Synthesis,Polymorphism and Thermal Decomposition Process of (n-C4H9)4NRE(BH4)4 for RE = Ho,Tm and Yb

In total, three novel organic derivatives of lanthanide borohydrides, n-But₄NRE(BH₄)₄ (TBAREB), RE = Ho, Tm, Yb, have been prepared utilizing mechanochemical synthesis and purified via solvent extraction. Studies by single crystal and powder X-ray diffraction (SC-XRD and PXRD) revealed that they crystalize in two polymorphic forms, α- and β-TBAREB, adopting monoclinic (P2₁/c) and orthorhombic (Pnna) unit cells, previously found in TBAYB and TBAScB, respectively. Thermal decomposition of these compounds has been investigated using thermogravimetric analysis and differential scanning calorimetry (TGA/DSC) measurements, along with the analysis of the gaseous products with mass spectrometry (MS) and with analysis of the solid decomposition products with PXRD. TBAHoB and TBAYbB melt around 75 °C, which renders them new ionic liquids with relatively low melting points among borohydrides.     

Scaling confirmation of the thermodynamic dislocation theory

The thermodynamic dislocation theory (TDT) is based on two highly unconventional assumptions: first, that driven systems containing large numbers of dislocations are subject to the second law of thermodynamics and second, that the controlling inverse timescale for these systems is the thermally activated rate at which entangled pairs of dislocations become unpinned from each other. Here, we show that these two assumptions predict a scaling relation for steady-state stress as a function of strain rate and that this relation is accurately obeyed over a wide range of experimental data for aluminum and copper. This scaling relation poses a stringent test for the validity of the TDT. The fact that the TDT passes this test means that a wide range of problems in solid mechanics, previously thought to be fundamentally intractable, can now be addressed with confidence.

For almost a century, the dislocation theory of crystalline deformation has played a central role in materials science. Unfortunately, this theory has made only modest progress for about seven decades. Although crystalline solids are essential in engineering applications and although modern experimental techniques have provided a wealth of information about dislocations in these solids, the theories developed to explain dislocation-driven phenomena have been primarily phenomenological. They describe phenomena mathematically but do not explain them; they are not predictive.The cause of this theoretical failure is clear. Dislocation-driven deformations of solids are complex nonequilibrium processes involving macroscopic numbers of dynamical degrees of freedom. Theoretical physicists know that they must use statistical methods to deal with such situations. Especially important is the second law of thermodynamics, which states that driven complex systems must move toward their most probable configurations (i.e., that their entropies must be nondecreasing functions of time). However, leading materials scientists since the 1950s have asserted that dislocation energies are too large, and that dislocation entropies are too small, for the second law to be applicable (1, 2).We have argued for a decade that those assertions are wrong. The thermodynamic dislocation theory (TDT) is based directly on the second law. It was introduced in 2010 (3) and has been shown in a series of publications since then (4–13) to be capable of solving a wide range of the most important problems in solid mechanics including strain hardening, elastic–plastic yielding, shear banding, grain-size effects, and the like. Those problems were out of reach of the conventional approaches. However, questions remain. How sure are we that the TDT is more reliable than the observation-based phenomenologies? Can we use it confidently to solve important materials problems that have been left untouched by the conventional methods?To test the reliability of the TDT, we have used it to derive a scaling law for steady-state deformations. Such scaling laws have been proposed in the past. For example, Kocks and Mecking (14) devoted much of their review article to the search for scaling relations based on experimental data and phenomenological strain-hardening formulas. The TDT-based scaling relation, however, is derived directly from first principles. As we shall show, it is accurately obeyed over a wide-enough range of experimental data to make it seem highly unlikely that there is anything fundamentally incorrect about its underlying assumptions. Our increased confidence in the TDT now leads us to raise some issues that urgently need to be addressed for both basic and applied reasons.The thermodynamic basis of the TDT has been presented in earlier publications (especially refs. 3, 7, and 9). Its main premise is that the dislocations in a deforming crystalline solid can be described—indeed, must be described—by an effective temperature Te f f that differs greatly from the ordinary, ambient temperature T. Te f f is truly a “temperature” in the conventional sense of that word; it is derived by invoking the second law of thermodynamics. It is also a true temperature in the sense that, as energy flows through an externally driven system containing dislocations, effective heat is converted to ordinary heat and dissipated. Thus, this driven, nonequilibrium system should be visualized as consisting of two weakly coupled subsystems: the dislocations at temperature Te f f and the rest of the system playing the role of a thermal reservoir at temperature T.For present purposes, we need to know only that, in steady-state shear flow, the areal density of dislocations is given by the usual Boltzmann formula:ρss=1a2 exp −eDkB Te f fss,[1]where a is a minimum spacing between dislocations, eD is a characteristic dislocation energy, and Te f fss is the steady-state effective temperature. The quantity kB Te f fss/eD, usually denoted by the symbol χ~ss, is a measure of the degree of disorder of the subsystem of dislocations. It is determined by the rate at which this subsystem is being driven at the strain rate ϵ˙. If that driving rate is slow enough that irreversible atomic rearrangements have time to relax before the strain has changed appreciably, then χ~ss must be independent of the strain rate. Typical timescales for atomic motions are of the order of 10−10s. Thus, χ~ss must be a constant for strain rates up to 106/s or even higher. It then follows from Eq. 1 that steady-state dislocation densities must also be constant across this range of driving rates, which includes most ordinary applications. In ref. 3, we used a Lindemann-like argument to estimate that χ~ss∼0.25, which turns out to be roughly correct.The second core ingredient of the TDT is the depinning (“double-exponential”) formula, which also is based on a comparison of timescales. We know that the dislocations in a deforming solid, under a wide range of circumstances, are locked together in an entangled mesh that can deform only via thermally activated depinning of pairwise junctions. The pinning times are very much longer than the times taken by dislocation segments to jump from one pinning site to another. Thus, the depinning rate controls the deformation rate, and no other rates are relevant in this approximation.To be more specific, define the depinning rate to be τP−1=τ0−1 exp[−UP(σ)/kB T], where τ0 is a microscopic timescale and UP is a pinning energy that depends on the applied stress σ. Write this energy in the form UP(σ)=kBTP exp[−σ/σT(ρ,T)], where σT(ρ,T) is a characteristic stress that determines the magnitude of σ necessary to reduce the pinning barrier by a factor of 1/e. If a′ is the separation between dislocations needed to produce this reduction and 1/ρ is the average distance between dislocations, then a′ ρ is a strain, and σT(ρ,T)=μ(T) a′ ρ is a stress, where μ(T) is the (temperature-dependent) shear modulus. In fact, σT(ρ,T) is the Taylor stress. To compute the plastic strain rate, use the Orowan formula ϵ˙pl=ρ b v, where b is the magnitude of the Burgers vector and v is the average dislocation speed 1/(τP ρ). The result isϵ˙pl=bτ0ρ exp−TPTe−σ/σT(ρ,T)[2]or equivalently,σσT(ρ,T)=−ln TTPlnϵ˙0(ρ)ϵ˙pl,[3]where ϵ˙0(ρ)≡b ρ/τ0. This is the scaling relation.For steady-state situations in which ρ=ρss remains constant, Eq. 3 contains three system-dependent but theoretically strain rate-independent parameters: σTss≡σT(ρss,T), ϵ˙0ss≡ϵ˙0(ρss), and TP. Thus, plots of measured values of σ/σTss as functions of (T/TP) ln(ϵ˙0ss/ϵ˙pl) should collapse onto a single curve after we have identified the values of those three parameters, which we can do by using known values of the modulus μ(T) and using a least-squares method to find the best fit between the parameters and the scaling curve.To check this scaling prediction, we have used a set of compression measurements by Samanta (15). These are old results, but they have the special advantage for us of using two different materials and testing them at different temperatures and strain rates under otherwise identical conditions. Our scaling graph shown in Fig. 1 contains 32 points: 12 for pure copper at three temperatures in the range (600○C to 900○C) and four strain rates (960 /s to 2,300/ s) and 20 for pure aluminum at four temperatures in the range (250○C to 550○C) and five strain rates (520 /s to 2,200 /s). Clearly, these points fall very accurately on the smooth curve predicted by the TDT analysis, which adds greatly to our confidence in this theory. The most physically interesting fitting parameters are TP=45,000 K for copper and TP=27,800 K for aluminum, which differ somewhat from previous estimates, possibly because of differing sample preparations or measurement techniques.Open in a separate windowFig. 1.Scaling relation given by Eq. 3. The solid curve is the function f(x)=ln(1/x), with x=(T/TP) ln(ϵ˙0ss/ϵ˙pl). The data points are from ref. 15 as interpreted in ref. 16.The time-dependent TDT consists of three physics-based equations of motion. The first is Hooke’s law with the (“hypo-elasto-plastic”) assumption that elastic and plastic shear rates are additive:σ˙=2 μ(1+ν) (ϵ˙tot−ϵ˙pl),[4]where ν is Poisson’s ratio and ϵ˙tot is the total elastic plus plastic strain rate. ϵ˙pl is given by Eq. 2, making this a highly nonlinear equation.Second is an equation of motion for ρ, which is a statement of energy conservation:ρ˙=κρ σ ϵ˙plγD 1−ρρss(χ~).[5]Here, γD is the dislocation energy per unit length, and κρ is the fraction of the input power σ ϵ˙pl that is converted into dislocations. The second term inside the square brackets determines the rate at which dislocations are annihilated. It does this by invoking a detailed balance approximation using the effective temperature χ~; that is, it says that the density ρ must approach the value given by Eq. 1 but with the steady-state χ~ss replaced by a time-dependent χ~ during the approach to steady-state deformation.Finally, the equation of motion for χ~ is a statement of the first law of thermodynamics:ce f f eD χ~˙=σ ϵ˙pl 1−χ~χ~ss−γD ρ˙,[6]where ce f f is the effective specific heat. The second term in the parentheses is proportional to the rate at which effective heat is converted to ordinary heat, which reminds us that χ~ is a thermodynamically well-defined temperature. Like the comparable term in Eq. 5, this is a detailed balance approximation. The last term on the right-hand side accounts for energy stored in the form of dislocations.To illustrate the solutions of these equations of motion, we show in Fig. 2 just 2 of Samanta’s 32 stress–strain datasets, compared here with the TDT predictions. The agreement between theory and experiment shown here is reassuringly excellent. Ref. 16 has details about how the TDT equations were reformulated for numerical purposes and how parameter values were chosen for comparing their predictions with the experiments. In computing the curves shown in Fig. 2, we simplified the analysis by neglecting Eq. 6 for χ~ and simply solving Eq. 5 with χ~=χ~ss=0.23, consistent with our observation in ref. 3 that χ~→χ~ss very rapidly at high temperatures T. Our measured value of χ~ss is roughly consistent with our original guess that χ~ss∼0.25. The graphs in Fig. 2 are almost identical to those shown for wider ranges of temperatures and strain rates in the early TDT papers. They also illustrate the invariance of the onset slopes for non-prehardened copper discovered experimentally by Kocks and Mecking (14) and explained theoretically in refs. 3 and 7.Open in a separate windowFig. 2.Strain hardening curves for Cu: T=1,023 K, ϵ˙=1,800/s (upper blue) and T=1,173 K, ϵ˙=960/s (lower red). The data points are from ref. 15.We emphasize that these equations of motion are based entirely on fundamental principles—the laws of thermodynamics, energy conservation, and dimensional analysis. Specific phenomena such as hardening, grain-size effects, or yielding transitions play no role in deriving them. Those phenomena are predicted by the equations. The associated physical mechanisms are contained in the derivation of the double-exponential depinning formula, Eq. 2, and in the conversion factors κρ and ce f f in Eqs. 5 and 6. For example, the extreme stress sensitivity of the strain rate in Eq. 2 naturally explains the ρ and T dependences of yield stresses; the phenomenological concept of a “yield surface” is unnecessary. In a more specific way, the physically understandable grain-size dependence of the conversion factor κρ in Eq. 5 provides a simple explanation of Hall–Petch effects. Both of these predictions are discussed in ref. 7.One of the most remarkable aspects of these results is how many ingredients of conventional dislocation theories are completely absent in this elementary version of the theory. The TDT dislocations are simply lines. We do not ask whether they are edge dislocations or screw dislocations or whether they are excess dislocations or geometrically necessary ones. There are no partial dislocations. The crystals through which they move have no specific symmetries. Their motions are unaffected by crystalline orientations, slip planes, or stacking faults. They do not undergo cross-slip. They interact with each other only at the pinning junctions and not via long-ranged elastic forces.Apparently, we can go remarkably far with only this TDT caricature, but there must be limits. Finding and understanding those limits should be a high priority for new investigations. After we see what important physics is missing, we should be able to put realistic features back into the theory in fundamentally consistent ways and thereby understand what roles they play and how important those roles may be.This process of making the TDT more realistic should help us distinguish useful phenomenological concepts from those that are unrealistic. Our candidates for the “unrealistic” category include distinctions between “mobile” and “immobile” dislocations, distinctions between different “stages” of strain hardening, and the idea that large flow stresses at high strain rates can be explained by something called “phonon drag.” At present, we see no scientific basis for any of those conventional ideas, but surely we are mistaken in some cases. We expect to learn a great deal by finding clear counterexamples or missing ingredients in the TDT.Similarly, there must be many limits to the validity of our scaling analysis. Here is an already obvious one. We have pointed out that the assumption of constant χ~ss, and thus, constant ρss, must be changed at physically plausible, high strain rates. Already, in ref. 3, we showed how a simple strain rate dependence of χ~ss with a corresponding increase in ρss can explain the high stresses observed in strong-shock experiments. We thus found agreement between TDT and experiment over 15 decades of strain rate. This kind of analysis of high strain rates was also applied in ref. 8 to interpret molecular dynamics simulations of crystalline deformation (17). We consider those simulations to have been especially important in the development of the present theory.There are many other open issues, but most of them seem to be minor technicalities in comparison with a far more important question: what is the physics of brittle and ductile fracture in crystalline solids? Basic theoretical research in this area has been at a decades-long standstill comparable with that which has afflicted theories of strain hardening.Consider the following. We know that solids are stronger when they are colder; their yield stresses and flow stresses decrease with increasing temperature. This behavior is now predicted by the TDT as seen in Eq. 2 and its applications. However, we also know that solids become more brittle (i.e., they break more easily) at lower temperatures despite the fact that they are stronger. How can these properties be consistent with each other?This basic question has not been answered. So far as we know, it is seldom even asked in the solid mechanics literature. The conventional model used for studying brittle or ductile crack initiation is one in which dislocations are emitted from infinitely sharp crack tips and move out along well-defined slip planes (18, 19). These dislocations either move freely, supposedly implying brittle behavior, or become dense enough to shield the crack tip and somehow produce ductility and toughness. Agreement with experiment is modest at best. As stated in a recent experimental paper by Ast et al. (20), an “understanding of the controlling deformation mechanism is still lacking.” Finding a predictive theory of fracture toughness in crystalline solids should now be feasible and should be a high priority for materials theorists.     

Oxidation-Induced Destabilization of Model Antibody-Drug Conjugates

Oxidation of biopharmaceutics represents a major degradation pathway, which may impact bioactivity, serum half-life, and colloidal stability. This study focused on the quantification of oxidation and its effects on structure and colloidal stability for a model antibody and its lysine (ADC-L) and cysteine (ADC-C) conjugates. The effects of oxidation were evaluated by a forced degradation study using H₂O₂ and a shelf-life simulation, which used degrading polysorbate 80 as source for reactive oxygen species. Differential scanning fluorimetry revealed decreasing transition temperatures of the CH₂ domain with rising oxidation, resulting in a loss of colloidal stability as assessed by size-exclusion high pressure liquid chromatography. The conjugation technique influences structural changes of the monoclonal antibody (mAb) and subsequently alters the impact of oxidation. ADC-C was most effected by oxidation as the CH₂ domain showed the biggest destabilization on conjugation compared to the mAb and ADC-L. Quantification of Fc methionine oxidation by analytical protein A chromatography revealed 4-fold higher oxidation after 8 weeks for the ADC-C compared to the mAb. Payload degradation was observed independently of the conjugation technique used or if free in solution by ultraviolet-visible. In addition, adding antioxidants can be a suitable approach to prevent oxidation and achieve baseline stabilization of the proteins.     

Comparison of the isomerization mechanisms of human melanopsin and invertebrate and vertebrate rhodopsins

Comparative modeling and ab initio multiconfigurational quantum chemistry are combined to investigate the reactivity of the human nonvisual photoreceptor melanopsin. It is found that both the thermal and photochemical isomerization of the melanopsin 11-cis retinal chromophore occur via a space-saving mechanism involving the unidirectional, counterclockwise twisting of the =C11H-C12H= moiety with respect to its Lys340-linked frame as proposed by Warshel for visual pigments [Warshel A (1976) Nature 260(5553):679–683]. A comparison with the mechanisms documented for vertebrate (bovine) and invertebrate (squid) visual photoreceptors shows that such a mechanism is not affected by the diversity of the three chromophore cavities. Despite such invariance, trajectory computations indicate that although all receptors display less than 100 fs excited state dynamics, human melanopsin decays from the excited state ∼40 fs earlier than bovine rhodopsin. Some diversity is also found in the energy barriers controlling thermal isomerization. Human melanopsin features the highest computed barrier which appears to be ∼2.5 kcal mol⁻¹ higher than that of bovine rhodopsin. When assuming the validity of both the reaction speed/quantum yield correlation discussed by Warshel, Mathies and coworkers [Weiss RM, Warshel A (1979) J Am Chem Soc 101:6131–6133; Schoenlein RW, Peteanu LA, Mathies RA, Shank CV (1991) Science 254(5030):412–415] and of a relationship between thermal isomerization rate and thermal activation of the photocycle, melanopsin turns out to be a highly sensitive pigment consistent with the low number of melanopsin-containing cells found in the retina and with the extraretina location of melanopsin in nonmammalian vertebrates.For a long time it was assumed that the human retina contains only two types of photoreceptor cells: the rods and cones responsible for dim-light and daylight vision, respectively. However, recent studies have revealed the existence of a small number of intrinsically photosensitive retinal ganglion cells (ipRGCs) that regulate nonvisual photoresponses (1). ipRGCs express an atypical opsin-like protein named melanopsin (2, 3) which plays a role in the regulation of unconscious visual reflexes and in the synchronization of endogenous physiological responses to the dawn/dusk cycle (circadian rhythms) (4, 5).Melanopsins are unique among vertebrate photoreceptors because their amino acid sequence shares greater similarity to invertebrate than vertebrate rhodopsin (i.e., the photoreceptor of rods) (6, 7). Like rhodopsins, melanopsins feature an up–down bundle architecture of seven transmembrane α-helices incorporating the 11-cis isomer of retinal as a covalently bound protonated Schiff base (PSB11 in Fig. 1A). Light-induced (i.e., photochemical) isomerization of PSB11 to its all-trans isomer (PSBAT) triggers an opsin conformational change that, ultimately, activates the receptor and signaling cascade (8, 9). However, similar to invertebrate and in contrast to vertebrate rhodopsins, melanopsins are bistable (10). Indeed, although vertebrate rhodopsins need a retinoid cycle (11) to regenerate PSB11, melanopsins have an intrinsic light-driven chromophore regeneration function via PSBAT back-isomerization. Furthermore, past studies have shown that melanopsins use an invertebrate-like signal transduction cascade (12).Open in a separate windowFig. 1.PSB11 chromophore reactivity. (A) Chromophore structure and isomerization to PSBAT. (B) Schematic representation of the photochemical (full arrows) and thermal (dashed arrows) isomerization paths. The CI is located energetically above the TS, features a different geometrical structure, and drives a far-from-equilibrium process. ΔE_S1-S0, τ_cis→trans, and Ea^T (in red) are the fundamental quantities computed in the present work.Melanopsins are held responsible for photoentrainment, using the changes of irradiance and spectral composition to adjust the circadian rhythm (13). The different studies carried out so far on melanopsin light sensitivity do not lead to consistent results. Although Do et al. (14) argue that ipRGCs work at extremely low irradiation intensities showing a single-photon response larger than rods, Ferrer et al. (15) conclude that the melanopsin has a reduced sensitivity relative to visual pigments. On the other hand, these photoreceptors would be expected to display high light sensitivity (14). In the vertebrate retina their density is 10⁴ times lower than that of rhodopsins. Moreover, the receptor is not confined in a dedicated cellular domain such as the outer segment of rods and cones, resulting in a ipRGCs photon capture more than 10⁶-fold lower than that of rods and cones per unit of retina illumination. A high sensitivity of melanopsins would also be consistent with their presence in extraretina locations such as in pineal complex, deep brain, and derma of nonmammalian vertebrates (e.g., amphibian) (16–18). The amount of light that can penetrate into such regions is limited and enriched in the red component due to light scattering by the surrounding tissues (14).The molecular-level understanding of the primary light response of melanopsin is a prerequisite for the comprehension of more complex properties such as its activation and sensitivity. Despite numerous studies carried out since its discovery (16), there is presently little information on the molecular mechanism of melanopsin activation. The common PSB11 chromophore of melanopsins and rhodopsins does not guarantee that the same mechanism operates in both photoreceptors. This not only concerns light-induced activation but also thermal activation: a process whose rate limits the photoreceptor light sensitivity and that is currently associated with thermal, rather than photochemical, PSB11 isomerization (19–24).The mechanism of light-induced PSB11 isomerization in vertebrate rhodopsins has been extensively investigated. Spectroscopic studies have shown that in bovine rhodopsin (Rh) the isomerization occurs on a subpicosecond timescale (25–27). Moreover, the observation of ground state (S₀) vibrational coherence (28) is consistent with a direct transfer of the excited state (S₁) population to the photoproduct (Fig. 1B) passing through a conical intersection (CI). Such a path has been located along the S₁ potential energy surface by constructing a multiconfigurational quantum chemistry (MCQC) based computer model of the photoreceptor (29–31) and spectroscopically supported by probing in the infrared (31). More recently (32), the same computer model has been used to map the Rh thermal isomerization path (Fig. 1B) providing information on the transition states controlling the reaction.Here we present a computational study focusing on the mechanism of photochemical and thermal isomerization of human melanopsin (hMeOp). This would require the construction of a computer model of hMeOp starting from the receptor crystal structure. However, the lack of hMeOp crystallographic data does not allow the use of the protocol previously applied in Rh studies. The significant sequence similarity between squid rhodopsin (sqRh), whose crystal structure is available (PDB code: 2Z73) (33), and hMeOp (40%, SI Appendix, Fig. S1) provides the fundamentals for constructing a structural model of hMeOp at a significant atomic resolution. Building on a study by Batista and coworkers (34) on murine melanopsin, we combine comparative modeling of hMeOp with MCQC to construct a quantum mechanics/molecular mechanics (QM/MM) computer model capable of simulating the photochemical and thermal isomerization reactions of hMeOp. The results are then compared with those found using Rh and sqRh models constructed using the same protocol. Such a comparison is expected to provide information on the differences in spectral and functional properties of these evolutionary distant pigments. As we will show below, the models indicate that hMeOp has a faster photochemical isomerization dynamics and a higher thermal isomerization barrier than both Rh and sqRh.     

Ablation of Breast Cancer Cells Using Trastuzumab‐Functionalized Multi‐Walled Carbon Nanotubes and Trastuzumab‐Diphtheria Toxin Conjugate

Trastuzumab (Herceptin^®) is a monoclonal antibody (mAb) for specific ablation of HER2‐overexpressing malignant breast cancer cells. Intensification of antiproliferative activity of trastuzumab through construction of immunotoxins and nano‐immunoconjugates is a promising approach for treatment of cancer. In this study, trastuzumab was directly conjugated to diphtheria toxin (DT). Also, conjugates of trastuzumab and multiwalled carbon nanotubes (MWCNT) were constructed by covalent immobilization of trastuzumab onto MWCNTs. Then, antiproliferative activity of the fusion constructs against HER2‐overexpressing SK‐BR‐3 and also HER2‐negative MCF‐7 cancer cell lines were examined. Cells treated with trastuzumab‐MWCNT conjugates were irradiated with near‐infrared (NIR) light. Efficient absorption of NIR radiation and its conversion to heat by MWCNTs can be resulted to thermal ablation of cancerous cells. Our results strongly showed that both trastuzumab‐MWCNT and trastuzumab‐DT conjugates were significantly efficient in the specific killing of SK‐BR‐3 cells. Targeting of MWCNTs to cancerous cells using trastuzumab followed by exposure of cells to NIR radiation was more efficient in repression of cell proliferation than treatment for cancer cells with trastuzumab‐DT. Our results also showed that conjugation linkers can significantly affect the cytotoxicity of MWCNT‐immunoconjugates. In conclusion, our data demonstrated that trastuzumab‐MWCNT is a promising nano‐immunoconjugate for killing of HER2‐overexpressing cancerous cells.     

The Promising Future of Fluoropolymers

This article aims at showing the usefulness of fluoropolymers (FPs), supplying an overview of their synthesis, applications, and recycling. FPs are currently prepared by conventional radical polymerization of fluoromonomers. These specialty polymers, produced in low tonnage compared to that of commodity ones, display outstanding properties, such as chemical, oxidative, and thermal resistances, low refractive index, dissipation factor, permittivity, and water absorptivity, and excellent weatherability and durability. More recent routes for their preparations are suggested, controlled or not, leading to random, alternated, block, graft, dendrimers, or multiarm copolymers, as well as their applications ranging from coatings to high performance (thermoplastic) elastomers, energy related‐materials (e.g., fuel cell membranes, components for lithium‐ion batteries, electroactive devices, and photovoltaics) to original and surfactants, optical devices, organic electronics, composites, and shape memory polymers.     

Temperature-influenced dimensional change of different molar anatomical areas

ObjectiveTo analyse the linear coefficient of thermal expansion (LCTE) of different tooth regions using thermal mechanical analysis (TMA).MethodsSpecimens (n = 12) were sectioned from different anatomical areas from recently extracted molars using a slow-speed diamond saw. During analysis the specimens were kept saturated with phosphate-buffered saline using a specially designed quartz container that was placed inside the TMA unit. Specimens were subjected to a 15–50 °C heating cycle as well as a 50–15 °C cooling cycle at a 5 °C/min rate. LCTE was determined using the slope of each respective cycle with each specimen being run three times with the mean representing the LCTE of each specimen. Mean results between heating and cooling for each sample were compared with paired t-test while results between regions were compared with ANOVA and Tukey post hoc (p = 0.05).ResultsSignificant differences in LCTE were noted between tooth regions with caries-affected dentine, cervical, and root surfaces exhibited significantly lower LCTE. Furthermore, cooling LCTE was significantly greater than heating in all areas.ConclusionsUnder the conditions of this study, molar LCTE was found not to be uniform in all areas. Furthermore, cooling LCTE was found to be greater than heating.