. So the new chemical object is not just C. It is [C, geometry, boundary, gradient history, observables]. If changing a controlled gradient while holding stoichiometry fixed changes a measured property, then formula-only chemistry is incomplete. That is Geoalchemy in one hard sentence: matter is not merely what atoms are present, but what history the atoms were forced to remember.

$crml People don’t read! in message below i made an error 17640/171.25=103$ it don’t know how using automatic calculation i found 161,401 $/t ore .! ore in HfO2 is 0.35kg / t , 0.3kg / t metal , recovery is 85%*88%*97%=0,726 price Hf is 1572$/kg =339,552$/t ore.

$crml in PEA revenue from metals is 17640 M$ for 171.25Mt so 161,401 $/ton ore . ore content in HfO2 is 0.35kg / t , 0.3kg / t metal , recovery is 85%*88%*97%=0,726 price Hf is 1572$/kg so 0.3*72.%*1572=339,552$/ton ore so 2 times rest of metals .

Neural networks in reciprocal space teach interatomic potentials about long-range physics Machine learning interatomic potentials (MLIPs) have largely replaced classical force fields for atomistic simulations, offering near-DFT accuracy at a fraction of the cost. They work by decomposing total energy into atomic contributions that depend only on each atom's local neighborhood within a cutoff radius. This "nearsightedness" makes MLIPs scale linearly with system size, but it bakes in a blind spot: anything decaying slower than 1/r^d doesn't fit in a finite cutoff. Coulomb forces in ionic crystals, van der Waals dispersion, dipole couplings in defective oxides, all get truncated. Most existing fixes predict atomic charges with an auxiliary network and plug them into an analytic Ewald sum, which hardcodes a 1/r form and struggles with mixed long-range physics. Ruijie Guo and coauthors propose a different approach. They build a reciprocal-space neural network (RSNN) that learns the interaction kernel itself. Take the structure factor S(k) (the Fourier transform of atomic positions weighted by learned per-atom features), feed |k| and |S(k)|/V into a fully connected network, and let the model discover what shape the long-range potential should take. The architecture is provably invariant under E(3) symmetry and unit-cell choice, which is non-trivial in reciprocal space because |S(k)| itself depends on cell volume. They test it on three systems. On a synthetic NaCl benchmark with both Coulomb and Lennard-Jones interactions, RSNN cuts energy MAE by roughly 2.4x over Allegro alone and beats Latent Ewald Summation (which assumes pure 1/r). On GaxNy with defects, error drops further when paired with a three-step training protocol that fits short-range first, then long-range, then jointly fine-tunes. On HfO2 with oxygen vacancies, force MAEs barely move but predicted phonon spectra improve dramatically, since long-range corrections matter disproportionately for collective modes. Adding a reciprocal-space descriptor also improves band-gap predictions on the HOIP perovskite benchmark. This removes a trade-off between MLIP speed and physical fidelity in simulations of electrolytes, ferroelectrics, or defective semiconductors. Battery materials, catalysts on charged surfaces, and hybrid perovskites for photovoltaics sit in regimes where standard local potentials silently lose accuracy. A learnable kernel that adapts to whatever long-range physics the data contains is a more accurate default. Paper: Guo et al., Physical Review B (2026) — journal license | doi.org/10.1103/7yn2-22h6

$DAL News💥 Blue Lagoon licence expansion enters final approval stages stages🟢 Expanded licences increase Dalaroo’s footprint across prospective onshore, coastal and offshore target areas🟢 Offshore environments identified as priority exploration targets for heavy mineral accumulation🟢 Planned offshore exploration to include Van Veen grab sampling and bathymetric surveys🟢 Special Adivisor, Greg Barnes (ex-Tanbreez/Kvanefjeld) on board✅️ Recent surface samples include👇 4.42% ZrO2 & 98ppm HfO2 4.09% ZrO2 & 99ppm HfO2 3.82% ZrO2 & 82ppm HfO2 3.58% ZrO2 & 61ppm HfO2 3.13% ZrO2 & 62ppm HfO2 2.85% ZrO2 & 73ppm HfO2 DYOR | $26m MC | ZrO2 | HfO₂ #Hafnium #Zirconium $ETM $EUR $CRML #Greenland #UltraLowMc

$DAL #GOLD $EDV #rareearths #Greenland My 2nd largest Holding in PF currently after a mind blowing run on my largest Holding $ALR 45 bhaggs (still holding) Project 1 (#GOLD) Bondoukou Gold Project 💠only 35kms northwest from $EDV's (yes same $20B Gold producer in $ALR) Endeavour Mining’s 4.5Moz Tanda gold Deposit within the same Birimian gold-prospective terrane 💠fully granted Mining lease 💠Extensive artisanal workings occur over approximately ~9 km of strike length 💠Historical rock-chip results include grades up to 17.95 g/t Au 💠2241 soil sampling completed awaiting results Project 2 Blue Lagoon Rare Earth Project (#Greenland, #Rareearths) 💠Greenland has the third highest reserves of REE in the western world 💠similar geochemical anomaly footprint to the Kvanefjeld (ASX: ETM Energy Transition Metals) and Kringlerne/Tanbreez (NASDAQ: CRML Critical Metals Corp) 💠sample resulted in Exceptional #Zr02 & #HfO2 4.42% ZrO2 & 98ppm HfO2 4.09% ZrO2& 99ppm HfO2 3.82% ZrO2 & 82ppm HfO2 3.58% ZrO2 & 61ppm HfO2 3.13% ZrO2 & 62ppm HfO2 2.85% ZrO2 & 73ppm HfO2 & numerous More #Gold Project in Cote D’Ivoire Waiting Patiently...

$CRML to buyout $EUR for $835m Deal giving #CRML full ownership of #Tanbreez Rare Earth Project in #Greenland $DAL $DALMF: #OTCQB #GREENLAND #RAREEARTHS #crml $DAL the next cheapie in #Greenland with their Blue Moon #rareEarths project ⚡️Maiden Sampling Identified District-Scale Critical Minerals System at Blue Lagoon - Greenland with Exceptional high-grade Zirconium Oxide (ZrO2) and Hafnium Oxide (HfO2) - 4.42% ZrO2 & 98ppm HfO2 - 4.09% ZrO2& 99ppm HfO2 - 3.82% ZrO2 & 82ppm HfO2 - 3.58% ZrO2 & 61ppm HfO2 - 3.13% ZrO2 & 62ppm HfO2 - 2.85% ZrO2 & 73ppm HfO2 ⚡️Fast-Tracks 2026 Exploration at Blue Lagoon Following Standout 2025 Results ⚡️Founder for #tanbreez project is advisor/vendor on $DAL ⚡️only ~26m MC ⚡️district-scale critical minerals potential this is my next $ALR ( 💰* 50 so far & counting) like performance packing stock 🤞🤞 #BULLS #asx $DAL $ALR #patience reuters.com/business/critica…

OMG $DAL now have TWO bidders in the horizon 1. Cote D’ivoire gold $20B MC $EDV.tse 2. Greenland REE : Critical Metals Corp (NASDAQ: CRML snaps up $EUR for $1 .2B UltraLowMc $DAL is advancing it's rare earth project in Greenland 💪 Blue Lagoon RE Highlights 👇 Consistent rare earth mineralisation across a 2.7km strike ✅️ All samples showing anomalous values and grades up to 0.81% TREO ✅️ Fast-tracking a 2026 exploration✅️ Total landholding to 460 km² ✅️ Special Adivisor, Greg Barnes (ex-Tanbreez/Kvanefjeld) on board✅️ Recent surface samples include👇 4.42% ZrO2 & 98ppm HfO2 4.09% ZrO2 & 99ppm HfO2 3.82% ZrO2 & 82ppm HfO2 3.58% ZrO2 & 61ppm HfO2 3.13% ZrO2 & 62ppm HfO2 2.85% ZrO2 & 73ppm HfO2 DYOR | $25m MC | ZrO2 | HfO₂ #Hafnium #Zirconium $ETM $EUR $CRML #Greenland

University of Cambridge researchers developed a hafnium oxide memristor that cuts AI energy use by 70%. By using strontium-doped HfO2 to create p-n junctions, the chip enables smooth interface switching instead of random filaments. getaibook.com/news/cambridge…

#Greenland is back $CRML surge 40% after expanding Rare Earth Mining Position In Greenland $EUR | $EUROC set to rocket tomorrow 😃💰 UltraLowMc $DAL is also advancing it's rare earth project in Greenland 💪 Blue Lagoon RE Highlights 👇 Consistent rare earth mineralisation across a 2.7km strike ✅️ All samples showing anomalous values and grades up to 0.81% TREO ✅️ Fast-tracking a 2026 exploration✅️ Total landholding to 260 km² ✅️ Special Adivisor, Greg Barnes (ex-Tanbreez/Kvanefjeld) on board✅️ Recent surface samples include👇 4.42% ZrO2 & 98ppm HfO2 4.09% ZrO2 & 99ppm HfO2 3.82% ZrO2 & 82ppm HfO2 3.58% ZrO2 & 61ppm HfO2 3.13% ZrO2 & 62ppm HfO2 2.85% ZrO2 & 73ppm HfO2 DYOR | $23m MC | ZrO2 | HfO₂ : #Hafnium #Zirconium $ETM $EUR $CRML #Greenland zerohedge.com/markets/critic…

PdNeuRAM: forming-free, multi-bit Pd/HfO2 ReRAM for energy-efficient neuromorphic computing nature.com/articles/s44172-0…

$crml metals forgotten in PEA Ta2O5 150ppm 497$/kg GaO2 100ppm 288/kg HfO2 350ppp 5.51$/kg ( price of ZrO2 separation from ZrO2 costly ) 105,279$ / ton in situ value with 96% *83*88%=70% global recovery with 171.25Mt extraction revenue 12 641M$ NPV 5829M$

📄 M-CODE: Materials Categorization via Ontology, Dimensionality and Evolution. Representative target structure classes grouped by domain and annotated with the corresponding M-CODE tags. Pristine Structures (top row): MoS2 monolayer, SrTiO3 slab, Si nanowire, and graphene nanoribbon. Compound Pristine Structures (second row): Si/SiO2/HfO2/TiN heterostack, Cu/SiO2 interface (ZSL-matched), twisted MoS2/WS2 interface, and hBN/hBN nanoribbon interface. Defective Structures (third row): Pt adatom on WS2, island on TiN(001), Cu(321)/(3̄2̄1̄) grain boundary, and nitrogen substitution in graphene. Processed Structures (bottom row): perturbed graphene, passivated Si surface, passivated Si nanowire edge, and Si box cutout. Read the full article: arxiv.org/pdf/2602.14384 #materials #RnD #mat3ra #exabyteio #materialsscience #materialsdesign #materialsmodeling #science #technology