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Record W6893213917 · doi:10.5281/zenodo.15331067

From Majorana Fermions to Quantum Devices: The Role of Nanomaterials in the Second Quantum Era

2025· article· en· W6893213917 on OpenAlexaff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicTopological Materials and Phenomena
Canadian institutionsCanadian Association of Physicists
Fundersnot available
KeywordsMAJORANAFermionQuantum computerQuantumQuantum technologyQuantum nanoscienceDirac (video compression format)Quantum dotMacroscopic quantum phenomena

Abstract

fetched live from OpenAlex

From Majorana Fermions to Quantum Devices: The Role of Nanomaterials in the Second Quantum Era Mehmet Keçeci ORCID: https://orcid.org/0000-0001-9937-9839 https://doi.org/10.5281/zenodo.15331067 Received: 03.05.2025 Abstract: The pace and scope of scientific and technological advancements in our era are unprecedented, with every method, technique, process, and practice evolving almost instantaneously. The most striking example of this transformation is the emergence of quantum computers. The Second Quantum Revolution, which began in the early 21st century, focuses on harnessing quantum phenomena such as superposition, entanglement, and tunneling to develop groundbreaking technologies in computation, communication, and precision measurement (Arute et al., 2019). In this context, concepts from particle physics, such as Weyl fermions and Majorana fermions, have found innovative applications in condensed matter physics, electronics, materials science, and even nanomedicine. For instance, Majorana fermions—originally predicted as elementary particles—are now explored as potential building blocks for topological quantum computers due to their non-Abelian statistics, which could enable fault-tolerant quantum computation (Lutchyn et al., 2018). Similarly, two-dimensional (2D) monolayer materials (e.g., graphene, MoS₂, WS₂) have revolutionized electronics by enabling ultra-low energy consumption and high electron mobility. Recent advances in 2D materials include their use in flexible electronics, photovoltaics, and quantum optoelectronics (Xu et al., 2020). Nanotechnology has become an indispensable tool across all scientific disciplines. In nanomedicine, nanoparticle-based drug delivery systems have enabled targeted cancer therapies, minimizing side effects while maximizing efficacy (Wang et al., 2021). Additionally, quantum dots are employed in high-resolution biomedical imaging and biosensing, offering unparalleled sensitivity. These developments underscore nanotechnology’s role not only in engineering but also in advancing life sciences. The Second Quantum Revolution will extend its impact beyond information technology, addressing global challenges in energy, healthcare, and environmental sustainability. For example, quantum simulations can model complex molecular interactions, accelerating drug discovery and materials design (Cao et al., 2019). Such research will likely catalyse a technological explosion unprecedented in human history. Keywords: DAQC, Majorana Fermions, Monolayers, Nonlocality, Nanorod, Nanostructure, Nanotechnology, Quantum Dots, Quantum Devices, Quantum Simulation, Second Quantum Revolution, Second Quantum Era, Spintronics, Topological Qubits, Weyl Semimetals.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0020.006
Scholarly communication0.0030.008
Open science0.0010.003
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0070.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.019
GPT teacher head0.249
Teacher spread0.230 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2025
Admission routes1
Has abstractyes

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