The depletion of petroleum-based resources and the adverse environmental problems, such as pollution, have stimulated considerable interest in the development of environmentally sustainable materials, which are composed of natural fiber–reinforced polymer composites. These materials could be tailored for a broad range of sustainable industrial applications with new surface functionalities. However, there are several challenges and drawbacks, such as composites processing production and fiber/matrix adhesion, that need to be addressed and overcome. This review could provide an overview of the technological challenges, processing techniques, characterization, properties, and potential applications of NFRPC for sustainable industrial applications. Interestingly, a roadmap for NFRPC to move into Industry 4.0 was highlighted in this review.

Aluminium-metal matrix composites (Al-MMCs) are quite attractive due to their low density, ability to be reinforced by precipitation, good corrosion resistance, high thermal and electrical conductivity, and high damping capacity. In this study, the aluminium-magnesium (Al-Mg) master alloy was reinforced with 0.5 to 1.0 wt% strontium (Sr) by casting technique. Then, the Al-Mg-Sr composite alloy was characterised by its mechanical properties and microstructure characterization. An Instron tensile machine and a Vickers hardness tester were used to determine the tensile strength and hardness of the Al-Mg-Sr composite alloy. The Gamry potentiometer electrode was used to determine the corrosion rate of this composite alloy. From the results, increased Sr content increases the tensile strength and hardness of the Al-Mg alloy. Field emission scanning electron microscope (FESEM) results show that the synthesised composite alloys have a uniform distribution of reinforcement, which tends to be fine and associated with a clean interface with the metal matrix. X-ray diffraction (XRD) analysis confirms that only the elements Al, Mg, and Sr were detected during characterization. The morphology shows that the particles of the Sr phase have a dendritic structure. In the corrosion test with the Gamry potentiometer electrode, Al-Mg with a composition of 1.0 wt% Sr showed the best results in terms of corrosion rate compared to cast Al-Mg with 0.5 wt% and the Al-Mg alloy itself. All the summarised results show that Al-Mg alloy composites containing different weights of Sr phase were successfully fabricated and the particles were uniformly distributed in the matrix of the composites.

Aluminium-silicon (Al-Si) alloy is the metal matrix composite (MMCs), widely used in constructions and transports requiring high strength and ductility. This research focused more on the Al-Si as the base alloy which act as matrix component, while Scandium (Sc) act as the reinforcement and inoculants that helps to reduce the grain size of the base alloys, in order to investigate the effect of Sc onto mechanical properties and wear behaviour of Al-Si alloy. In this research, Al-Si was reinforced with 0.2 to 0.8 wt.% Sc. Al-Si and Sc were melted at 720 °C in high temperature furnace. Al-Si-Sc composite then were poured to stainless steel mould and solidified at room temperature. From results obtained, the increment of Sc increased the value of mechanical properties to Al-Si. The ductility of Al-Si was observed much lower in the absence of Sc. The tensile strength of unrefined Al-Si and Al-Si with 0.6 wt.% Sc were 325 and 377.3 MPa. The hardness value for the unrefined Al-Si also shows less compared with Al-Si with 0.8 wt.% Sc, which were 70 and 85 Hv. Results show that 0.6 wt.% Sc gave the lowest wear rate which was 0.7 x 10-4 at 100 N, while for 150 N, the wear rate was 1.25 x 10-4 g/min. Microstructure of Al-Si-Sc composite alloy has much finer compared to unlined Al-Si alloy. Increased Sc in Al-Si alloy influenced the grain refinement and mechanical strength of that composites.

Biochar has attracted growing interest as a carbon-rich additive or partial cement replacement in cementitious composites, but published data remain fragmented across feedstocks, processing conditions, mixture designs and material types. This study compiled a structured database from 56 sources and 368 extraction rows. The target variable was strength ratio, defined as the compressive strength of a biochar-modified mixture divided by that of its corresponding control. After excluding 22 control rows that equal unity by definition, the final modelling dataset contained 131 biochar-modified rows: 82 high-confidence Tier 1 rows for the main analysis and 49 Tier 2 rows for sensitivity analysis. Tier 1 random splitting produced R² = 0.335, MAE = 0.118 and RMSE = 0.164, whereas leave-one-source-out (LOSO) validation produced R² = −0.030, MAE = 0.170 and RMSE = 0.207. Statistical testing showed that strength-ratio normalisation substantially reduced, but did not eliminate, material-type effects. Nested group-aware tuning did not restore positive Tier 1 LOSO R² (best R² = −0.008). On 54 independently published experimental mixture means not used for model development, the tuned Tier 1 random forest gave R² = −0.153, MAE = 0.082 and RMSE = 0.108; the strong rank correlation (Spearman ρ = 0.797) but positive prediction bias indicated partial trend recognition without reliable calibration. Missing-data sensitivity, source-level Friedman and Wilcoxon-Holm tests, and grouped SHAP analyses reinforced the same interpretation. Biochar dosage remained the leading predictor, but the framework is best understood as exploratory, diagnostic and source-aware rather than as a universal or deployable prediction model.

Published bamboo characterization studies often report physical, chemical and energy properties separately, although early material selection requires multiple criteria to be considered together. This study develops a literature-informed multi-criteria analytical approach for screening bamboo feedstocks for bioenergy and preliminary biocomposite applications. Literature evidence is used to justify criterion relevance, scoring direction and limitations, while numerical weights are treated as transparent application-priority assumptions. A four-species Malaysian dataset [1] is used as a worked case. The Bioenergy Suitability Index (BESI) combines calorific value, Klason lignin, specific gravity, ash content and K + Na, whereas the preliminary Biocomposite Suitability Index (BCSI-R) combines estimated cellulose, crystallinity index, Klason lignin, combined extractives and a pH target score. Robustness was tested across 19 scenarios per index, including equal weighting, application-priority scenarios, leave-one-criterion-out tests and ±20% weight perturbations. Bambusa vulgaris ranked first in all 19 scenarios for both indices, and rankings remained unchanged under all ±20% weight tests. External literature supported the relevance of the selected criteria while also revealing important system dependence, particularly for ash chemistry, crystallinity and bonding variables. BESI and BCSI-R are therefore screening-level decision-support tools intended to expose assumptions and prioritize subsequent experimental validation, not to predict application performance.