Toward computational and experimental characterisation for risk assessment of metal oxide nanoparticles

Escorihuela, Laura; Martorell, Benjamí; Ralló, Robert; Fernández, Alberto

doi:10.1039/c8en00389k

Cited by 13 publications

(13 citation statements)

References 62 publications

(74 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Over the past few decades, researchers and energy experts are closely collaborating in a wide range of research activities to mitigate the harmful impacts of power generation to ensure sustainable power generation and environmental protection. [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Optimization of a 660 MWe Supercritical Power Plant Performance—A Case of Industry 4.0 in the Data-Driven Operational Management. Part 2. Power Generation

Ashraf

Uddin

Kamal³

et al. 2020

Energies

View full text Add to dashboard Cite

Modern data analytics techniques and computationally inexpensive software tools are fueling the commercial applications of data-driven decision making and process optimization strategies for complex industrial operations. In this paper, modern and reliable process modeling techniques, i.e., multiple linear regression (MLR), artificial neural network (ANN), and least square support vector machine (LSSVM), are employed and comprehensively compared as reliable and robust process models for the generator power of a 660 MWe supercritical coal combustion power plant. Based on the external validation test conducted by the unseen operation data, LSSVM has outperformed the MLR and ANN models to predict the power plant’s generator power. Later, the LSSVM model is used for the failure mode recovery and a very successful operation control excellence tool. Moreover, by adjusting the thermo-electric operating parameters, the generator power on an average is increased by 1.74%, 1.80%, and 1.0 at 50% generation capacity, 75% generation capacity, and 100% generation capacity of the power plant, respectively. The process modeling based on process data and data-driven process optimization strategy building for improved process control is an actual realization of industry 4.0 in the industrial applications.

show abstract

Section: Introductionmentioning

confidence: 99%

Optimization of a 660 MWe Supercritical Power Plant Performance—A Case of Industry 4.0 in the Data-Driven Operational Management. Part 2. Power Generation

Ashraf

Uddin

Kamal³

et al. 2020

Energies

View full text Add to dashboard Cite

show abstract

“…What it is more important with data and nanoinformatics is that the machine learning tools and QSAR/QSPR models for the correlation of nanomaterial properties with toxicity, exposure, and hazard assessment can support the policy adaptivity to new nanotechnologies in the future upon standardization [ 57 ]. Thus, design procedures can be used to maximize nanomaterial utility while there is compliance with European Commission and global strategies to replace, reduce, and refine (3R principles), which aims to reduce adverse biological effects and support ethical science by reducing the tests performed using animals [ 47 , 49 ].…”

Section: Selection Of Nanomaterials Tailored For Improvements In Qual...mentioning

confidence: 99%

“…The most prominent machine learning models contain the quantitative structure–activity relationships (QSARs) and nanostructure–activity relationships (QNAR) concerning the toxicological effect prediction of engineered nanomaterials using compartment-based mathematical models for toxicokinetic, toxicodynamic, in vitro and in vivo dosimetry, and environmental fate. EU reports, projects, and nanosafety clusters have assessed their applicability for regulatory purposes and to provide proper REACH guidance [ 21 , 45 , 46 , 47 ]. All these methods have been established for years, and the developed regulations/standards or SOPs satisfy the reproducibility and interoperability needs for the consolidated reporting of properties and behavior, while also satisfy the demand for safe nanomaterials by design.…”

Section: Introductionmentioning

confidence: 99%

“…• Typical machine learning approaches and applications • Limitations, routes for overcoming challenges and future orientations [21] Advancing Biosensors with Machine Learning • Chemometrics for different biosensors types and data • Machine learning models advantages and limitations [26] Knowledge gaps in immune response and immunotherapy involving nanomaterials: Databases and artificial intelligence for material design In silico studies with QSAR models and machine learning [47] Machine Learning for Advanced Additive Manufacturing…”

mentioning

confidence: 99%

See 1 more Smart Citation

Digital Innovation Enabled Nanomaterial Manufacturing; Machine Learning Strategies and Green Perspectives

Konstantopoulos

Koumoulos²,

Charitidis

2022

Nanomaterials

View full text Add to dashboard Cite

Machine learning has been an emerging scientific field serving the modern multidisciplinary needs in the Materials Science and Manufacturing sector. The taxonomy and mapping of nanomaterial properties based on data analytics is going to ensure safe and green manufacturing with consciousness raised on effective resource management. The utilization of predictive modelling tools empowered with artificial intelligence (AI) has proposed novel paths in materials discovery and optimization, while it can further stimulate the cutting-edge and data-driven design of a tailored behavioral profile of nanomaterials to serve the special needs of application environments. The previous knowledge of the physics and mathematical representation of material behaviors, as well as the utilization of already generated testing data, received specific attention by scientists. However, the exploration of available information is not always manageable, and machine intelligence can efficiently (computational resources, time) meet this challenge via high-throughput multidimensional search exploration capabilities. Moreover, the modelling of bio-chemical interactions with the environment and living organisms has been demonstrated to connect chemical structure with acute or tolerable effects upon exposure. Thus, in this review, a summary of recent computational developments is provided with the aim to cover excelling research and present challenges towards unbiased, decentralized, and data-driven decision-making, in relation to increased impact in the field of advanced nanomaterials manufacturing and nanoinformatics, and to indicate the steps required to realize rapid, safe, and circular-by-design nanomaterials.

show abstract

“…Although more experiments need to be conducted to verify the theory, it points out a possible way for designing biomaterials. [ 197 ] Currently, two strategies are widely used to regulate the potential of the interface.…”

Section: Advanced Designs Of Implants With Charge‐transfer Monitoring or Regulating Abilitiesmentioning

confidence: 99%

Biomedical Implants with Charge‐Transfer Monitoring and Regulating Abilities

et al. 2021

View full text Add to dashboard Cite

Transmembrane charge (ion/electron) transfer is essential for maintaining cellular homeostasis and is involved in many biological processes, from protein synthesis to embryonic development in organisms. Designing implant devices that can detect or regulate cellular transmembrane charge transfer is expected to sense and modulate the behaviors of host cells and tissues. Thus, charge transfer can be regarded as a bridge connecting living systems and human‐made implantable devices. This review describes the mode and mechanism of charge transfer between organisms and nonliving materials, and summarizes the strategies to endow implants with charge‐transfer regulating or monitoring abilities. Furthermore, three major charge‐transfer controlling systems, including wired, self‐activated, and stimuli‐responsive biomedical implants, as well as the design principles and pivotal materials are systematically elaborated. The clinical challenges and the prospects for future development of these implant devices are also discussed.

show abstract

Toward computational and experimental characterisation for risk assessment of metal oxide nanoparticles

Abstract: In silicomethods provide an alternative and reliable route for the toxicity evaluation of metal oxide nanoparticles.

Cited by 13 publications

References 62 publications

Optimization of a 660 MWe Supercritical Power Plant Performance—A Case of Industry 4.0 in the Data-Driven Operational Management. Part 2. Power Generation

Optimization of a 660 MWe Supercritical Power Plant Performance—A Case of Industry 4.0 in the Data-Driven Operational Management. Part 2. Power Generation

Digital Innovation Enabled Nanomaterial Manufacturing; Machine Learning Strategies and Green Perspectives

Biomedical Implants with Charge‐Transfer Monitoring and Regulating Abilities

Contact Info

Product

Resources

About