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Record W2621286616 · doi:10.1080/15422119.2017.1335214

Perspective and Roadmap of Energy-Efficient Desalination Integrated with Nanomaterials

2017· article· en· W2621286616 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSeparation and Purification Reviews · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsUniversity of Ottawa
FundersMinistry of Higher Education, Malaysia
KeywordsDesalinationCommercializationGeothermal desalinationWater desalinationEnvironmental scienceBusinessEngineering

Abstract

fetched live from OpenAlex

Desalination is known to be one of the most sustainable solutions for water treatment to provide fresh water for many water-stressed communities and industrial sectors. As the integration of nanotechnology with desalination processes is most likely to dominate the future research attention and desalination market, this manuscript presents state-of-the-art review on the enabling of cutting edge desalination technology integrated with nanomaterials. The technological needs and future perspective, which include the challenges and opportunities of nano-enabled desalination processes are critically reviewed in this contribution. Recent developments and findings on the state-of-the-art nano-enabled desalination processes are discussed. Key issues such as scale-up, economic competitiveness, potential environmental impacts and energy consumption are also reviewed. This minireview aims to provide directions and guidelines to the desalination research community regarding the future outlook and roadmap of the application of nanotechnology in desalination processes at the bench scale and commercialization level.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.331
Threshold uncertainty score0.312

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.026
GPT teacher head0.304
Teacher spread0.278 · 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