Data-Driven Quantitative Intrinsic Hazard Criteria for Nanoproduct Development in a Safe-by-Design Paradigm: A Case Study of Silver Nanoforms.

The current European (EU) policies, that is, the Green Deal, envisage safe and sustainable practices for chemicals, which include nanoforms (NFs), at the earliest stages of innovation. A theoretically safe and sustainable by design (SSbD) framework has been established from EU collaborative efforts toward the definition of quantitative criteria in each SSbD dimension, namely, the human and environmental safety dimension and the environmental, social, and economic sustainability dimensions. In this study, we target the safety dimension, and we demonstrate the journey toward quantitative intrinsic hazard criteria derived from findable, accessible, interoperable, and reusable data. Data were curated and merged for the development of new approach methodologies, that is, quantitative structure-activity relationship models based on regression and classification machine learning algorithms, with the intent to predict a hazard class. The models utilize system (i.e., hydrodynamic size and polydispersity index) and non-system (i.e., elemental composition and core size)-dependent nanoscale features in combination with biological in vitro attributes and experimental conditions for various silver NFs, functional antimicrobial textiles, and cosmetics applications. In a second step, interpretable rules (criteria) followed by a certainty factor were obtained by exploiting a Bayesian network structure crafted by expert reasoning. The probabilistic model shows a predictive capability of ≈78% (average accuracy across all hazard classes). In this work, we show how we shifted from the conceptualization of the SSbD framework toward the realistic implementation with pragmatic instances. This study reveals (i) quantitative intrinsic hazard criteria to be considered in the safety aspects during synthesis stage, (ii) the challenges within, and (iii) the future directions for the generation and distillation of such criteria that can feed SSbD paradigms. Specifically, the criteria can guide material engineers to synthesize NFs that are inherently safer from alternative nanoformulations, at the earliest stages of innovation, while the models enable a fast and cost-efficient in silico toxicological screening of previously synthesized and hypothetical scenarios of yet-to-be synthesized NFs.

Quantitative surface free energy with micro-colloid probe pairs.

Measurement of the surface free energy (SFE) of a material allows the prediction of its adhesion properties. Materials can have microscale or sub-microscale surface inhomogeneities, engineered or random, which affect the surface macroscopic behaviour. However, quantitative characterization of the SFE at such length scales remains challenging in view of the variety of instruments and techniques available, the poor knowledge of critical variables and parameters during measurements and the need for appropriate contact models to derive the SFE from the measurements. Failure to characterize adhesion correctly may result in defective components or lengthy process optimization costing billions to industry. Conversely, for planar and homogeneous surfaces, contact angle (CA) measurements are standardized and allow for calculating the SFE using for example the Owen-Wendt model, relying only on the properties of the probing liquids. As such, we assessed and report here a method to correlate quantitative measurements of force-distance curves made with an atomic force microscope (AFM) and with silica and polystyrene (PS) colloidal probe pairs, with quantitative CA measurements and CA-derived SFE values. We measured five surfaces (mica, highly oriented pyrolytic graphite, thermally grown silica on silicon, silicon, and silicon with a super-hydrophobic coating), ranging from hydrophilic to super-hydrophobic, and found an excellent classification of the AFM measurements when these are represented by a set of principal components (PCs), and when both silica and PS colloidal probes are considered together. A regression of the PCs onto the CA measurements allows recovery of the SFE at the length scale of the colloidal probes, which is here ca. 1 micron. We found that once the PC-regression model is built, test sets of only ten AFM force-distance curves are sufficient to predict the local SFE with the calibrated silica and PS colloidal probes.

Guest Molecule-Mediated Energy Harvesting in a Conformationally Sensitive Peptide-Metal Organic Framework.

The apparent piezoelectricity of biological materials is not yet fully understood at the molecular level. In particular, dynamic noncovalent interactions, such as host-guest binding, are not included in the classical piezoelectric model, which limits the rational design of eco-friendly piezoelectric supramolecular materials. Here, inspired by the conformation-dependent mechanoresponse of the Piezo channel proteins, we show that guest-host interactions can amplify the electromechanical response of a conformationally mobile peptide metal-organic framework (MOF) based on the endogenous carnosine dipeptide, demonstrating a new type of adaptive piezoelectric supramolecular material. Density functional theory (DFT) predictions validated by piezoresponse force microscopy (PFM) measurements show that directional alignment of the guest molecules in the host carnosine-zinc peptide MOF channel determines the macroscopic electromechanical properties. We produce stable, robust 1.4 V open-circuit voltage under applied force of 25 N with a frequency of 0.1 Hz. Our findings demonstrate that the regulation of host-guest interactions could serve as an efficient method for engineering sustainable peptide-based power generators.

Diphenylalanine-Derivative Peptide Assemblies with Increased Aromaticity Exhibit Metal-like Rigidity and High Piezoelectricity.

Diphenylalanine (FF) represents the simplest peptide building block that self-assembles into ordered nanostructures with interesting physical properties. Among self-assembled peptide structures, FF nanotubes display notable stiffness and piezoelectric parameters (Young's modulus = 19-27 GPa, strain coefficient d33 = 18 pC/N). Yet, inorganic alternatives remain the major materials of choice for many applications due to higher stiffness and piezoelectricity. Here, aiming to broaden the applications of the FF motif in materials chemistry, we designed three phenyl-rich dipeptides based on the ß,ß-diphenyl-Ala-OH (Dip) unit: Dip-Dip, cyclo-Dip-Dip, and tert-butyloxycarbonyl (Boc)-Dip-Dip. The doubled number of aromatic groups per unit, compared to FF, produced a dense aromatic zipper network with a dramatically improved Young's modulus of ∼70 GPa, which is comparable to aluminum. The piezoelectric strain coefficient d33 of ∼73 pC/N of such assembly exceeds that of poled polyvinylidene-fluoride (PVDF) polymers and compares well to that of lead zirconium titanate (PZT) thin films and ribbons. The rationally designed π-π assemblies show a voltage coefficient of 2-3 Vm/N, an order of magnitude higher than PVDF, improved thermal stability up to 360 °C (∼60 °C higher than FF), and useful photoluminescence with wide-range excitation-dependent emission in the visible region. Our data demonstrate that aromatic groups improve the rigidity and piezoelectricity of organic self-assembled materials for numerous applications.

Rheological Issues in Carbon-Based Inks for Additive Manufacturing.

As the industry and commercial market move towards the optimization of printing and additive manufacturing, it becomes important to understand how to obtain the most from the materials while maintaining the ability to print complex geometries effectively. Combining such a manufacturing method with advanced carbon materials, such as Graphene, Carbon Nanotubes, and Carbon fibers, with their mechanical and conductive properties, delivers a cutting-edge combination of low-cost conductive products. Through the process of printing the effectiveness of these properties decreases. Thorough optimization is required to determine the idealized ink functional and flow properties to ensure maximum printability and functionalities offered by carbon nanoforms. The optimization of these properties then is limited by the printability. By determining the physical properties of printability and flow properties of the inks, calculated compromises can be made for the ink design. In this review we have discussed the connection between the rheology of carbon-based inks and the methodologies for maintaining the maximum pristine carbon material properties.