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Record W2890219123 · doi:10.4236/msa.2018.910057

Insight on the Ultrastructure, Physicochemical, Thermal Characteristics and Applications of Palm Kernel Shells

2018· article· en· W2890219123 on OpenAlex
Richard Ntenga, Etienne Mfoumou, A. Béakou, Martin Tango, Jordan Kamga, Ali Ahmed

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

VenueMaterials Sciences and Applications · 2018
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsAcadia UniversityNova Scotia Community College
Fundersnot available
KeywordsDifferential scanning calorimetryMaterials scienceThermogravimetric analysisScanning electron microscopePalm kernelAmorphous solidChemical engineeringTransmission electron microscopyThermogravimetryNanoporeThermal analysisCarbon fibersThermalComposite materialNanotechnologyChemistryOrganic chemistryComposite number

Abstract

fetched live from OpenAlex

The ultrastructure and physicochemical and thermal properties of Palm Kernel Shells (PKS) in comparison with Coconut Kernel Shells (CKS) were investigated herein. Powder samples were prepared and characterized using Surface Electron Microscopy (SEM) and Transmission Electron Microscopy (TEM). Chemical and elemental constituents, as well as thermal performance were assessed by Van Soest Method, TEM/EDXA and SEM/EDS techniques. Differential Scanning Calorimetry (DSC) and Thermogravimetric Analysis (TGA) were also performed for thermal characterization. SEM/EDS and TEM/EDXA revealed that most of the PKS and CKS materials are composed of particles with irregular morphology; these are mainly amorphous phases of carbon/oxygen with small amounts of K, Ca and Mg. The DSC data permitted to derive the materials’ thermal transition phases and the relevant characteristic temperatures and physical properties. Thermal Transition phases of PKS observed herein are consistent with the chemical composition obtained and are similar to those of CKS. Nonetheless, TGA/DTG showed that the combustion characteristics of PKS are higher than those of CKS. Taken together, our results reveal that PKS have nanopores and can be efficiently used for 3D printing and membrane filtration applications. Moreover, the chemical constituents found in PKS samples are in agreement with those reported in the literature for material structural applications and thus, present potential use of PKS in these applications.

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 score0.036
Threshold uncertainty score0.502

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 studies0.0010.001
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 head0.011
GPT teacher head0.244
Teacher spread0.233 · 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