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On the contrary, the formulas for correlation based on the H-bonding Gibbs energies
evaluation of diverse substances were used to derive the ΣCa and ΣCd descriptors
(free energy receiver and donating factors) [73]. It was anticipated that such descrip-
tors would effectively characterize the Gibbs energies’ sublimation activity since their
physical perception was intimately tied to those of the Gibbs energies [72].
Even before conducting tests for laboratory toxicity evaluations, it is crucial to
establish sufficient physicochemical characterizations of nanomaterials [74]. Particu-
late size, size distribution, morphological characters, composition, extent of surface,
surface chemistry, and particulate reactivity in the mixture are significant variables
that require to be precisely characterized as necessities for carrying out nanoparticle
toxicity investigations, as the aforementioned researchers have appropriately recom-
mended. This assertion cannot be reiterated enough as predictions of particle size as
well as additional features have been determined in a number of freshly published
nanotoxic ity investigations employing solely (1) manufacturer’ s information (in the
absence of researcher confirmation), (2) electron microscopy, or (3) surface area eval-
uations, and such restricted descriptions offer limited insight into the particle type
under study [75].
The issue is the fact that the majority of the investigations, which were recently
published that use nanoparticles, were carried out in vitro or in cultured cell settings
(i.e., within the wet stage), at which the physicochemical properties of the small par-
ticles, involving particle size, are probably going to shift from the “just received” (i.e.,
dry phase) nanopowder included in a vial. This misinterpreta tion of the particulate
form pursuant to study stems from the fact that the characterization was probably
done in an atmosphere of dryness, and thus has nothing to do with how nanoparticles
engage with cells in a cultured cell environment. As a result, in the Murdock et al.
examination, these researchers concentrated on describing a broad spectrum of nano-
materials, such as metals, their oxides, and carbon-derived frameworks, employing
dynamic light scattering (DLS) in conjunction with transmission electron microscopy,
for particulates distributed beneath damp environments in cultured cell medium,
both with and without serum. In order to evaluate the toxicity of the reported agglom-
erate modifications across a variety of conditions of research, certain fundamental
cell survival and morphological investigations were associated with DLS particle size
characterization assays [74].
According to Murdock and colleagues, a lot of metals and their oxide nanoparticles
have a propensity to aggregate in solution. Additionally, other factors, especially the in-
clusion of serum in the medium used for culture, may have an impact on toxicity meas-
ures. This is probably because these factors may have an impact on the agglomeration
as well as surface chemistry of the nanoparticles. These elements indicate crucial varia-
bles that were formerly overlooked. Murdock and colleagues’ methodology-associated
investigation focused primarily on evaluations of particle size traits in cultured cell me-
dium, but they also discussed additional approaches for evaluating nanoparticle physi-
cochemical properties as well as the effects of prepping nanoparticles for toxicological
9 Nanotoxicity prediction in nanotechnology-driven drugs using QSPR modeling 193
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research. Methods for X-ray photoelectron spectroscopy were used to identify the chem-
ical makeup of surfaces of silver nanoparticles [74].
Furthermore, it has been discovered that sonicating nanoparticles lowers aggre-
gation and has a negligible impact on particulate surface charge. Just before the start
of culture of cells trials, sonication was used to speed up particulate dispersion and
solution mixing. Several research organizations or task groups have made it a priority
that toxicologists thoroughly characterize the physicochemical features of the nano-
particle types, which are currently considered for hazard examination, in line with
the thesis of the investigations by Murdock et al. [74]
Nevertheless, this suggestion frequently degenerates into a “laundry list” of physi-
cochemical traits and lacks proper prioritization. As a result, before executing hazard
studies with nanoparticle types, toxicologists should, at the very least, characterize the
following (prioritized) physicochemical properties for the purpose of sufficiently ex-
plaining the physical features of the nanoparticle type being assessed: dependence on
the contact path, particle size as well as distribution (wet phase) and surface area (dry
phase) in the appropriate medium being used; crystallinity and crystal structure, me-
dium in which they aggregate, composition and surface coatings, and surface reactivity;
a technique for preparing nanomaterials that includes post-synthesis alterations (such
as neutralizing certain kinds of ultrafine TiO
2
particle types); and specimen quality.
This is a strategy that is narrowly targeted and concurrent with a minimal, standard-
ized evaluation of the physicochemical features that ought to be looked into before the
creation of toxicological studies using nanoparticles [76].
The key physicochemical descriptors of nanomaterials might not be the best indi-
cators of their toxicity conduct, partly because a number of these characteristics are
“context dependent,” that is, they are influenced by the environment (pH, strength of
ions, macromolecules, etc.), the means of exposure, etc. The interdependence of multi-
ple nanomaterial features also makes it possible for changes to one attribute to unin-
tentionally affect a number of others. For instance, altering nanomaterial structure or
size could lead to imperfections in the surface or alter the surface chemistry. Due to
the aforementioned, it is challenging to establish libraries of nanomaterials with spe-
cific characteristics that are precisely differed to identify the key factors that influ-
ence toxicity, identify toxic thresholds for various descriptors, and promote the
creation of QNARs. The difficulty in forecasting nanomaterial toxicology is made
much more complex by the developing understanding that they mature and undergo
transformation across their existence [77, 78]. Although certain context-dependent
physicochemical attribute modifications, including size and surface charge alterations
brought about by biomolecule attachment, are clear and widely reported, others, like
redox or photochemical action, are less so and call for clinical evaluation and confir-
mation. It has been argued that these referred to as secondary or inherited qualities,
including nanomaterial capacity to produce oxidative stress or liberate hazardous me-
tallic ions, could represent more pertinent attributes with certain toxicity consequen-
ces [79].
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Sayes et al. provide a critical description of a key phase in proving the viability of
categorizing nanomaterials on the basis of a set of comparable physicochemical fea-
tures. Apparently homogeneous class of nanomaterials might be divided into sub-
groups based on interrelationships seen in their nanodescriptors employing principal
component analysis (PCA) utilized for identifiable nanomaterial characteristics (engi-
neered size, quantity, aggregated size in water, zeta potential, pH and age of the sus-
pension, and oxidant production) for five metal colloids (silver, copper, nickel, zinc,
and iron) [80]. PCA has also been used to reduce complication in slime [81], aerosol
particle dimension patterns of distribution, and information from metabolomics after
earthworm came in contact with TiO
2
nanomaterials [82, 83].
The possibility that the identical physicochemical descriptor may give rise to a num-
ber of Physicochemical Characteristics (PCs) has become an essential characteristic that
distinguishesthismethod:theprimaryphysicochemical descriptors operating toxic effects,
as identified by ITS-NANO [84], are capable of being specifically assessed compared to the
degree of involvement for each PC/axis (i.e., the loadings), as might the primary mecha-
nism of action, since this will be the PC that takes into account the greatest quantity of
variation in the information. The involvement of internal (structural) and external (sur-
face and contact with medium) qualities in nanotoxicity descriptors has just started to ap-
pear; the corresponding importance of both of these categories of variables, along with
intrinsic expansion, has still to be defined. Nevertheless, it is now well-established that the
influence on structural straining may serve as a significant descriptor of toxic effects [85,
86]. To investigate QSAR simulation employing the physicochemical characteristics of
nanomaterials, further clinical evidence alongside additional investigations are needed.
9.4.2 The relevance of structural characteristics
Previous to the release of the CYP2D6 crystal arrangement in 2006 [87], an array of
pharmacophore modeling investigations were carried out to investigate the structure
properties of CYP2D6 ligands and inhibiting agents, as well as the likely ligand-enzyme
association pattern. Manual positioning was used to create the primary CYP2D6 sub-
strate designs, which were designed on an assortment of substrates that included a
fundamental nitrogen atom in position 5 [88] or 7 [89] from the position of oxidation,
as well as a planar aromatic ring nearest to the place of oxidation, that was configured
to be coplanar [88, 89].
In debrisoquine, the gap between the basic nitrogen atom as well as the location
where it undergoes oxidation is around 5, while for dextromethorphan, the gap measures
approximately 7. There were however no compounds overlaid on top of one another in
the 5-model [88]. As a result, these early models did not adjust for the interaction of a
number of substrates. The CYP2D6 2F9Q framework, including other mammalian CYP
proteins with known frameworks, exhibited the usual helical CYP folded features seen in
the rest of the CYP domain [87].
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The dimensions and configurations of the various secondary structural compo-
nents, which include helices A–L and four sheets, are similar to those seen in CYP2C9
alongside various CYP2 molecules, and helices E, I, J, K, and L form the protein’s core.
In CYP2D6, the F–G loop has a short hydrophobic helical G’ that is likely connected
through the endoplasmic reticulum. The lengthy I helix travels through the entire of
the structure without apparent deformation. The heme complex is deeply embedded
into the protein’s structure and is strongly linked to CYP2D6 through H-bonding en-
gagements. When compared to the molecular makeup of CYP2C9 [90], there exists a
minimum of six significant spots in the CYP2D6 framework with noteworthy distinc-
tions, with a particularly recognized difference related to the F helix, F–G loop, and B′
helix [87]. These areas are all positioned upon the distant part of the protein and the
majority of these are engaged in describing the form and traits of the active spot [90].
The problem of how crucial it is to properly characterize the nanomaterial proc-
essing before beginning toxicity experimentsisraisedintheMurdocketal.study,
which might have had the strongest influence. In fact, the outcomes of nanotoxicity
investigations will be of little usefulness or relevance if the type of nanoparticle being
examined (as well as the clinical settings were used) is not fully and accurately de-
scribed. Furthermore, the findings of the described research will not be comparative
to those from different investigations done with related nanomaterial kinds. In a nut-
shell the nanomaterials risk information is going to be misrepresented. The following
would remain a regrettable mistake considering the absence of nanotoxicity health
impacts and environmental evidence at the moment [74].
9.4.3 Advanced computational techniques for feature selection
The previous section covered various methods for compiling and organizing test data.
Numerous computational and da ta extraction methods are needed to examine the
readily accessible data while streamlining the evaluation process. The researchers
have evaluated the QSAR techniques used in previous studies. The next section looks
at the methods used the most frequently to forecast nanotoxicity.
9.4.3.1 Regression
Estimation and forecast frequently employ regression analysis. It clarifies why there
is variance in the values of those variables that are dependent relative to the indepen-
dent variable. The most popular types of linear regression techniques include multi-
variate linear regression, partial least squares (PLS) regression, and fundamental
element regression. Whenever one observes cross-correlation between some descrip-
tors, the PLS is utilized. When many replies ought to have exhibited c oncurrently,
these solutions prove to be useful. The main benefit of linear regression approach is
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that it is transparent in its ability to offer significant pertinent data for physicochemi-
cal descriptors. The aforementioned models occasionally operate inefficiently due to
the proximity of data elements that are not related to yield. Therefore, PCA is used to
eliminate these various assessment lowering procedures. Various earlier studies had
used PCA to improve the outcome of their QSAR experiments.
9.4.3.2 Decision tree
An effective method for implementing regression and classification is a decision tree.
Based on both quantitative and qualitative information about pharmacological pro-
cesses, QSAR may be used for estimating toxicity. The important features of decision
trees that contribute substantially to toxicity assessment are automated parameter se-
lection, the elimination of irrelevant descriptors, and the ability to extract useful in-
formation from tiny, noisy, and big datasets [91, 92].
9.4.3.3 Support vector machine
Support vector machine can deal with a number of problems, including collinear de-
scriptors, nonlinear interactions, small and big databases, including overfitted mod-
els. It h as shown to be a successful methodforlinearmodelingthatcanhandle
problems involving classification as well as regression. Its high accuracy and specula-
tive nature makes it a fascinating technique [92, 93].
9.4.3.4 Artificial neural network
Artificial neural network (ANN) is a different technique used for huge databases and
relationships of nonlinear facts. The ANN technique has a number of drawbacks, in-
cluding difficulty selecting the optimal level of level of detail, difficulty with excess
fitting, high generalization, susceptibility to changes in variables and network config-
uration, and finally difficulty predicting the outcome [94, 95]. Only considering physi-
cal features is insufficient for creating an accurate computational version. There is a
necessity to consider the nano descriptors (NP descriptors), as nano characteristics
vary greatly. NP descriptors are distinctive qualities or a combination of different ma-
terials attributes.
The degree of injury may be estimated based on an assessment of these factors.
Corresponding to this, different NP descriptors were created as a synthesis of diverse
nanomaterial traits. In addition to a compound’s physical characteristics, the methods
used during its production, conjunction, and modification also served as the first set
of information to investigate its degree of toxicity. The information on these descrip-
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tors have to be improved because they could not be in the right format for integration
to computational frameworks. The conversion of all of these conceptual descriptors to
programming languages for computation constitutes one of the key problems. The
conversion of the original information into standard forms is challenging due to the
complexity and variability of NM. To transform such unprocessed information into an
arrangement that computers can read, certain conventional techniques and standards
are needed.
9.5 Model development
9.5.1 Building predictive models for nanotoxicity
To effectively mitigate the psychological toxicity associated with nanomaterials, models
must possess the capability to precisely forecast the physicochemical properties and po-
tential hazards shown by these materials [96]. The creation of nanomaterial prediction
models has the potential to facilitate the improvement of nanospecific testing and the
availability of open, curated information. Applying information may also be applied
within the framework of multicriteria decision analysis (MCDA) and expert elicitation
methodologies, to effectively prioritize hazards in areas where data availability is re-
stricted [97]. Furthermore, the incorporation of data about molecular and cellular inter-
actions is crucial for the advancement and execution of predictive models. The use of
high-throughput/in vitro testing is essential for the evaluation of possible hazards re-
lated to the widespread application of MNMs, as emphasized by Hartung [98]. The rein-
forcement of the aim behind advocating for animal research is evident via the European
Union’s restrictions on such research and the United States’ efforts to improve in vitro
and in silico toxicity techniques [99, 100]. The topic of nanotoxicology is relatively new,
providing opportunities for the investigation of novel testing procedures The results, en-
compassing both favorable and unfavorable consequences, of these efforts can offer
valuable knowledge for the evaluation of toxicity, risks, and the development of policies
for managing risks about other groups of chemicals or emerging technological fields
that have not yet received substantial research focus or been accompanied by efforts to
develop more advanced or alternative methods for toxicological evaluation field [101].
The use of predictive models in the assessment of nanotoxicity assumes a crucial
function in conducting a thorough examination of the potential risks associated with
the utilization of NPs and nanomaterials [102]. The main aim of these models is to pre-
dict the possible risk posed by NPs by the examination of their physicochemical charac-
teristics, such as size, shape, surface charge, and chemical composition, in conjunction
with their interactions with cellular tissues [103]. Several prominent kinds of predictive
models are often used in the domain of nanotoxicity.
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QSAR models: These models establish a correlation between the physicochemical
features of nanoparticles and their toxicological impact via the use of mathematical
formulas [104]. These models are predicated on the premise that NPs with comparable
characteristics would manifest comparable levels of toxicity. QSAR models can pro-
vide predictions about toxicity outcomes such as proliferation, genotoxicity, and in-
flammation [105].
Dose–response modeling: The primary objective of such models is to identify a
correlation between the concentration of NPs, sometimes referred to as the dosage,
and the resulting biological reaction, specifically in terms of toxicity [106]. These
measurements assist in determining the threshold concentration at which negative
consequences may manifest and offer useful knowledge into the link between dosage
and response [107].
The use of ML and AI models: The utilization of ML and AI methodologies, includ-
ing decision trees, random forests, SVMs, and deep learning (DL) neural networks, en-
ables the examination of intricate datasets for pattern recognition and the prediction
of nanotoxicity. The aforementioned models possess the capability to effectively pro-
cess a diverse array of input variables, including physicochemical qualities as well as
biological data [108].
Omics-based methodologies, including transcriptomics, proteomics, and metabo-
lomics, have the potential to facilitate the development of prediction models via the
identification of biological indicators or fingerprints that are indicative of the nano-
toxicity [109]. These models can offer valuable insights into the underlying processes
of toxicity and make predictions on the possible effects of nanoparticles on biological
tissues [110].
The use of read-across and grouping strategies in scientific research: The practice
of read-across entails the extrapolation of toxicity data from one NP to another, rely-
ing on resemblance in features or configurations. Grouping algorithms are used to
classify NPs into distinct categories, hence facilitating the estimation of toxicity for un-
tested NPs belonging to the same category [111].
The field of in silico modeling encompasses the use of computational simulations,
such as molecular dynamics and docking studies, to forecast the interactions between
NPs and biological molecules, for example, proteins, DNA, and biological elements
[112]. These simulations can offer valuable insights into putative pathways of toxicity.
Integrating multi-omics and systems biology models involves amalgamating many
data sources, including genomes, proteomics, and metabolomics, in conjunction with
computer models [113]. This approach facilitates a comprehensive understanding of
nanotoxicity processes. Systems biology methodologies are essential to comprehend the
intricate interplay inside biological structures [114].
Toxicogenomics and pathway analysis are scientific approaches that investigate
the comprehensive alterations in gene regulation resulting from exposure to NPs.
Pathway analysis methods are used to identify pivotal cellular processes that are im-
pacted by nanoparticles and make predictions about their toxicity [115].
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The use of toxicity databases and expert systems in academic research and scien-
tific studies: The availability of extensive databases containing detailed nanoparticle
toxicity data, as well as the use of specialized systems that exploit collected informa-
tion to provide forecasts, may serve as invaluable tools for t he anticipation of the
nanotoxicity [114].
9.5.2 Machine learning algorithms in QSPR modeling
ML has gained significant popularity in recent years due to its remarkable advance-
ments in various fields. It has found extensive usages in massive data collection and
parallel computation domains, like drug development, robotics, autonomous driving,
image recognition, and representing information [116, 117]. Drug development is recog-
nized well for its inefficiency primarily because to the substantial expense of research
(about US $2.6 billion per newly authorized medicine), a poor outcome rate in clinical
investigations (less than 12%), and a limited return on investment [118]. The utilization
of computer-assisted methodologies in the process of drug development, encompassing
design, assessment, and scrutiny, has the potential to decrease expenses, enhance the
rate of success, and improve the value of the return [119, 120]. Since the 1990s, various
methodologies like molecular dynamics (MD) simulation, homology modeling, QSAR
modeling and molecular docking, are being employed to investigate the mechanisms
underlying drug action [121–123]. The use of computational intelligence in conjunction
with large data analytics and high-performance computations has significantly en-
hanced the ef ficacy of drug development, particularly during the drug exploration
phase. An increasing number of pharmaceutical organizations are allocating resources
toward the adoption and integration of AI technologies. Now, the medical AI market is
estimated to be valued at over US $700 million, with projections indicating a compound
annual growth rate of 53%. The drug development sector now holds a significant por-
tion, over 35%, of the AI market share [124, 125]. In order to enhance the efficacy of
drug discovery and augment the likelihood of successful drug synthesis, numerous ML
enterprises have developed expertise in catering to the needs of pharmaceutical compa-
nies. These enterprises offer a range of services encompassing clinical image recogni-
tion, disease target identification, de novo drug design, compound screening [126],
toxicity prediction, and the prognostication of absorption, distribution, metabolism, and
excretion (ADME) [127, 128]. The AI business Benevolent recycled a medicine, as docu-
mented by Stephenson et al. [129]. Currently, this drug is undergoing second phase clini-
cal research and is being evaluated by Johnson & Johnson. Nevertheless, it is crucial to
possess knowledge about the advantages and disadvantages associated with various AI
systems, given that they are designed to be highly efficient for certain objectives [130].
Pharmaceutical businesses might potentially use diverse AI systems to effectively ad-
dress various domains, including drug development and clinical tria ls, hence poten-
tially uncovering innovative therapeutic interventions for intricate ailments [131].
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AI techniques are propelled by the use of novel machine learning methods, ad-
vancements in processing capabilities, and the continuous accumulation of experimen-
tal data [132]. Cutting-edge biotechnologies, like next-generation genomics [133], high-
throughput screening, cryoelectron microscopy, medical digitization, and internet-of-
things infrastructure [134, 135], offer valuable and extensive datasets that can be utilized
in ML-based drug development. ML algorithms can be broadly categorized into three
main types: unsupervised, supervised, and reinforced [136, 137]. Unsupervised ML tech-
niques have been used to uncover latent patterns within medical and scientific datasets,
as well as to discern novel infection domains [138]. The incorporation of supervised ML
techniques has been shown in the prediction of pharmacological effects, adverse effects,
and ADME properties. Previous studies have utilized existing data from pharmaceuticals
and clinical trials to achieve these predictions. DL, being an advanced form of ML, has
unparalleled potential to enhance and expand the capabilities of ML. The use of AI is
anticipated to emerge as a prominent and economically viable approach in the realm of
medication virtual screening, offering reduced risks [139, 140].
The concept of QSAR, first introduced by Corwin Hansch and his colleagues in
1962, is a logical progression from the principles of physical chemistry to the realm of
virtual drug screening. Over the course of five decades, QSAR has seen significant ad-
vancements via multidisciplinary discoveries and community engagement [141]. This
evolution has propelled QSAR from its initial state as a basic regression analysis
method, limited to handling related molecules, to a sophisticated multiple statistics
ML methodology capable of analyzing extensive datasets including diverse chemical
structures. The utilization of QSAR models has been extensive in the modeling of the
biophysical properties of various chemicals[142].Additionally,thesemodelshave
been employed in the evaluation of the potential impacts of pharmaceuticals, chemi-
cals, and nanomaterials on both human health and ecosystems [143, 144] developed a
QSAR system known as MS-HEMs for the purpose of handling high-energy com-
pounds. Within the domain of computer-aided drug design (CADD), the utilization of
QSAR has been acknowledged well as a proficient approach for the purpose of design-
ing and refining drugs based on structural and knowledge-based principles [145]. This
article provides a comprehensive examination of the benefits, dependability, and lim-
its of QSAR in the context of generating novel insights into ML-based QSAR models.
Additionally, it explores the integration of these models with experimental or simula-
tion data and discusses their possible applications [144, 146].
9.5.3 Model validation and performance assessment
Nanotechnology encompasses a wide range of scholarly disciplines, including nanoma-
terials, nanoelectronics, nanomedicine, and nanomanufacturing. Models and simula-
tions play a crucial role in understanding and predicting occurrences at the nanoscale,
as well as offering instructions for experimental pursuits [147]. However, in order to
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ensure the validity of the results obtained from these models and experiments, it is im-
portant to conduct a comprehensive verification and evaluation of their performance.
The validation and assessment of models and techniques in the field of nanotechnology
are of paramount importance in ensuring their reliability and effectiveness [148].
Model validation is the methodical evaluation of the extent to which a computer model
accurately represents and reproduces the attributes and dynamics of the real-world sys-
tem it intends to simulate. Within the realm of nanotechnology, it is essential to ascer-
tain the congruity between the prognostications posited by the model and the empirical
observations as well as the theoretical postulations. When doing model validation, it is
crucial to consider several significant elements [149]. The present discourse aims to dis-
cuss the significance of empirical evidence in academic research. Evaluate the agree-
ment between model predictions and empirical observations, ideally obtained under
rigorously controlled and reproducible conditions. The dataset may include several at-
tributes of nanoscale systems, including their structural, mechanical, electrical, and
other pertinent dimensions [150]. Theoretical expectations refer to the anticipated out-
comes or predictions that are based on theoretical frameworks or models. Examine if
the predictions made by the model are consistent with well-established physical princi-
ples and theories that are relevant to the investigation of nanoscale phenomena [151].
The purpose of doing a sensitivity analysis is to assess the impact of variations in model
parameters on the precision of predictions. This technique facilitates the discovery of
pivotal parameters and the assessment of the model’s resilience [152]. Cross-validation
is a widely used approach in the field of ML that serves the purpose of assessing the
performance of a model in accurately predicting data points that have not been previ-
ously seen. The process entails partitioning the given dataset into distinct subsets for
training and testing purposes, hence facilitating the evaluation of the model’s predicted
performance [153]. Evaluation of performance extends over simple validation of mod-
els, including the evaluation of both the dependability and excellence of experimental
approaches and computational models [154] and involves:
(1) Analyzing the quantitative measures of accuracy and precision in exper imental
measurements or model predictions. Statistical analysis and the estimate of er-
rors are of paramount importance [155].
(2) Reproducibility refers to the evaluation of the capacity to repeat experimental
findings or computer simulations in various laboratory and environmental condi-
tions. The need for standardizing procedures is often observed [156].
(3) Limitations and uncertainty: This section aims to identify and quantify the sour-
ces of uncertainty and limitations present in both experimental procedures and
computational models. The incorporation of uncertainty analysis is of paramount
importance in the process of decision-making [156].
(4) Benchmark ing is the comparison of p erformance across many mo dels or experi-
mental methodologies in order to address a specific nanoscale challenge. Bench-
marking is a valuable tool for determining the most dependable and precise
methodology [156].
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