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Advances in green chemistry over the past 25 years have improved sustainability in the development of new cosmetic and personal care products.

A new sustainable material for storing heat and releasing it on demand has been demonstrated for long-term latent heat storage (LLHS). The material consists of a high-latent-heat sugar alcohol phase change material (PCM) dispersed within ionic cross-linked matrices of polyvinyl alcohol (PVA). This material's unique property is the inhibition of undesired crystallization of the PCM during cooling due to the strong intermolecular interactions of the polymeric matrices, which leads to vitrification instead of crystallization. The release of latent heat can be controlled due to the PCM's stability below its cold-crystallization, which is triggered by reheating, as demonstrated by differential scanning calorimetry (DSC), optical microscopy (OM) and in situ X-ray diffraction (XRD). The addition of an ionic citrate cross-linker further tunes the vitrification and cold-crystallization properties of the PCM. Homogeneity and the presence of hydrogen bonding of the cold-crystalizing PCM (CC-PCM) were studied by scanningelectron microscopy-energy dispersive X-ray spectroscopy (SEM-EDX), Fourier transform infrared spectroscopy (FTIR) and XRD. Thermal stability was confirmed by thermogravimetric analysis (TGA) and 100 consecutive DSC heating-cooling cycles. The CC-PCM demonstrated high latent heat of fusion, up to 266 J g(-1), depending on the composition. As a super-adsorbent, PVA was able to swell and hold the liquid PCM resulting in form-stability and leakage-preventive properties above the melting temperature. Taken together, these results confirm that PVA matrices are promising for the thermal and structural stabilization of sugar alcohol PCMs, overcoming unexpected heat release and phase separation, and withstanding repeated melting-cooling cycles for LLHS.

Metal oxide nanoparticles are emerging as important contributors in a variety of applications including water treatment, catalytic transformations, and energy generation and storage, among others. Controlling size and shape is of significant interest in the nanotechnology community as these are critical in determining nanoparticle performance, impacting properties such as reactivity, conductivity, and magnetic behavior. In addition to employing green solvents, supercritical fluid nanoparticle synthesis is a robust and facile method to meet the need to control size and shape for a variety of metal oxide nanoparticles. Supercritical water, supercritical ethanol, and supercritical carbon dioxide solvent systems offer tunable properties that allow for control of nanoparticle size and shape. This review investigates the synthesis routes, the mechanisms for size and shape control, and unique characteristics particular to each green solvent. Finally, a decision tree is developed to facilitate synthetic route design for the intended nano metal oxide composition, size, and shape that highlights the need for consideration of energy and life cycle impacts.

The field of Green Chemistry has seen many scientific discoveries and inventions during the 20 years since the 12 Principles were first published. Inspired by tree diagrams that illustrate diversity of products stemming from raw materials, we present here the Green ChemisTREE as a showcase for the diversity of research and achievements stemming from Green Chemistry. Each branch of the Green ChemisTREE represents one of the 12 Principles, and the leaves represent areas of inquiry and development relevant to that Principle (branch). As such, in this ‘meta-review’, we aim to describe the history and current status of the field of Green Chemistry: by exploring activity within each Principle, by summarizing the benefits of Green Chemistry through robust examples, by discussing tools and metrics available to measure progress towards Green Chemistry, and by outlining knowledge gaps, opportunities, and future challenges for the field.

The NRF2-ARE antioxidant pathway is an important biological sensing and regulating system that responds to xenochemicals. NRF2 senses chemically-caused production of reactive oxygen species (ROS) and electrophilic interactions with chemical species. Upon NRF2 activation, the expression of a wide array of genes will be upregulated to counteract oxidative or electrophilic insults. However, when the external disruption exceeds the inherent resilience of the biological system, cellular damage can occur, eventually leading to cytotoxicity. Induced NRF2 activity in in vitro assays is therefore a signal that a man-made chemical may cause unwanted cellular activity. This was the motivation to derive a chemical design strategy to minimize the risk that new chemicals would perturb this pathway. We constructed a logistic regression model using design variables derived from density functional theory (DFT) calculations and physical properties. The model showed excellent predictive power to distinguish between NRF2-active and inactive chemicals based on the EPA ToxCast high throughput screen (HTS) assay data (tested in the concentration range of 10−3–102 μM). External evaluation showed that the area under the curve (AUC) for the receiver operating characteristic (ROC) of the model is 0.81 and the precision is 0.90. Combining this model with a previously developed cytotoxicity model, we developed a probabilistic design diagram to guide chemical design with the twin goals of minimizing NRF2 antioxidant pathway activity and cytotoxicity. This work initiated a simultaneous design strategy against two toxicity pathways of interest in molecular design research.

Toxicity is a concern with many chemicals currently in commerce, and with new chemicals that are introduced each year. The standard approach to testing chemicals is to run studies in laboratory animals (e.g. rats, mice, dogs), but because of the expense of these studies and concerns for animal welfare, few chemicals besides pharmaceuticals and pesticides are fully tested. Over the last decade there have been significant developments in the field of computational toxicology which combines in vitro tests and computational models. The ultimate goal of this field is to test all chemicals in a rapid, cost effective manner with minimal use of animals. One of the simplest measures of toxicity is provided by high-throughput in vitro cytotoxicity assays, which measure the concentration of a chemical that kills particular types of cells. Chemicals that are cytotoxic at low concentrations tend to be more toxic to animals than chemicals that are less cytotoxic. We employed molecular characteristics derived from density functional theory (DFT) and predicted values of log(octanol–water partition coefficient) (log P) to construct a design variable space, and built a predictive model for cytotoxicity based on U.S. EPA Toxicity ForeCaster (ToxCast) data tested up to 100 μM using a Naive Bayesian algorithm. External evaluation showed that the area under the curve (AUC) for the receiver operating characteristic (ROC) of the model to be 0.81. Using this model, we provide probabilistic design rules to help synthetic chemists minimize the chance that a newly synthesized chemical will be cytotoxic.

In silico toxicity models are critical in addressing experimental aquatic toxicity data gaps and prioritizing chemicals for further assessment. Currently, a number of predictive in silico models for aquatic toxicity are available, but most models are challenged to produce accurate predictions across a wide variety of functional chemical classes. Appropriate model selection must be informed by the models’ applicability domain and performance within the chemical space of interest. Herein we assess five predictive models for acute aquatic toxicity to fish (ADMET Predictor™, Computer-Aided Discovery and REdesign for Aquatic Toxicity (CADRE-AT), Ecological Structure Activity Relationships (ECOSAR) v1.11, KAshinhou Tool for Ecotoxicity (KATE) on PAS 2011, and Toxicity Estimation Software Tool (TEST) v.4). The test data set was carefully constructed to include 83 structurally diverse chemicals distinct from the training data sets of the assessed models. The acute aquatic toxicity models that rely on properties related to chemicals’ bioavailability or reactivity performed better than purely statistical algorithms trained on large sets of chemical properties and structural descriptors. Most models showed a marked decrease in performance when assessing insoluble and ionized chemicals. In addition to comparing tool accuracy and, this analysis provides insights that can guide selection of modeling tools for specific chemical classes and help inform future model development for improved accuracy.

Editorial to accompany the part-themed issue on molecular design for reduced toxicity.

The routine rational design of commercial chemicals with minimal toxicological hazard to humans and the environment is a key goal of green chemistry. The development of such a design strategy requires an understanding of the interrelationships between physical–chemical properties, structure, mechanisms and modes of action. This study develops property-based guidelines for the design of chemicals with reduced chronic aquatic toxicity to multiple standardized species and endpoints by exploring properties associated with bioavailability, narcotic toxicity and reactive modes of action, such as electrophilic interactions. Two simple properties emerge as key parameters that distinguish chemicals in the Low EPA level of concern to three aquatic species from those in the High level of concern – octanol–water partition coefficient, (log Po–w) and ΔE (LUMO–HOMO energy gap). Physicochemical properties were predicted using Schrodinger's QikProp, while frontier orbital energies were determined based on AM1 and DFT calculations using Gaussian03. Experimental toxicity data used consisted of chronic toxicity thresholds (NOEC) for Daphnia magna reproduction (317 compounds, 504 h-assay) and Oryzias latipes (Japanese medaka, 122 compounds in 336, 504 and 672 h assays) survival, and Pseudokirchneriella subcapitata, a green algae model (392 compounds). Results indicate that 92% of compounds of Low chronic concern have log Po–w values 9 eV. Chronically safe compounds to P. subcapitata meet similar criteria – 80% have log Po–w values < 3 and ΔE greater than 9 eV. Our work proposes design guidelines that can be used to significantly increase the probability that a chemical will have low chronic toxicity, based on the endpoints evaluated, to the three diverse aquatic species studied, and potentially to other aquatic species.

One of the most elusive yet significant goals of green chemistry is the routine design of commercially useful chemicals with reduced toxicological hazard. The main objective of this study was to derive property guidelines for the design of chemicals with reduced acute aquatic toxicity to multiple species. The properties explored included chemical solubilities, size, shape and molecular orbital energies. Physicochemical properties were predicted using Schrodinger's QikProp, while frontier orbital energies (HOMO, LUMO and HOMO–LUMO gap) were determined based on AM1 and DFT calculations using Gaussian03. Experimental toxicity data included acute toxicity thresholds (LC50) for the fathead minnow (Pimephales promelas; 570 compounds), the Japanese medaka (Oryzias latipes; 285 compounds), a cladoceran (Daphnia magna; 363 compounds) and green algae (Pseudokirchneriella subcapitata, 300 compounds). Mechanistically-driven qualitative and quantitative analyses between the in-silico predicted molecular properties and in vivo toxicity data were explored in order to propose property limits associated with higher probabilities of acutely safe chemicals. The analysis indicates that 70–80% of the compounds that have low or no acute aquatic toxicity concern by EPA guidelines to the four species have a defined range of values for octanol-water partition coefficient (logPo/w) and ΔE (LUMO–HOMO gap). Compounds with logPo/w values less than 2 and ΔE (AM1) greater than 9 eV are significantly more likely to have low acute aquatic toxicity compared to compounds that do not meet these criteria. These results are mechanistically rationalized. Our work proposes design guidelines that can be used to significantly increase the probability that a chemical will have low acute toxicity to the four species studied, and potentially other aquatic species.

Supercritical carbon dioxide (scCO2) was used to extract components of interest from Scenedesmus dimorphus, a microalgae species, under varied algal harvesting and extraction conditions. Liquid chromatography-mass spectrometry (LC-MS) was used to quantify the concentration of fatty acid methyl esters (FAME) and the FAME profile of transesterified lipids, phospholipids and pigments extracted under varied supercritical temperatures and pressures. The scCO2 extraction results are compared with conventional solvent extraction to evaluate differences in the efficiency and nature of the extracted materials. Algae harvested by centrifugation (vs.lyophilization) demonstrated a similar extraction efficiency in scCO2, indicating potential energy benefits by avoiding conventional algal mass dehydration prior to extraction. Centrifuged algae and optimized extraction conditions (6000 psi; 100 °C) resulted in comparable FAME yields to conventional processes, as well as increased selectivity, reflected in the decreased pigment, nitrogen and phospholipid contamination of the FAME. Cell pre-treatments—sonication, microwave, bead beating and lyophilization—showed an enhancement in extraction yield in both conventional solvent and scCO2 extraction, allowing for improved extraction efficiencies. This study suggests that scCO2, a green solvent, shows great potential for algal lipid extraction for the sustainable production of biodiesel.

Algae are being explored as a sustainable energy feedstock, having potential to reduce dependence on petrofuels and offset greenhouse gas emissions. Economic considerations and principles of green design suggest that if algae-to-fuel technology is to be successful, biofuels must be produced simultaneously with value-added co-products. At present, the algae industry is centered around a limited number of products, such as low-volume/high-value speciality nutrients. New products for medium- and high-volume markets will be needed as biomass production increases in scale. This Perspective highlights non-fuel applications of algal biomass that have received relatively little attention to date but are promising for future development. It is our goal to draw attention to some of the unique opportunities that algae present with respect to biochemical composition as compared to lignocellulosic energy crops.