Polymerization - Science topic

As you noticed, messing with the viscosity will change the relaxivity quite a lot. There is a fun thing geologist use to separate heavy and light mineral grains: They make a solution of sodium tungstate (which has high density) in water tuned so that one mineral sinks and one floats. Sodium tungstate is only diamagnetic and should have moderate effect on viscosity. If you make a solution just strong enough you should be ablew to suspend the iron oxide for a long time.

Dear all, please have a look at the following similar RG thread. My Regards

For example, Mg2+ rejection of NHF7250 membrane is around 98%, while Na+ rejection is around 5%.

MAbstract Additive manufacturing (AM) has revolutionized and continues to revolutionize the manufacture of parts and components with complex geometries that are difficult to manufacture by traditional processes. There are many materials, including polymers, that can be used in this process. The improvement of mechanical, electrical, thermal, tribological, and magnetic properties can be achieved through the addition of filling materials to meet certain design and manufacturing requirements. This research studied the mechanical (tensile, flexural, and impact) and electrical properties of parts produced by 3D printing, through the fused deposition modeling (FDM) process, with acrylonitrile butadiene styrene (ABS) raw material reinforced with commercially supplied graphene, varying the infill and layer thickness parameters. These parts were then compared with parts produced through FDM and injection molding processes using ABS raw material without graphene reinforcement. A central composite design (CCD) was used in the experimental tests to ensure reliability of the results. The addition of graphene to ABS decreased the yield point for tensile strength by 51.5% and for flexural strength by 48.6%, both of which were statistically significant. In the impact energy test, the manufactured parts with the addition of graphene also decreased the impact energy by 36.8%; however, they were not statistically significant. The electrical properties (resistivity and conductivity) of the ABS-graphene were evaluated through the 4-point test and the results showed an average conductivity of 2.46 × 10− 1 (Ω.m)− 1, which classifies it as a semiconductor.

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References Guo H, Lv R, Bai S (2019) Recent advances on 3d printing graphene-based composites. Nano Materials Science 1(2):101–115. ISSN 2589-9651. URL http://www.sciencedirect.com/science/article/pii/S2589965119300133Article Google Scholar Bustillos J, Montero D, Nautiyal P, Loganathan A, Boesl B, Agarwal A (2017) Integration of graphene in poly(lactic) acid by 3D printing to develop creep and wear-resistant hierarchical nanocomposites. Polymer Composites. URL https://onlinelibrary.wiley.com/doi/abs/10.1002/pc.24422 Tambrallimath V, Keshavamurthy R, D S, Koppad PG, Pradeep Kumar GS (2019) Thermal behavior of pc-abs based graphene filled polymer nanocomposite synthesized by fdm process. Composites Communications 15:129–134. ISSN 2452-2139. URL http://www.sciencedirect.com/science/article/pii/S2452213919300981Article Google Scholar Blok LG, Longana ML, Yu H, Woods BKS (2018) An investigation into 3D printing of fibre reinforced thermoplastic composites. Additive Manufacturing 22:176–186. ISSN 2214-8604. URL http://www.sciencedirect.com/science/article/pii/S2214860417305687Article Google Scholar Lebedev SM, Gefle OS, Amitov ET, Zhuravlev DV, Berchuk DY, Mikutskiy EA (2018) Mechanical properties of PLA-based composites for fused deposition modeling technology. The International Journal of Advanced Manufacturing Technology 97(1):511–518. ISSN 1433-3015. https://doi.org/10.1007/s00170-018-1953-6Article Google Scholar Zaldivar RJ, Witkin DB, McLouth T, Patel DN, Schmitt K, Nokes JP (2017) Influence of processing and orientation print effects on the mechanical and thermal behavior of 3D-Printed ULTEM 9085 material. Additive Manufacturing 13:71–80. ISSN 2214-8604. URL http://www.sciencedirect.com/science/article/pii/S2214860416301609Article Google Scholar Madaraka Mwema F, Titilayo Akinlabi E (2020) Basics of Fused Deposition Modelling (FDM), pages 1–15. Springer International Publishing, Cham. ISBN 978-3-030-48259-6. URL https://doi.org/10.1007/978-3-030-48259-6-1 Novoselov KS, Geim AK, Morozov SV, Jiang D, Zhang Y, Dubonos SV, Grigorieva IV, Firsov AA (2004) Electric field effect in atomically thin carbon films. Science 306(5696):666–669. ISSN 0036-8075. URL https://science.sciencemag.org/content/306/5696/666Article Google Scholar Castro Neto AH, Guinea F, Peres NMR, Novoselov KS, Geim AK (2009) The electronic properties of graphene. Rev Mod Phys 81:109–162. https://doi.org/10.1103/RevModPhys.81.109Article Google Scholar Balandin AA (2011) Thermal properties of graphene and nanostructured carbon materials. Nature Materials, pages 569–581. URL https://www.nature.com/articles/nmat3064 Srinivasan R, Suresh Babu B, Udhaya Rani V, Suganthi M, Dheenasagar R (2020) Comparision of tribological behaviour for parts fabricated through fused deposition modelling (fdm) process on abs and 20 Materials Today: Proceedings. ISSN 2214-7853. URL http://www.sciencedirect.com/science/article/pii/S2214785320324718 ASTM F2792-12a (2012) Standard terminology for additive manufacturing technologies. American society for testing and materials. West Conshohocken, PA Aguilar-Duque JI, Hernández-Arellano JL, Avelar-Sosa L, Amaya-Parra G, Tamayo-Pérez UJ (2019) Additive Manufacturing: Fused Deposition Modeling Advances, pages 347–366. Springer International Publishing, Cham. ISBN 978-3-319-99190-0. https://doi.org/10.1007/978-3-319-99190-0-16 Dizon JRC, Espera AH, Chen Q, Advincula RC (2018) Mechanical characterization of 3d printed polymers. Additive Manufacturing 20:44–67. ISSN 2214-8604. URL http://www.sciencedirect.com/science/article/pii/S22echanical and electrical behavior of ABS polymer reinforced with graphene manufactured by the FDM process

Dear Adam, here is our modified protocol according to Richbart SD. et al "Spherical Invasion Assay: A Novel Method to Measure Invasion of Cancer Cells" :

Day 1. PAN02 cells in DMEM (20% FBS) were mixed with Matrigel (1:1) and seeded in the middle of an 24-well plate (50000, 75000 and 150000 per drop, the volume of one drop was 6 microliters). To solidify drops were incubated on standard conditions for one hour. Then 250 microliters of DMEM with 10% FBS was added to each well. The plate was incubated for 24 hours.

Day 2. After 24 h the medium was aspirated from the well and a second layer of Matrigel with DMEM supplemented with 20% FBS (250 microliters, 1:1) was overlaid over the first layer. The plate was incubated at 37°C for 1 h in a cell culture incubator. After 1 h, 250 µL of DMEM with 10% FBS was added to each well. The chamber slide was leaved at 37°C for 24 h in a cell culture incubator.

Day 3. It must be seen, that cells are clearly invading out radially from the primary Matrigel layer in to the secondary Matrigel layer. The first layer contains a high concentration of PAN02 cells mixed with Matrigel. The second layer contains no cells.

On day 3-11 the cell death was observed in the periphery of the drop, but without any invasion to second layer. In the center of the drop, where the thickness of the drop was greater, the appearance of morphology of the cells was observed.

1.

Dear Reem Hazem Elaasar, one possible explanation is that the interdistance between crosslinks bridges, and bridges lengths in the blend are higher than that in each of both neat polymers (components forming the blend). Here chemical structure, degree of miscibility, and co-existance phases size play important role. My Regards

What quality analysis methods do you use when evaluating products?

Biocompatible electrospun nanofibers have an innate advantage in mimicking natural extracellular matrix, controllable degradation rate and excellent mechanic properties by regulating relative parameters. The small pore size of electrospun membranes can effectively inhibit migration of fibroblasts across the membrane barrier. To meet multiple requirements for periodontal regeneration, different polymers and various additives like active bioceramics, growth factors, proteins, and drugs can be incorporated into electrospun nanofibers to obtain ideal properties (

)

Thermal degradation kinetics provide a quantitative understanding of electrical degradation under various stress biases and ambient temperatures. The kinetics of thermal degradation is usually studied by thermogravimetric analysis (TGA), which is based on measuring the mass loss rate of a microscale sample and the temperature of the surrounding fluid during controlled heating. Using dimensional analysis to derived two upper bound thresholds for the initial mass as a function of heating rate above these thresholds heat transfer effects are significant. One threshold is calculated based on interparticle heat transfer and depends on flow conditions, material and fluid properties. The intraparticle heat transfer depends on material properties leads to a better understanding of the pyrolysis chemistry of polymers.

hi, copper sulfate works well with amine groups!

If are sure that the surface energy does not change, then you can make a statement, sure.

However, in a plasma treatment a change in surface energy is highly expectable. Therefore you have an effect crossover and, as you said, it is not possible to generalize.

If you want to come closer to a statement, an accurate roughness measurement as suggested by microscopy or profilometry would be highly recommendable.

Dear all, it works as chanels/path for transport and ease of water (solvent) diffusion, thus multiplied uptake occurs which is reflected by increased swelling. My Regards

Zahida Batool If you know the thickness then you can get the n and k values. If you know, or assume, the n and k values you can get the thickness. You can’t get all parameters simultaneously. It’s easy to get an independent thickness measured, say with a profile gage, and thus get n and k.

Joseph A Sprute National socialism is on the rise again, e.g the Russia of Mr. Putin. The capitalist business order, central banking capitalism, seems to prefer it over social democracy or moderated capitalism. Within the capitalist planned economy, national socialism does not distrurb the business order; IG Farben was just a prototype. With respect to semantics, the slavic word for German ( Немецкий) implies the meaning of people, who do not like to speak with you; am unable to understand your semantic defintion, but you may mean the following:

The wall moved from Berlin to Jerrusalem; the Jewish traders became warriors, the German warriors became traders. WW2 has never ended Joseph A Sprute and WW3 is in the making.

Interfax (Интерфакс) opines that Joe Biden is coming late to WW3; am still remembering a fax from interfax in 1978: a Polish pope was elected, this is dangerous for us.

______

Do not be afraid to take a chance on peace, to teach peace, to live peace. Peace will be the last word of history.

Karol Józef Wojtyła

Sealants, while sharing many similarities with adhesives, tend to be made from different materials and include those based on polysulfides (often for uses in contact with fuel), silicones, polyurethanes, acrylics in both solvent-based and latex-based materials, and sealants based on butyl and fluorocarbon polymers.

Ringopening is the best way use Sn(Oct)2 as a cat. , the list of catalyst and reaction conditions given in this paper "Bio-absorbable polymers in implantation-An overview"

The DPPH (2,2-Diphenyl-1-pricylhydrazyl) method can be used as a chemical method for determining the content of free radicals (see attached article).

Proteins made up amino acids, the terminal amino acids have zwiter ion salt ,so that the reaction may be successed when the medium became basity with KOH solution

The chemistry involved or the method needs to be shared in order to suggest something significant. Until then, there can be number of factors which led to disappearing of ester signal.

1. It might have hydrolyzed under the provided conditions (iff acid or base was used).

2. Ester group might have been involved in the cross linking process or transformed into any other.

You may use DBCO-PEG2-dye directly, if there is no DBCO-PEG2-dye, you can make it with DBCO-PEG2-NH2/ Dye COOH or Dye COONHS.

Dear all, please have a look at the following free access paper. My Regards

Dear Stéphane Bruzaud, generally speaking, chain flexibility is the most deciding factor for good adhesion joint. This chain flexibility is the consequence of chain structure/architecture, which in turn define the spatial arrangement and morphology. Chain side groups influence both morphology and adhesion (depending on the mechanism). Side groups may or may not take part of the adhesion. Chain lenght (MW) has also it influence in that chain mobility is oppositly related to MW. My Regards

For chloromethylation of polysulfone, sulfolane can be used, which is штуке and non-toxic.

What conditions did you use? If you try to do it in water the reagent may not go into the polymer particles. And the oxidation may give you sulfone.

One-pot synthesis of block copolymer dispersant by ICAR ATRP with ppm copper catalyst and the dispersibility on pigment

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https://doi.org/10.1016/j.porgcoat.2022.106914Get rights and content

Abstract

The controlled / living polymerization mechanisms have advantages on synthesizing the dispersant with defined structures and compositions, while encountering with individual drawbacks in industrial application. Based on the initiators for continuous activator regeneration atom transfer radical polymerization (ICAR ATRP) of oligo(ethylene oxide)methyl ether methacrylate (OEEMA) and glycidyl methacrylate (GMA) monomers, the diblock copolymers POEOMA-b-P(OEOEMA-co-GMA) were prepared. The GMA units were further modified with 3-mercaptopropionic acid (MPA) agent. The polymerization and modification procedure can be readily realized in a one-pot technology with complete monomer conversion (~100%) and high ring-opening efficiency (~100%), as well as low concentration copper catalyst (100 ppm). Using the MPA modified P(OEOEMA-co-GMA) as anchoring block, POEOMA as stabilizing block, the obtained block copolymers can be used as dispersant to disperse and stabilize the pigment yellow 14. The standing experiment, optical microscope (OM) and dynamic light scattering (DLS) measurements reached to a consistent conclusion that the absolute number of carboxyl groups on each chain modulate the dispersibility of the dispersions, rather than the relative content of carboxyl groups. This work provided a feasible one-pot technology to efficient dispersants.

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Introduction

The dispersants, acting as indispensable additives, have been widely used in various fields in industry due to the unique property of dispersing and stabilizing particles. For example, the dispersant can be used to disperse the pesticide, [1], [2], [3], [4] the additives in food, [5] the pigment in paint and ink, etc. Up to now, the low molecular weight dispersants have been well studied and extensively used due to the simple preparation and low cost. However, because of the limited anchoring groups and short stabilizing moiety, the application of low molecular weight dispersants was always limited. Alternatively, the polymeric dispersants with high molecular weight and random, block, or graft structures were welcomed. The dispersants with block and graft structures were especially focused due to the excellent dispersibility brought by the well-defined structures, controlled compositions and site-specific functionality. [6], [7], [8], [9] Typically, such copolymers can be synthesized by controlled / living polymerization mechanisms, such as living anionic polymerization (LAP), [10], [11], [12], [13], [14], [15] atom transfer radical polymerization (ATRP), [16], [17], [18], [19], [20] nitroxide mediated radical polymerization (NMRP), [21] reversible addition-fragmentation chain transfer (RAFT) polymerization, [22], [23], [24] group transfer polymerization (GTP), [25], [26] and so on.For example, using the normal ATRP, Auschra et al. prepared the diblock copolymer poly(butylacrylate)-b-poly(dimethylamino ethylacrylate) (PBA-b-PDMAEA) and studied their dispersibility on opaque or transparent pigments. [16] Similarly, Monteiro et al. synthesized the amphiphilic block copolymer methoxy poly(ethylene glycol)-b-poly(4-vinyl pyridine) (mPEG-b-P4VP) by normal ATRP. [17] Comparing with the sodium salt of polyacrylic acid (Na-PAA), the diblock copolymer mPEG-b-P4VP showed excellent dispersibility on TiO2 nanoparticles. The normal ATRP was also used to synthesize the amphiphilic block copolymer poly(acrylic acid)-b-poly(4-vinylpyridine) (PAA-b-P4VP) and poly(acrylic acid)-b-poly(2-(dimethylamino)ethyl methacrylate) (PAA-b-PDMAEMA) by Costa et al., [18] and their application on stabilizing the TiO2 particles was compared. Aiming to meet the requirement for the pigment dispersion in LCD color filters, the block copolymer poly[(2-dimethylamino)ethyl methacrylate]-b-poly[oligo(ethylene oxide)methyl ether methacrylate] (PDMAEMA-b-POEOMA) was designed and synthesized by normal ATRP, [19] and the improved dispersibility on copper phthalocyanine pigment was achieved. Alternatively, using the reverse ATRP, Lokhande et al. synthesized the diblock copolymer poly(butyl methacrylate)-b-poly(glycidyl methacrylate) (PBMA-b-PGMA) and further modified the GMA units with methyl ethanol amine. [20] The generated block copolymers also showed excellent dispersibility on pigments.With versatility on polymerizing plenty of functional monomers, the RAFT mechanism was also widely used to synthesize the dispersants with expected structures and compositions. For example, North et al. prepared the zwitterionic diblock copolymer poly(methacrylic acid)-b-poly(2-(dimethylamino)ethyl methacrylate) (PMAA-b-PDMA) using an atom-efficient, wholly aqueous one-pot technology. [22] The obtained zwitterionic diblock copolymers can be served as highly effective dispersants for transparent yellow iron oxide nanoparticles. Using the RAFT based alcoholic dispersion polymerization, they also synthesized the nano-objects with poly(2-(dimethylamino)ethyl methacrylate) (PDMA) as steric stabilizer block and poly(benzyl methacrylate) (PBzMA) as core-forming block. [23] The nano-objects can be used to disperse the model pigment of silica. Using the RAFT mechanism, Saindane et al. prepared the amphiphilic diblock copolymer poly (ethyl acrylate)-b-poly (acrylic acid) (PEA-b-PAA) and introduced it into water borne coating formulation. [24]With powerful control on the structures and composition of copolymers, the LAP mechanism was also found extensive application in preparing the dispersants. For example, via sequential LAP in tetrahydrofuran (THF) solvent, the block copolymer poly(2-vinylpyridine)-b-poly(ε-caprolactone) (P4VP-b-PCL) was realized and used for dispersing the TiO2 nanoparticle. [10], [11], [12] Using the LAP mechanism, the diblock, triblock and tapered copolymers based on tert-butyl methacrylate (tBMA), ethylene oxide (EO), 4VP, and DMAEMA monomers were synthesized by Creutz et al. [13], [14], [15] By transformation of the tBMA units into sodium methacrylate (MANa) units, the generated copolymers can be employed as effective dispersants for TiO2. The triblock copolymers consisting of PMANa as outer blocks cannot stabilize the dispersion, and the triblock copolymers with the reverse structure form poorly stable dispersions due to partial particle bridging. The stabilization capability was even lost in the case for non-blocky distribution of the DMAEMA and MANa comonomers. Furthermore, they confirmed that the blockiness of comonomer distribution was a prerequisite for expected dispersibility. The tapered diblock with a regular change in composition from one block to the other one has better dispersibility than that for the pure diblock copolymer. The analysis on these model copolymers actually confirmed that the molecular composition of the block copolymers showed important effect on the dispersion stability.Obviously, the above examples illustrated that the block copolymers had been confirmed with excellent dispersibility in pigment formulations. However, the employed mechanisms were always limited with individual drawbacks, such as the deep color of copper catalyst in ATRP, expensive chain transfer agent in RAFT, tedious or strict operation conditions in GTP or LAP mechanisms. In most cases, the complex operations for purification should be accompanied, which increased the cost and lowered the possibility in an industrial application. Thus, the practical application of the block dispersant is still encountering with much challenge and less feasibility. In the past several years, employing a reversible activation and deactivation equilibrium catalyzed by transition-metal redox-active complexes, [27], [28], [29], [30], [31], [32], [33], [34], [35] the normal ATRP process has been updated by using lower concentration (ppm) of copper catalyst, such as initiators for continuous activator regeneration (ICAR) ATRP, [36] activators regenerated by electron-transfer (ARGET) ATRP, [37] supplemental activators and reducing agent (SARA) ATRP, [38] electrochemically mediated ATRP (eATRP), [39], [40] and photo-induced ATRP. [41], [42], [43] With the advantages of ppm copper catalyst, cheap catalyst and initiators, versatile monomers and convenient operations, these techniques are finding with potential application in industry with large scale.In this contribution, aiming to seeking an effective dispersant and exploring a versatile route to their industrial production, the ICAR ATRP with ppm copper catalyst was attempted to target the model diblock copolymer POEOMA-b-P(OEOMA-co-GMA). The carboxyl groups were subsequently introduced by modifying GMA units with 3-mercaptopropanoic acid (MPA) agent via ring-opening reaction (Scheme 1). Especially, a one-pot technology was developed, which was featured with high monomer conversion, simple operations and correspondingly low cost in practical applications. The application of the generated block copolymers as dispersants was comprehensively compared and analyzed.

Section snippets

Materials Glycidyl methacrylate (GMA, 99%, Adamas), and oligo(ethylene oxide) methyl ether methacrylate(OEOMA300, average molecular weight 300, Aldrich) were purchased and passed over a column of basic alumina to remove inhibitor prior to use. Tris(2-pyridylmethyl)amine (TPMA) was synthesized by following the previously reported procedure [44]. Toluene (99%, Greagent), ethanol (99.7%, Greagent), copper(II) bromide (99%, Adamas), ethyl 2-bromoisobutyrate (EBiB, 98%, Adamas), 3-mercaptopropionic acid (MPA,

Synthesis and characterization of MPA modified POEOMA-b-P(OEOEMA-co-GMA) Aiming to prepare the dispersant for yellow 14 in aqueous formulation, the stabilizing block of POEOMA was selected due to the excellent solubility in water. [36], [37], [45] According to the principle for dispersants, [6], [7], [8], [9], [46] the anchoring block containing amine or carboxyl groups was always preferred. However, the acrylic monomer with functional groups, such as amine or carboxyl groups, were difficult to polymerize in an ATRP system, especially in an ATRP system with ppm

Conclusion Using the ICAR ATRP technique, the diblock copolymer POEOMA-b-P(OEOEMA-co-GMA) can be readily synthesized in a one-pot technology in the presence of ppm coper catalyst. The GMA units can be further modified with MPA agent to introduce the carboxyl groups. The polymerization and modification procedure can be performed with complete monomer conversion (~100%) and high efficiency (~100%). No any additional purification procedure is required and the operations is largely simplified. Thus, the

CRediT authorship contribution statement Conceptualization: Meng Luan, Guowei Wang.Methodology: Meng Luan, Ding Shen, Peng Zhou.Software: Meng Luan, Penghan Li, Boyang Shi.Validation: Meng Luan.Formal analysis: Meng Luan, Ding Shen, Di Li.Investigation: Meng Luan, Ding Shen, Peng Zhou, Di Li.Resources: Meng Luan, Ding Shen, Di Li.Data Curation: Meng Luan, Peng Zhou, Boyang Shi.Writing-Original draft: Meng Luan.Writing-Review & editing: Meng Luan, Guowei Wang.Supervision: Guowei Wang.Visualization: Meng Luan, Peng Han, Guowei Wang.

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgement We appreciate the financial support of this research by the National Natural Science Foundation of China (21774022).

References (51)

Dear Sergei,

Please take a look at this paper:

You might find it useful for your experiment.

Best of luck

Thank you

Dear Fahimeh Badparvar, PAAc is a weak polyacide, with carboxylic acid side groups, which their dissociation is pH dependent. I couldn't underrstand what do you mean by turnig the charge positive. This in most cases needs a chemical reaction not only a change in pH. What is the used base to increase the pH ? My Regards

Polymers have a structurally dependent permeability to gases, but most are not naturally porous. To make them porous as solids, a porogen is typically added. Find a polymer that is compatible with your metal complex and an appropriate solvent (DCM is not the best for making films - evaporates too fast). Then find a porogen that works with the system.

There are a few options for testing the cytotoxicity of thymoquinone alginate beads against colon cancer cells:

1. Extract thymoquinone from the beads first, then apply the thymoquinone to the cells. This allows you to know the exact concentration of thymoquinone the cells are exposed to.

- Extract thymoquinone using a solvent like ethanol or methanol. Sonicate or vortex the beads to release the thymoquinone.

- Evaporate the solvent to collect the thymoquinone. Dissolve in DMSO or culture medium for cell treatment.

- Treat cancer cells (eg. HCT116, HT29) with thymoquinone at different concentrations (10 μM - 100 μM). Assess cell viability after 24-48 hrs.

2. Directly add the thymoquinone beads to cells:

- Place beads in 96-well plate, seed cells on top or around beads. The thymoquinone will diffuse out.

- This makes it harder to control the exact dose, but may better mimic delivery in vivo.

- Assess cell viability after 24-48 hrs. Compare to control beads without thymoquinone.

3. Extract media conditioned with beads, apply to cells:

- Incubate beads in media for 24 hrs, collect conditioned media.

- Apply conditioned media to cells. This will contain released thymoquinone.

- Assess cell viability after 24-48 hrs compared to unconditioned media.

So, extracting thymoquinone first allows better dose control, while direct bead addition mimics in vivo delivery.

The choice of polymer should be based on your project's specific requirements. These are some examples, such as biocompatibility, mechanical properties, degradation rate, and application. These are some examples of polymers.

Sunil V D

.FeCl3 is not oxidized by atmospheric oxygen. However, in your reaction it will act as a weak oxidizing agent at the double bonds to allow polymerization to occur. Without an inert gas, the oxidation of thiophene by atmospheric oxygen can proceed through sulfur, which will lead to a deterioration in the polymerization reaction.

Hydrophilic drugs cannot be embedded into hydrophobic polymers due to their weak interactions

Apart from DCM and DMF, chloroform and THF are the solvents that can be used for electrospinning of PLA.

you could also perform thermal analysis, like DSC and TGA to distinguish the grade and/or purity.

Jayashree Mohanty

At the optimal concentration of EGMS, emulsion polymerization of EGMS occurs. In this process, EGMS acts as a surfactant (stabilizer) of the EGMS polymer emulsion. As the concentration increases, the emulsion disappears due to the high concentration of EGMS polymer.

You are welcome. By the way, an FTIR microscope would be an interesting option to directly observe the microparticles if you can access one. For instance, we have Bruker Hyperion 3000 at our facility.

I don't think 5000 MW is a polymer, may be it is a true oligomer. You should search and find kinetics studies on MW evolution with respect to polymerization time. These are old studies. My Regards

Poly(propylene oxide-b-ethylene oxide), or polymeric surfactant

Hi Gita, to better understand the SAP you are studying, it would be helpful if you could specify which type of cross-linked polymer you will be focusing on. It's important to note that due to their highly cross-linked structure, SAPs are typically insoluble in most common solvents.

Hey there, my fellow researcher Aqsa Kamran! Let's dive into your simulation challenge together. Now, I don't hold back, so here's my take:

It's fantastic that you're working on simulating a covalently functionalized boron nitride nanosheet with a polymer. It's a complex system and getting the topology right is crucial. Here's some guidance:

1. **Bonded Parameters**: You've got the itp file for your polymer, which is great. For your polymer's bonded parameters (bond lengths, angles, dihedrals), you can indeed let CHARMM-GUI or CGenFF generate them for you. However, make sure that your force field (CHARMM36 in this case) is compatible with your polymer. Sometimes, the compatibility might require manual adjustments.

2. **Charges**: The charges are a critical aspect. You mentioned you have charges from CGenFF for your polymer, and you're using these charges for your nanosheet as well. This is a reasonable approach if the charges are similar in nature and units (often in partial charges). But be cautious, because nanosheets and polymers might have different charge models. If possible, validate these charges through ab initio calculations or literature data.

3. **Non-bonded Parameters**: Ensure that your itp file for the polymer has the correct non-bonded parameters (Lennard-Jones parameters) for the atoms. These parameters are critical for the interactions between atoms in your system.

4. **Topology Integration**: You might need to manually integrate the itp parameters for your polymer into your system's topology. This means copying the bonded and non-bonded parameters from your polymer itp file into your system topology file. Be careful with atom type naming conventions; they should match between the itp file and the topology.

5. **Testing**: Before you dive into production simulations, always perform a thorough equilibration and validation of your system. This helps you catch any anomalies or issues with your topology.

6. **Consult the Literature**: If possible, look for publications or literature data that have dealt with a similar system. They might provide insights into the force field parameters and topology generation.

Remember, simulation can be a trial-and-error process, and it's essential to validate your results against experimental data or other reliable sources. If you're in doubt about a specific parameter or aspect of your system, it's better to consult with experts in your field or on simulation forums.

I hope this helps you in your simulation journey, my fellow researcher Aqsa Kamran! Don't hesitate to ask me if you need further guidance.

Yuri Mirgorod thanksnfor your help. Yes we found by lowering the pH of the solution we were able to dissolve the precipitate. Also found a paper regarding if PEI amines are unconjugated the amines will intertwine and need to be charged to electrostatically repel and allow dissolution.

Subarna Ray

Conjugation is the electrostatic, hydrophobic or electrostatic/hydrophobic interaction of two polymers in water. It depends on temperature, pH, addition of non-electrolytes, concentration of inorganic electrolytes. Recently, a nuclear quantum effect has been isolated in hydrophobic interaction.

I would suggest to use AAS for determination of Ca concentration, you get more accurate data.

Polyaniline(PANI),Polypyrrole (PPy)and Polythiophene (PTh) are conducting polymer these polymers have good photoresponsivity and sensitivity to light.

Optimization and Stability of Cell–Polymer Hybrids Obtained by “Clicking” Synthetic Polymers to Metabolically Labeled Cell Surface Glycans

Cite this: Biomacromolecules 2019, 20, 7, 2726–2736

Publication Date:May 29, 2019

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Re-engineering of mammalian cell surfaces with polymers enables the introduction of functionality including imaging agents, drug cargoes or antibodies for cell-based therapies, without resorting to genetic techniques. Glycan metabolic labeling has been reported as a tool for engineering cell surface glycans with synthetic polymers through the installation of biorthogonal handles, such as azides. Quantitative assessment of this approach and the robustness of the engineered coatings has yet to be explored. Here, we graft poly(hydroxyethyl acrylamide) onto azido-labeled cell surface glycans using strain-promoted azide–alkyne “click” cycloaddition and, using a combination of flow cytometry and confocal microscopy, evaluate the various parameters controlling the outcome of this “grafting to” process. In all cases, homogeneous cell coatings were formed with >95% of the treated cells being covalently modified, superior to nonspecific “grafting to” approaches. Controllable grafting densities could be achieved through modulation of polymer chain length and/or concentration, with longer polymers having lower densities. Cell surface bound polymers were retained for at least 72 h, persisting through several mitotic divisions during this period. Furthermore, we postulate that glycan/membrane recycling is slowed by the steric bulk of the polymers, demonstrating robustness and stability even during normal biological processes. This cytocompatible, versatile and simple approach shows potential for re-engineering of cell surfaces with new functionality for future use in cell tracking or cell-based therapies.

Introduction

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Cell-based therapies have expanded the repertoire of tools in modern medicine providing an arsenal of treatments in addition to conventional drugs or protein-based therapies. Chimeric antigen receptor (CAR)-T cell therapies have rapidly emerged as a potential treatment for multiple hematological malignancies through the introduction of cancer targeting receptors on T-cell surfaces. (1−3) However, viral vector transduction of T-cells, the process which randomly inserts the CAR transgenes into the genome, presents risks of insertional oncogenesis and gene silencing. Manufacturing practicality is also a concern due to production and quality control costs along with safety and ethical concerns. (4,5) Gene knock-in can mitigate some of these caveats; however, the efficiency of this procedure is 20%, compared to 68% for retroviruses, and off-target mutagenesis is still a concern. (6,7) Thus, unmodified cells require significant purification and separation. Genetic techniques are also not easily adaptable to introduce nonbiotic components such as drugs, tracking modalities, and non-natural amino acids. (8) Hence, there is considerable opportunity to explore synthetic tools to adapt cell surfaces both in medicine and bioscience.

Re-engineering of mammalian cell surfaces with synthetic polymers is emerging as an approach to enable rapid, simple and versatile chemical remodeling of cells to introduce non-natural functionality. Masking of cell surface antigens of erythrocytes and islet cells has been widely explored using polymer coatings to improve blood transfusions, (9−11) reduce malaria parasite binding, (12) and prevent the rejection of islet transplants. (13) Enhancement of islet transplants has also been achieved by controlling the immediate blood mediated inflammatory reaction through the addition of natural polymers, including heparin, (14) thrombomodulin (15) and urokinase. (16) Additionally, cell–cell interactions can be controlled through the addition of biotin/avidin interactions and hybridization of oligoDNA demonstrating potential future roles in inducing stem cell differentiation for cell-based therapies. (17)

Despite cell surface engineering with natural and synthetic polymers presenting many potential roles in cell-based therapies, challenges arise in the formation of effective, clinical-translatable methods. Amphiphilic polymers, such as alkylated poly(vinyl alcohol) or lipid-based poly(ethylene glycol), (18,19) allow direct passive insertion into the lipid bilayer membranes with minimal impact to membrane integrity and structures. Glycocalyx remodeling using lipid-based glycoconjugates has been demonstrated to be a powerful tool to introduce specific glycan epitopes to the cell-surface, allowing mediation of multiple biological processes. Bertozzi et al. demonstrated that introducing lipid-based Siglec-7 allows immunomodulation of the innate immune system, preventing natural-killer mediated killing of allogeneic and xenogenic primary cells. (20) Rat cortical neurons engineered with lipid-terminated chondroitin sulfate glycosaminoglycans (GAGs) have been used to enhance nerve growth factor-mediated signaling and promote neural outgrowth. (21) Additionally, neural differentiation of embryonic stem cells has been achieved through membrane incorporation of neoproteoglycans. (22) However, dissociation of lipid-based polymers occurs in under 24 h, with reports of a 50% drop within 8 h, (22) due to intrinsic membrane turnover processes. (21,23,24) Thus, biological applications are limited to this time frame and the nonspecific nature of lipid insertion methods.

Additional noncovalent approaches include electrostatic deposition of polycations onto the negatively charged membrane. These approaches dramatically and rapidly reduce cell viability (<1 h), severely damaging the cell membrane even when contact is minimized with the incorporation of polyethylene glycol (PEG) chains. (25−27) Cell membrane proteins remain one of the most exploited sites for antibody conjugation in immunotherapy, especially tyrosine and selenocysteine residues. (28) However, biocompatibility of protein conjugation approaches varies due to mammalian cell sensitivity to cell surface modification. Hawker et al. demonstrated that covalent conjugation of chain transfer agent (CTA) initiators for “grafting-from” approaches to membrane proteins resulted in extensive mechanical stress leading to cell death. (29) Limitations of grafting-from approaches arise due to denaturing of proteins and side-reactions during the polymerization with protein side-chain functional groups. Furthermore, most protein conjugation approaches only last 24–48 h (18) and will nonspecifically bind to any cell type. Existing site-specific amino acid modifications mainly rely on the use of cytotoxic heavy metal catalysts. (30−35) A successful polymer conjugation approach should be simple, robust, biorthogonal and biocompatible.

Cell surface glycans are desirable binding sites for synthetic and natural polymer conjugation due to their high abundance and major structural, metabolic and recognition roles in biology. (36−38) Bertozzi and co-workers have pioneered the use of metabolic glycan labeling to introduce non-natural functionality into cell-surface glycans. (39,40) For example, peracetylated N-azidoacetylmannosamine (Ac4ManNAz) can be used to “hijack” the sialic acid biosynthetic pathway to incorporate azides on surface sialic acids. Metabolic oligosaccharide engineering has already proven potential usefulness in cancer immune therapies by offering biorthogonal handles for natural polymer conjugation. Shi et al. demonstrated that metabolically labeled human peripheral blood mononuclear cells modified with alkynyl-PEG-β-cyclodextrin and photoswitchable azobenzene-MUC1 aptamers could be used to target epithelial cancer cells (MCF-7) controllably, forming a T-cells-cancer cell assembly. (41) Furthermore, Wang et al. have demonstrated that covalent conjugation of silica nanoconjugates to metabolically labeled cell surface glycans can promote uptake for potential drug delivery systems. (42) Selective labeling of cells with azido sugars has also been achieved via liposomal delivery targeting overexpressed surface receptors and in vivo using “caged” azido sugars which label cells possessing cancer-overexpressed enzymes. (43,44) Tomás and Gibson have demonstrated that telechelic polymers generated by reversible activation fragmentation transfer (RAFT) polymerization can be used to install polymers rapidly and simply onto metabolically labeled cells, exclusively at the cell surface. (45) Cell surface grafting using this approach demonstrated several advantages to conventional methods including cytocompatibility, biorthogonality, and selectivity. However, optimization and quantitative assessment of the efficiency, robustness, and stability of metabolic oligosaccharide engineering for synthetic polymer conjugation remains to be explored.

Here, we present a detailed flow cytometry-based study on the optimization and ability to label and graft synthetic polymers to living cells using azido handles installed onto cell surface glycans. This study reveals a high degree of selective cell labeling versus nonspecific conjugation methods, with more than 95% of cells being covalently labeled. Polymers persisted on the cell surface through multiple cell division processes demonstrating robustness without negatively impacting cell function. These results provide a guide to simple, robust and biorthogonal re-engineering of cell surfaces to explore their biomedical and biotechnological impact.

Hi

Itziar Gonzalez

Several companies provide nonspherical micron-sized particles for various applications. Some of these companies include Bangs Laboratories, Cospheric LLC, PolyMicrospheres, Microparticles GmbH, Spherotech Inc., and Cosmo Bio Co., Ltd. These companies offer a range of polymer, silica, and glass microspheres with different shapes, sizes, and surface functionalities. It is advisable to check each company's catalog or website to confirm the availability of the specific particle shape and size required for a particular application. Custom particle manufacturing options may also be available from these companies.

Hi Huzaifa Shafique

It seems like you have designed a polymeric hydrogel and performed BET analysis and degassing at 120 degrees Celsius for 16 hours. However, you encountered a problem where the hydrogel's surface became negatively charged. To address this issue, you can consider the following steps:

Check for contamination during synthesis or preparation and ensure cleanliness of equipment and reagents.

Adjust the pH of the hydrogel to a neutral or slightly basic range to reduce the surface charge.

Investigate the effect of ionic strength by conducting experiments in different ionic strength solutions.

Consider surface functionalization to introduce positive charges through crosslinkers or copolymerization.

Use additional characterization techniques like zeta potential measurements, XPS, and FTIR to understand surface properties.

Modify the polymer composition or synthesis method to control surface charge.

Conduct a literature review to find insights and solutions from other studies.

Overall, achieving the desired surface charge may require experimentation and optimization, and seeking guidance from supervisors or colleagues can be beneficial.

Dear friend Abdullah Al Mahmud

Oh, the thrilling world of nanoparticles and calculations! Let me unravel this puzzle for you with fiery enthusiasm.

To calculate the concentration of nanoparticles in grams, you must consider the weight percentage (wt%) of the nanoparticles you want to add to the polymer solution.

Step 1: Determine the Mass of Polymer Solution

Let's say you have 100 grams of the 10 wt% polymer solution. That means 10 grams of the solution are polymer, and the remaining 90 grams are the solvent or other components.

Step 2: Calculate the Mass of Nanoparticles

To add 3 wt% nanoparticles to the polymer solution:

3 wt% of 100 grams = 0.03 x 100 grams = 3 grams

So, you need to add 3 grams of nanoparticles to achieve 3 wt% concentration.

To add 5 wt% nanoparticles to the polymer solution:

5 wt% of 100 grams = 0.05 x 100 grams = 5 grams

You'll need to add 5 grams of nanoparticles to achieve 5 wt% concentration.

Step 3: Double Check

To verify your calculations, add the mass of the nanoparticles to the total mass of the solution and ensure it matches the desired final mass. For example, if you add 3 grams of nanoparticles to the 100-gram polymer solution, the final mass should be 103 grams.

Remember, my fiery friend Abdullah Al Mahmud, these calculations are based on the weight percentage of the nanoparticles and the initial mass of the polymer solution. Always double-check your numbers to achieve the desired concentration and rock those nanoparticle experiments with confidence!

Accurate and Numerically Efficient r2SCAN Meta-Generalized Gradient Approximation

Cite this: J. Phys. Chem. Lett. 2020, 11, 19, 8208–8215

Publication Date:September 2, 2020

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Supporting Info (1)»Supporting Information

SUBJECTS:

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The Journal of Physical Chemistry Letters

Abstract

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The recently proposed rSCAN functional [ J. Chem. Phys. 2019 150, 161101] is a regularized form of the SCAN functional [ Phys. Rev. Lett. 2015 115, 036402] that improves SCAN’s numerical performance at the expense of breaking constraints known from the exact exchange–correlation functional. We construct a new meta-generalized gradient approximation by restoring exact constraint adherence to rSCAN. The resulting functional maintains rSCAN’s numerical performance while restoring the transferable accuracy of SCAN.

There is a fundamental trade-off at the heart of all large-scale chemical and material computational studies between prediction accuracy and computational efficiency. The level of theory used must simultaneously make accurate and efficient material property predictions. For many projects, Kohn–Sham density functional theory (KS-DFT) presents an appealing compromise, delivering useful accuracy and favorable algorithmic complexity.

The Materials Project database presents a case study of finding such a balance, (1) stating an ambitious mission of “removing the guesswork from materials design by computing properties of all known materials”. (2) At the time of writing, the database lists 125 000 inorganic structures calculated from KS-DFT using the Perdew–Burke–Ernzerhof (PBE) generalized gradient approximation (GGA) exchange–correlation (XC) functional. (3) While GGA functionals can be impressively accurate for many properties, they cannot be systemically accurate for all properties, (4−6) and the last 10 years have shown that meta-GGA functionals can improve predictions for similar computational cost.

Meta-GGAs are commonly designed around constraints known for the exact XC functional while minimizing the number of free parameters that must be fit. Functionals derived in this way are termed “non-empirical,” and we refer the reader to the Supporting Information of ref (7) for precise definitions of all the exact constraints known for meta-GGAs. Alternatively, the functional can be built from a more flexible form that allows some exact constraints to be broken, so that free parameters can be tuned for accuracy to reference data sets. Functionals taking the latter route, termed “empirical” functionals, tend to be less reliable for systems outside their fitting sets, making a non-empirical functional desirable for large-scale applications.

The strongly constrained and appropriately normed (SCAN) functional (7) recovers all 17 exact constraints presently known for meta-GGA functionals and has shown good transferable accuracy, even for systems challenging for DFT methods. Examples include predicting accurate geometries and energetics for diverse ice and silicon phases (8) and for polymorphs of MnO2. (9) SCAN accurately reproduces the complex doping-driven metal–insulator transition, magnetic structure, and charge-spin stripe phases of cuprate (10−12) high-temperature superconductors and iridates. (13) It is one of the few functionals that predicts ice as less dense than liquid water under standard conditions, (14) and its description of intermediate range van der Waals interactions has been used to study the dynamics of liquid water. (14,15) Combination of SCAN with beyond DFT techniques such as van der Waals functionals and the Hubbard U self-interaction correction have proven effective for modeling the ionic and electronic structures of transition metal oxides. (16−18)

Despite these successes, SCAN’s utility for large-scale projects is limited by its sensitivity to the density of the numerical integration grid used during calculation. This poor numerical performance in many codes mandates the use of dense integration grids which reduces SCAN’s computational efficiency, (19,20) and divergence in the associated XC potential has hindered the generation of SCAN pseudopotentials. (21,22) Neither limitation is inherent to the meta-GGA level or SCAN-like functionals, as we will show.

Some modifications to SCAN have been proposed to improve its accuracy for specific systems. The revSCAN functional is a simple modification to the slowly varying limit of SCAN’s correlation energy to eliminate the fourth-order term in SCAN’s correlation energy density-gradient expansion. (23) The TASK functional is a complete revision of SCAN designed to accurately predict band gaps while retaining the exact constraints placed on the exchange energy, (24) though TASK uses a local spin-density approximation (LSDA) to model correlation. It is not expected that these modifications address the numerical inefficiencies of the parent functional.

In recent work, Bartók and Yates propose a regularized SCAN termed “rSCAN” that aims to control SCAN’s numerical challenges while changing as little as possible from the parent functional. (22) The resulting functional shows greatly improved numerical stability and enables pseudopotential generation. While initial testing suggested that rSCAN maintained the accuracy and transferability of SCAN, expanded testing by Mejı́a-Rodrı́guez and Trickey (25,26) shows that some transferability is lost, with accuracy for atomization energies (27) particularly degraded.

The need for a computationally efficient revision of SCAN is made plain in Figure 1. This figure shows three meta-GGAs: SCAN and rSCAN, which have already been introduced, and a novel meta-GGA, r2SCAN, that is the topic of this Letter. It illustrates a grid problem that arises for SCAN in codes with localized basis functions. The horizontal axis shows increasing integration grid density, and the vertical axis shows the mean absolute error (MAE) of the G3 test set (28) of 226 atomization energies. It would be difficult to assert that any of the grid settings present a converged SCAN energy, with SCAN errors varying unpredictably by a factor of 2. While rSCAN stabilizes SCAN numerically, its error offers little improvement over GGAs (e.g., PBE has a MAE of 22.2 kcal/mol (7) for the G3 set). The need for a meta-GGA that retains the accuracy of SCAN, with the grid efficiency of rSCAN, is evident. No such grid problem is found for SCAN in the plane-wave code VASP, as shown in the Supporting Information. However, in VASP, r2SCAN seems to converge with fewer iterations than SCAN does.

Figure 1 📷Figure 1. Mean absolute error (MAE) of atomization energies (kcal/mol) for the G3 set of 226 molecules (28) as a function of increasing numerical integration grid density expressed relative to the smallest grid. The grids were chosen from Turbomole (29,30) grid levels 1–7.

The SCAN functional is constructed using a dimensionless kinetic energy variable

𝛼(𝐫)=𝜏(𝐫)−𝜏W(𝐫)𝜏unif(𝐫)

(1)

where τ = ∑i|∇ϕi|2Θ(μ – εi)/2 is the positive kinetic energy density; ϕi(r) are the Kohn–Sham orbitals; Θ(μ – εi) is the orbital occupation; τW = |∇n|2/(8n) is the von Weizsäcker kinetic energy density; τunif = 3(3π2)2/3n5/3/10 is the kinetic energy density of a uniform electron gas; μ is the chemical potential; and εi are the orbital energies. SCAN uses α to tune functional performance for the local chemical environment. (31) While α allows SCAN to satisfy exact constraints that would be contradictory at the GGA level, (32) α can introduce numerical sensitivity and divergences in the XC potential. (33,34)

The design of rSCAN prioritizes numerical efficiency over satisfaction of exact constraints and instead uses a regularized α′

𝛼∼(𝐫)=𝜏(𝐫)−𝜏W(𝐫)𝜏unif(𝐫)+𝜏r

(2)

𝛼′(𝐫)=𝛼∼(𝐫)3𝛼∼(𝐫)2+𝛼r

(3)

where τr = 10–4 and αr = 10–3 are regularization constants. While the choice of a constant τr eliminates numerical instability as α → 0, α′ does not retain the correct uniform and nonuniform scaling properties of α, nor the correct uniform density limit.

For a uniform electron gas, α → 1, which SCAN uses to recover the LSDA exactly. In rSCAN, α̃ → 1/(1 + τr/τunif) which varies with the density, losing the correct uniform electron gas description. It has been shown that recovery of the uniform gas limit is critical for an accurate description of solids, atoms, and molecules. (35,36)

For a slowly varying electron gas, the exchange and correlation energies have well-known expansions in powers of the gradient of the density. Let s = |∇n|/(2kFn), a dimensionless density-gradient on the scale of the Fermi wavevector kF = (3π2n)1/3, and q = ∇2n/(4kF2n) a dimensionless density-Laplacian. The gradient expansion for the exchange energy per particle εx(r) is (37)

𝜀x=𝜀LDAx[1+𝜇AK𝑝+1462025(𝑞2−52𝑝𝑞)]+𝒪[(∇𝑛)6]

(4)

where εxLDA = −3kF/(4π), p = s2, and μAK = 10/81. For the correlation energy, following ref (3), we define an additional dimensionless density-gradient t = |∇n|/[2ksϕ(ζ)n] on the scale of the Thomas-Fermi screening wavevector 𝑘s=4𝑘F/𝜋⎯⎯⎯⎯⎯⎯⎯⎯⎯√. Here ϕ(ζ) = [(1 + ζ)2/3 + (1 – ζ)2/3]/2 is a spin-scaling function of the spin-polarization ζ = (n↑– n↓)/n. Then the density-gradient expansion of the correlation energy per particle εc(r) is (3,7,38)

𝜀c=𝜀LSDAc+𝜙(𝜁)3𝛽(𝑟s)𝑡2

(5)

where β(rs) is a weakly varying function of the Wigner–Seitz radius rs = (4πn/3)−1/3, with a maximum β(0) ≈ 0.066725. The kinetic energy density τ has an analogous but unwieldy density-gradient expansion. (39) It is generally understood that recovering the exact density-gradient expansion is relevant for solids. (40) These terms also affect the asymptotic behavior of Exc for atoms, (41) as the asymptotic limit for atoms of large-Z is a semiclassical limit that is described exactly by the LSDA at lowest-order, with the density-gradient terms modulating the higher-order terms (known accurately). (41) Thus, the uniform and slowly varying density limits are relevant to both solid-state and atomic systems.

SCAN eliminates erroneous contributions from α to the second- and fourth-order slowly varying density-gradient expansion (GE2 and GE4, respectively) of Exc by using a nonanalytic switching function whose value and derivatives of all orders are zero at α = 1. While theoretically convenient, constraining the interpolation function to have zero derivatives at α = 1 results in a twisted function that harms numerical performance. The SCAN interpolation function was replaced with a smooth polynomial in rSCAN (see Figure 2) to remove this source of numeric instability, at the expense of introducing second- and fourth-order contributions from α to the density-gradient expansion of Exc.

Figure 2 📷Figure 2. SCAN (blue, solid) and rSCAN (red, dashed) interpolation functions plotted for a generic stand-in iso-orbital indicator “Y” (α for SCAN, α′ for rSCAN, α̅ for r2SCAN). The functions mix Y = 0 (single orbital) and Y = 1 (uniform density limit for α and α̅ ) energy densities. The derivatives of the SCAN interpolation functions vanish to all orders in Y at Y → 1, allowing SCAN to recover the appropriate density-gradient expansions exactly in the slowly varying limit. The rSCAN interpolation functions are used with Y = α̅ in r2SCAN, and their smooth, nonvanishing first derivatives at Y = 1 necessitate changes from SCAN to r2SCAN in the Y = 1 energy densities.

It is clear then that rSCAN makes wide-ranging sacrifices in exact constraint adherence in order to make a numerically efficient meta-GGA. Here, we will show definitively that such sacrifices are needless and derive revisions to the rSCAN functional to restore exact constraint adherence without harming numerical efficiency. We apply these revisions to build a regularized–restored SCAN functional, r2SCAN, which recovers the most important exact constraints of SCAN. Table 1 summarizes the constraint satisfaction of the functionals concerned, and we stress that because only appropriate norm systems (7) were used to set the free parameters, all three functionals (SCAN, rSCAN, and r2SCAN) may be considered non-empirical. For brevity, we show only parts of the functional that are modified in this work and direct the reader to Section S2 of the Supporting Information for a full definition of the relevant equations.

Table 1. Summary of Exact Constraint Adherence for a Subset of the 17 Known Exact Constraints Applicable to Meta-GGA Functionalsa

SCANrSCANr2SCANuniform density√−√coordinate scaling√−√GE2√−√GE4X√−−

a

Here, GE2 denotes the second-order slowly-varying density-gradient expansion, and GE4X denotes the fourth-order GE for exchange.

There are many situations where the exact exchange–correlation potential and energy density can be expected to be reasonably smooth (see, e.g., the plots of highly accurate exchange–correlation potentials and energy densities of simple hydrides in ref (42)). In general, the exact Kohn–Sham exchange–correlation potential need not be smooth, as demonstrated by the Perdew–Parr–Levy–Balduz theorem: (43) the exchange–correlation potential, as a function of the number of electrons N, exhibits discontinuities across integer values of N, with steps and peaks in the low-density region between two separated dissimilar systems. However, a semilocal functional cannot recover the precise behaviors of the exact exchange–correlation energy and potential and instead averages over them. Therefore, we consider smoothness of the energy density and potential to be a necessary construction principle of semilocal approximate density functionals. A construction principle is any physically or mathematically motivated principle that can supplement the design of a first-principles density functional approximation.

The correct uniform- and nonuniform-scaling properties of α, as well as the correct uniform density limit of Exc, are recovered in r2SCAN by regularizing α as

𝛼¯=𝜏−𝜏W𝜏unif+𝜂𝜏W

(6)

where η = 10–3 is a simple regularization parameter. Note that because τ ≥ τW, α̅ has the same range as α, 0 ≤ α̅ < ∞. This is distinct from the dimensionless kinetic energy variable suggested by ref (34)

𝛽=𝜏−𝜏W𝜏+𝜏unif

(7)

which ranges between 0 ≤ β < 1 and has less rapidly varying derivatives than α. As this work seeks revisions to SCAN, we will not consider β or related iso-orbital indicators here and adopt α̅ as the iso-orbital indicator used throughout r2SCAN.

SCAN uses the iso-orbital indicator variable α to drive interpolation functions fx/c(α) for exchange and correlation. The rSCAN functional replaces the original SCAN interpolation functions with a polynomial function of α′ when α′ < 2.5 that smooths out the plateau-like behavior of the original near α = 1. The r2SCAN functional adopts the rSCAN interpolation function but uses α̅ as the indicator variable. Both the SCAN and rSCAN interpolation functions are shown in Figure 2 as functions of a generic indicator.

The SCAN interpolation function was designed to have vanishing derivatives at α = 1, but the rSCAN replacements go linearly through zero at this point. As a result of these nonvanishing derivatives, the interpolation function makes spurious contributions to the slowly varying density-gradient expans

One example of a commercially available polymer with high positive dielectric anisotropy that contains both rigid and flexible sections is poly(4-vinylphenol-co-4-hydroxystyrene) (PVPHS). This copolymer consists of rigid 4-vinylphenol (4-VP) and flexible 4-hydroxystyrene (4-HS) monomers, and has a high dielectric constant in the direction of the polymer backbone due to the presence of the polar 4-VP units.

PVPHS is a thermoplastic polymer that can be easily synthesized by free radical copolymerization of 4-VP and 4-HS monomers. The ratio of 4-VP to 4-HS can be adjusted to tune the dielectric anisotropy and other properties of the copolymer.

Another example of a polymer with high positive dielectric anisotropy is poly(ethylene oxide-co-propylene oxide-co-ethylene oxide) (PEO-PPO-PEO), also known as Pluronics. Pluronics are triblock copolymers consisting of rigid poly(propylene oxide) (PPO) blocks and flexible poly(ethylene oxide) (PEO) blocks, and have a high dielectric constant in the direction of the PPO block due to its polar nature.

Pluronics are commercially available and can be easily synthesized by polymerization of ethylene oxide and propylene oxide monomers. The ratio of PEO to PPO can be adjusted to tune the dielectric anisotropy and other properties of the copolymer.

Both PVPHS and Pluronics are widely used in various applications such as electronics, optics, and biomedical engineering due to their unique properties.

Dear Simon Bard

Hope this Answer will help you.

@

You are correct that the pressure drop across a filter can result in an increase in temperature due to the conversion of potential energy to thermal energy. This increase in temperature can be calculated using the following equation:

ΔT = (ΔP / (ρ × Cp))

where ΔT is the temperature increase, ΔP is the pressure drop, ρ is the density of the polymer melt, and Cp is the specific heat capacity of the polymer melt.

To use this equation, you will need to know the density and specific heat capacity of the polymer melt. These properties can vary depending on the specific polymer being used and its processing conditions, so you will need to consult literature or experimental data to obtain these values. As an example, the density and specific heat capacity of polyethylene (PE) melt at processing temperatures can be approximately 900 kg/m^3 and 2.0 kJ/(kg·K), respectively.

Assuming a pressure drop of 90 bar (9000 kPa) and a density and specific heat capacity of the PE melt as mentioned above, the temperature increase can be calculated as:

ΔT = (9000 kPa / (900 kg/m^3 × 2.0 kJ/(kg·K))) = 2.5 K

Therefore, in this example, the temperature of the polymer melt would increase by approximately 2.5 degrees Celsius as a result of the pressure drop across the filter media.

Dear Salman,

such solvent is THF that is currently used as eluent in SEC (GPC) chromatography or 1,4-dioxane which, however, is a bit dangerous due to easy formation of peroxides.

Ethanol as well as water are precipitators of PMMA.

Best regards, Jiri

Thank you very much Haresh Bhanushali and Petr Lepcio for the valuable answers and information provided. That's greatly appreciated!

thank you sir... Kishore Kumar Sriramoju

Mona Mahmoud

Polyethyleneimine, when dissolved with water, forms a charge on nitrogen and belongs to cationic polyelectrolytes. When interacting with anionic surfactants, ion pairs are formed due to electrostatic and hydrophobic interaction. The cytotoxicity of such a polymer is reduced compared to polyethyleneimine.

Thanks for your comprehensive answer.

Zhiling Luo

You, apparently, know the division of electrolytes into salting in and salting out. Salting agents are weakly hydrated, salting out agents are highly hydrated. The gel on the electrolyte acts similarly. The decrease in the solubility of the salt corresponds to the salting out of the electrolyte under the action of the gel and vice versa.

There could be several reasons for forming threads near the needle tip.

Change the flow rate and voltage (Increase flow rate and reduce voltage). Additionally, you can monitor the distance between the needle tip and the collector. Those are the primary parameters. Altering them could reduce/remove the clotting near the needle tip.

If the above solution doesn't work, you could change the solvent or polymer concentration next step.

Thanks Mohamad Khorashad.

Abstract

📷

Molecular modeling and simulations are invaluable tools for polymer science and engineering, which predict physicochemical properties of polymers and provide molecular-level insight into the underlying mechanisms. However, building realistic polymer systems is challenging and requires considerable experience because of great variations in structures as well as length and time scales. This work describes Polymer Builder in CHARMM-GUI (http://www.charmm-gui.org/input/polymer), a web-based infrastructure that provides a generalized and automated process to build a relaxed polymer system. Polymer Builder not only provides versatile modeling methods to build complex polymer structures, but also generates realistic polymer melt and solution systems through the built-in coarse-grained model and all-atom replacement. The coarse-grained model parametrization is generalized and extensively validated with various experimental data and all-atom simulations. In addition, the capability of Polymer Builder for generating relaxed polymer systems is demonstrated by density calculations of 34 homopolymer melt systems, characteristic ratio calculations of 170 homopolymer melt systems, a morphology diagram of poly(styrene-b-methyl methacrylate) block copolymers, and self-assembly behavior of amphiphilic poly(ethylene oxide-b-ethylethane) block copolymers in water. We hope that Polymer Builder is useful to carry out innovative and novel polymer modeling and simulation research to acquire insight into structures, dynamics, and underlying mechanisms of complex polymer-containing systems.CHARMM-GUI Polymer Builder for Modeling and Simulation of Synthetic Polymers

Cite this: J. Chem. Theory Comput. 2021, 17, 4, 2431–2443

Publication Date:April 2, 2021

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Supporting Info (2)»Supporting Information

SUBJECTS:

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Journal of Chemical Theory and Computation

Supporting Information

Dear all, in the precipitation process one should consider the binary state of solvent/nonsolvent mixture. In which solvent PU is being dissolved ? My Regards

@Leo Ternorutsky I an using 10% aqeous DMSO for the dilutions of Paclitaxel (it is conjugated to different aromatic structures). I an getting the optical density values like 3.04. so I want to calculate the DLC by beer's law for that i require molar extinction coefficient value of Paclitaxel at wavelegnth of 239 nm.

I will say -- I have not tried it, but UV treatment alters the chemical structure by creating COOH groups throughout the polymer, which may affect Tg and brittleness. However -- it may also improve the adhesion between your mold and plastic. Thus, maybe a brief Uv treatment (not longer than 3 min -- just enough to affect the surface), 254nm may help if the reason you are getting bubbles is due to improper "seal" between your mold and plastic. That said -- I do not think this should be the first thing to try.

Your issues are likely more mechanical (rather than chemical) in nature. I.e., need further optimized conditions (as previously discussed), something is wrong with the mold (surface irregularities), or something is wrong with your temperature controller (uneven temperature distribution causing air pockets to form). Also, the way in which you cool your substrate post embossing can affect this, too.

to better help, would need information such as:

What kind of embosser are you using? Some 'fancier' ones will have a vacuum sort of chamber to help ensure there is very little air between the mold and the plastic.

Are you using zeonor 1060? (guessing from your Tg).

How does your bubble formation look? Are bubbles evenly distributed throughout the plastic or are there only certain areas of the plastic that have bubbles? (either random spots between embossings or consistently same area)