ArticleNature communications2024
True random number generation using the spin crossover in LaCoO
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Morphology-Defined Entropy Streams for Random-Bit Generation and Probabilistic Sampling in Memristor Arrays.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Dual Randomness From Spatial and Temporal Variations in Stochastic Zinc Gallate Nanoclusters for PUF and TRNG Co-Driven Image Generation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Large-scale integrated optoelectronic chaos for machine learning acceleration.Nature communications · 2026Article
- Nanoscale Tracking of the High-Temperature Spin-State Transition in LaCoONano letters · 2025Article
- Benchmarking Stochasticity behind Reproducibility: Denoising Strategies in TaACS applied materials & interfaces · 2025Article
- Stochastic Photoresponse-Driven Perovskite TRNGs for Secure Encryption Systems.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Memristive True Random Number Generator for Security Applications.Sensors (Basel, Switzerland) · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
While digital computers rely on software-generated pseudo-random number generators, hardware-based true random number generators (TRNGs), which employ the natural physics of the underlying hardware, provide true stochasticity, and power and area efficiency. Research into TRNGs has extensively relied on the unpredictability in phase transitions, but such phase transitions are difficult to control given their often abrupt and narrow parameter ranges (e.g., occurring in a small temperature window). Here we demonstrate a TRNG based on self-oscillations in LaCoO
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.