Preparation and Fundamental Characterization of Cellulose Nanocrystal from Oil Palm Fronds Biomass

The objective of this work was to isolate cellulose nanocrystal (CNC) from oil palm fronds (Elaeis guineensis) and its subsequent characterization. Isolation involves sodium hydroxide/anthraquinone pulping with mechanical refining followed by total chlorine free bleaching (includes oxygen delignification, hydrogen peroxide oxidation and peracetic acid treatment) before acid hydrolysis. Bleaching significantly decreased kappa number and increased α-cellulose percentage of fibers as confirmed by Technical Association of the Pulp and Paper Industry standards. Transmission electron microscopy (TEM), X-ray diffraction, Fourier transform infrared spectroscopy and thermogravimetric analysis revealed that acid hydrolysis along with bleaching improved crystallinity index and thermal stability of the extracted nanocrystals. It was observed that CNC maintained its cellulose 1 polymorph despite hydrolysis treatment. Mean diameter as observed by TEM and average fiber aspect ratio of obtained CNC was 7.44 ± 0.17 nm and 16.53 ± 3.52, respectively making it suitable as a reinforcing material for nanocomposite.     

Wood Polypropylene (PP) Composites Manufactured by Mango Wood Waste with Virgin or Recycled PP: Mechanical,Morphology, Melt Flow Index and Crystalline Behaviour

Compositions of wood-polypropylene composites (WPCs) are prepared through melt compounding followed by injection moulding. WPCs are formulated for eight compositions with a different weight ratio of wood, virgin or recycled polypropylene and coupling agent. WPCs compositions are compared in terms of Melt Flow Index, Tensile, FESEM images, Flexural and crystallinity index for same operating variable conditions. From the results, recycled polypropylene based WPCs are superior in comparison to virgin polypropylene based WPCs. With the addition of 5 % coupling agent in recycled polypropylene-based composites for 45:50 composition, tensile and flexural values of WPCs are higher in comparison to all composition and neat virgin or recycled polypropylene. This work stands for the utilization of waste wood with recycled plastic for replacement of wood and virgin plastic.     

Food Industry Co-streams: Potential Raw Materials for Biodegradable Mulch Film Applications

Vast amounts of co-streams are generated from plant and animal-based food processing industries. Efficient utilization of these co-streams is important from an economic and environmental perspective. Non-utilization or under-utilization of co-streams results in loss of potential revenues, increased disposal cost of these products and environmental pollution. At present, extensive research is taking place around the globe towards recycling of co-streams to generate value-added products. This review evaluates various co-streams from food processing industries as raw materials in developing biodegradable agricultural mulching applications. Among the agriculture-based co-streams, potato peels, tomato peels, carrot residues, apple pomace, coffee residues and peanut residues were reviewed with respect to production amount, composition, film forming components and film forming capabilities. Similarly, selected co-streams from slaughterhouses, poultry and fish processing industries were also reviewed and evaluated for the same purpose.     

Preparation and Properties of a Chitosan–Hyaluronic Acid-Polypyrrole Conductive Hydrogel Catalyzed by Laccase

Electro conductive hydrogels, consisting of chitosan (CS), hyaluronic acid (HA), and polypyrrole (PPy), were prepared via an in situ enzymic polymerization of pyrrole in the CS–HA hydrogel, using laccase as the catalyst. This CS–HA–PPy composite hydrogel showed good conductivity. The chemical structure and morphology of this conductive hydrogel were studied by Fourier transform infrared spectroscopy, scanning electron microscopy, and X-ray diffraction technique. For CS–HA–PPy and CH–HA hydrogel, the temperature at which fastest decomposition occurred was 260 and 244 °C, respectively. That means the thermal stability of CS–HA–PPy is better than CS–HA hydrogel. The conductive hydrogel also showed excellent swelling and deswelling behaviors.     

Environmentally Friendly Oil-Modified Polyesters as Polymeric Plasticizers for Poly(vinyl chloride)

Oil-modified polyesters were synthesized to serve as polymeric plasticizers for PVC. A total of four polymeric plasticizers with different average molecular weights were prepared. Characterizations were done using Fourier-transformed infrared spectroscopy, proton nuclear magnetic resonance spectroscopy, and gel permeation chromatography. Some of the tests conducted on PVC films include thermal stability test using thermogravimetric analyser, determination of glass transition temperature (T_g), plasticizer migration and leaching resistance test, morphology study of plasticized PVC films using field emission scanning microscope, toxicity test, and tensile test. Owing to the plasticizing effect of the palm oil-based compound, T_g of the plasticized PVC has decreased to an average of 65 °C at 20 wt% loading. The polymeric plasticizer is also able to contribute positively to the thermal stability and mechanical properties of the PVC films. Some of the advantages of incorporating polymeric plasticizer with high molecular weight includes lower rate of leaching from plastic, and improved tensile strength and elongation at break. Besides, thermal stability of the plastic studied using Kissinger’s and Flynn–Wall–Ozawa’s approaches shows that PVC blended with high molecular weight oil-modified polyester is more thermally stable, evidenced by the increase in the activation energy of decomposition, E_d. Toxicity test using brine shrimp egg shows encouraging results, where the oil-based plasticizer is considerably less toxic compared to some of the commercial plasticizers.     

Optimization of Pellet Production from Agro-Industrial By-Products: Effect of Plasticizers on Properties of Pellets and Composite Pots

The present study aimed to optimize the pellets formulation (deoiled rice bran, potato peel powder and plasticizers) for the development of the injection molded pots. The maximum hardness and bulk density (desirable responses) were obtained for pellets having 100 g of deoiled rice bran, 100 g potato peel powder and 14 % of cashew nut shell liquid (CNSL) as well as 14 % of glycerol (GL) (on raw material basis). The optimized pellets and the pots developed from them were characterized for their physico-chemical, functional, rheological and morphological properties. Expansion ratio, pellet durability index and hardness of the pellets with 14 % CNSL were found to be 1.097, 98.647 % and 485.551 N, respectively. For pellets with 14 % GL expansion ratio, pellet durability index and hardness were found to be 1.150, 97.747 % and 462.949 N, respectively. The biodegradation analysis of the pots developed from optimized pellets with 14 % CNSL and GL degraded in 11 and 9 weeks, respectively. Porosity, puncture force, density and hardness of ‘AP’ pots were 27.473 %, 495.731 N, 1.549 g/ml and 542.641 N, respectively. However, for ‘BP’ pots, the porosity, puncture force, density and hardness were 32.548 %, 440.149 N, 1.191 g/ml and 507.841 N, respectively. Pots prepared from 14 % CNSL (AP) were better in physical and mechanical properties as compared to pots developed from glycerol.     

Nearest neighbor methods for testing reflexivity

Nearest neighbor (NN) methods are widely employed for drawing inferences about spatial point patterns of two or more classes. We introduce a method for testing reflexivity in the NN structure (i.e., NN reflexivity) based on a contingency table which will be called reflexivity contingency table (RCT) henceforth. The RCT is based on the NN relationships among the data points and was used for testing niche specificity in literature, but we demonstrate that it is actually more appropriate for testing the NN reflexivity pattern. We derive the asymptotic distribution of the entries of the RCT under random labeling and introduce tests of reflexivity based on these entries. We also consider Pielou’s approach on RCT and show that it is not appropriate for completely mapped spatial data. We determine the appropriate null hypotheses and the underlying conditions/assumptions required for all tests considered. We investigate the finite sample performance of the tests in terms of empirical size and power by extensive Monte Carlo simulations and illustrate the methods on two real-life ecological data sets.     

Fourth-corner correlation is a score test statistic in a log-linear trait–environment model that is useful in permutation testing

Ecologists wish to understand the role of traits of species in determining where each species occurs in the environment. For this, they wish to detect associations between species traits and environmental variables from three data tables, species count data from sites with associated environmental data and species trait data from data bases. These three tables leave a missing part, the fourth-corner. The fourth-corner correlations between quantitative traits and environmental variables, heuristically proposed 20 years ago, fill this corner. Generalized linear (mixed) models have been proposed more recently as a model-based alternative. This paper shows that the squared fourth-corner correlation times the total count is precisely the score test statistic for testing the linear-by-linear interaction in a Poisson log-linear model that also contains species and sites as main effects. For multiple traits and environmental variables, the score test statistic is proportional to the total inertia of a doubly constrained correspondence analysis. When the count data are over-dispersed compared to the Poisson or when there are other deviations from the model such as unobserved traits or environmental variables that interact with the observed ones, the score test statistic does not have the usual chi-square distribution. For these types of deviations, row- and column-based permutation methods (and their sequential combination) are proposed to control the type I error without undue loss of power (unless no deviation is present), as illustrated in a small simulation study. The issues for valid statistical testing are illustrated using the well-known Dutch Dune Meadow data set.     

A graphical assessment and spatial clustering of the Deepwater Horizon oil spill impact on Laughing Gulls

The Deepwater Horizon oil spill occurred in the Gulf of Mexico on April 20, 2010. Considered the largest accidental marine oil spill in history, oil flowed for three months and approximately five million barrels of oil spilled through by mid-July 2010. In this article, we analyze bird data to assess the impact of the oil spill on the Gulf wildlife. Particularly, we want to determine in which regions Laughing Gulls were mostly affected by the oil spill, and whether those regions spatially shifted throughout the year 2010. Though our data sets have some limitations to apply statistical analysis methods, we obtained very interesting results. Our analyses showed the general consistency of the results based on two population data sets (from the 2011 Data Expo and from the eBird community) and justified the use of both publicly available data sets. We showed that the closer the surface oil spill area approached to the Laughing Gulls habitats, the more significant clusters of bird cases were observed.     

Investigating the link between $$\hbox {PM}_{2.5}$$ and atmospheric profile variables via penalized functional quantile regression

Fine particulate matter (\(\hbox {PM}_{2.5}\)) events negatively affect the health of numerous persons globally each year. Previous works have described the association between air pollution and surface-level meteorological conditions; however, there has been less focus on the task of linking air pollution events with meteorological conditions at higher levels of the atmosphere. Working within the functional data framework, we develop a penalized functional quantile regression (PFQR) procedure to model conditional quantiles of a continuous response based on a functional covariate, with the ability to penalize selected derivatives of the estimated coefficient function. Our aim is to investigate the relationship between atmospheric profile variables (APVs), assumed to be functional, and key quantiles of the conditional distribution of surface-level \(\hbox {PM}_{2.5}\). Via a simulation study, we find that the performance of our PFQR procedure compares favorably to other related approaches. We conclude with an analysis of \(\hbox {PM}_{2.5}\) data at two Southeastern US locations, Columbia, SC and Tampa, FL, where we estimate the coefficient functions for the APVs corresponding to both ‘typical’ and ‘high’ \(\hbox {PM}_{2.5}\) events. As we believe that the true coefficient functions are smooth and may be exactly zero over subsets of their domains, we impose penalties on the 0th and 2nd derivatives. Our analysis indicates that the corresponding atmospheric conditions differ between the two locations, and that the conditions differ seasonally within location.