SKU: 89968356595

PALL OS070T12 T-series Centramate™ cassette, Omega PES membrane, 70 kDa molecular weight cut-off (MWCO), 0.1 m² effective filtration area (EFA)

Sale price$611.20 Regular price$679.11
Save 10%

Pay in installments of $169.78 with ShopPay, AfterPay and Klarna

Shipping Estimate
USA
  • USA
  • CAN

Ships within 48 hours · Estimated delivery Aug 19 - Aug 24

Promo Codes Available:

For Your Every Summer RSVP, with Code: SUMMER15

Description

PALL OS070T12 T-series Centramate™ cassette, Omega PES membrane, 70 kDa molecular weight cut-off (MWCO), 0.1 m² effective filtration area (EFA)T Series Centramate cassettes with Omega PES membrane are designed for development, pilot, and production scale TFF applications in diverse biological and biopharmaceutical processes. Easy implementation into your process. The new T series cassettes offer significant improvements in construction and design while utilizing the same proven Omega polyethersulfone (PES) membrane to reduce revalidation requirements. Increased safety, reliability, and

T-Series Centramate™ cassettes with Omega™ PES membrane are designed for development, pilot, and production-scale TFF applications in diverse biological and biopharmaceutical processes.
Easy implementation into your process. The new T-series cassettes offer significant improvements in construction and design while utilizing the same proven Omega polyethersulfone (PES) membrane to reduce revalidation requirements.
Increased safety, reliability, and reproducibility. Superior new materials of construction are durable and stable, exhibit very low extractables, and offer broad chemical compatibility.
High flux, high selectivity, and low protein binding are achieved through the use of Omega PES membrane.
Improved process performance. Cassettes are designed to provide optimal mass transfer to improve your process economics.
Easy scale up for robust purification processes. Available in scalable formats with the same materials of construction from development to production-scale processes.
Enhanced validation. T-series cassettes surpass the latest biopharmaceutical and regulatory standards such as biological reactivity, extractables, and TOC.
Omega polyethersulfone (PES) membranes offer high flux and selectivities. They have been specifically modified to minimize protein binding to the surface and interstitial structure of the membrane. This polymeric membrane is stable against biological and physical degradation due to the unique chemical properties of PES.
Omega membranes are cast on a highly porous, non-woven polyolefin support. They have an anisotropic structure, a thin skin-like top layer with a highly porous underlying support. The structure of the skin determines the porosity and permeability characteristics of the membrane and can typically be cleaned quicker and easier than membranes with a uniform, sub-micron depth structure. This membrane is compatible with acids, bases and a variety of other cleaning agents. Omega membranes are available in a wide range of nominal molecular weight cutoffs (NMWC).
Superior new materials of construction and improved cassette design increase process safety, reliability, reproducibility, and productivity:
The feed and permeate screen material for T-Series cassettes is made from polypropylene which is highly resistant to sodium hydroxide.
Larger feed and permeate ports provide lower pressure drops.
T-Series cassettes have been designed to provide maximum mass transfer through the membrane, resulting in faster processing times or reduced area installations compared to existing similar cassette formats.

Shipping Notes
  • Free Standard Shipping on $100+ Orders to the USA.
  • Except Preorder products are shipped in 48 hours.
  • Delivery to the USA:
  1. Standard Shipping : 3-10 business days
  • If time is of the essence, please consider selecting expedited delivery for faster service.
Exchange/Return Notes
  • We offer a 30-day return/exchange service after receiving.
  • Final sale items are not eligible for returns or exchanges.
  • To process your return/exchange, please contact us at [email protected]
  • Please click here for more details>>> Return & Exchange Policy
SKU: 89968356595

Discover Niche Categories That Outsell

Top-Converting Item to Boost Your Average Order

4.6 ★★★★★
Based on 30 reviews
Sort
Highest Rating
Newest First
Oldest First
Product Reviews
A
Verified Purchase
Amazon Customer
Phoenix, US
★★★★★ 4
Just learning it
Format: Paperback
Nice learning book just have to finish it
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on December 10, 2025
K
Verified Purchase
Kindle Customer
Lexington, US
★★★★★ 5
Very useful book
Format: Paperback
I use it for the machine learning class I teach.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on May 3, 2026
T
Verified Purchase
Tommy Jonsson
Phoenix, US
★★★★★ 5
Cover many areas in detail and recommendations for more to read for what's outside
Format: Paperback
Good book!
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on May 4, 2026
M
Verified Purchase
Moses Kayanda
Natrona Heights, US
★★★★★ 5
One of the best machine learning books...
Format: Paperback, Format: Paperback
Machine Learning can often be intimidating whether you are starting out or already a practitioner. It is easy to get stuck on one concept, walk away frustrated, or just copy that code you find on StackOverflow without really understanding what it does. What the authors of this book, Machine Learning with PyTorch and Scikit-Learn, have managed to do is to keep the reader engaged giving a deeper illustration as to how the concepts work. In this book, you get practical code examples, a detailed explanation of how the various library tools work, and exposure to the mathematical concepts behind machine learning algorithms. In addition, what I like about the book unlike many machine learning books is that the authors have managed to intuitively explain how each algorithm works, how to use them, and the mistake you need to avoid. I have not read a Machine Learning book that better explains Transformers as this one does. The authors have managed to give a detailed dive into this model architecture through well-explained codes and illustrations. As a reader, you walk away having intuitively grasped the concepts of attention and self-attention in ways that will make this crucial NLP architecture clear. You get exposed to pre-trained models from HuggingFace library which really helps to have that hands-on experience working with large datasets. As they have done throughout the book, the authors have broken down those complex mathematical operations into simple explanations that are easy to follow. What I generally like about the book is how it seamlessly connects all the chapters, not throwing off the reader. There are numerous external resources quoted throughout the book. This helps spark that curiosity to dig deeper. In addition, you get introduced to PyTorch, getting exposed to all those sophisticated libraries that help the reader learn how to maximize their compute power. I would say it is not intimidating at all even if you have not used PyTorch before. I would recommend this book to anybody seeking a textbook that is both easy to read and modern in its content. If were to rate the book I will give it a 10/10 as it really applies to both beginners and experienced practitioners, covers all the concepts one needs to apply in their operations, and acts as a quick reference.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on March 1, 2022
G
Verified Purchase
Gabe Rigall
Pawtucket, US
★★★★★ 5
Thorough Primer for Machine Learning and PyTorch
Format: Paperback
BLUF: A thorough primer for machine learning enthusiasts with plenty of theory to underscore its many practical examples. A definite must-have for anyone looking to add PyTorch to their machine learning tool belt. PROS: - Extremely thorough (if not comprehensive). I really appreciate that this book doesn't just thrust one into building models with PyTorch. It starts at the "beginning" and provides examples, theory, additional resources, and citations along the way. - Theory. Those whose calculus and linear algebra courses ended many years ago will appreciate (if not remember exactly) the mathematical theory and notation that accompanies almost every paragraph. This book gives one the opportunity to "dig deeper" or stay in the shallows until the notation stops. - Python. Rather than simply utilizing Scikit-Learn to illustrate concepts and introduce models, this book contains many sections where models (such as a Perceptron) are coded from the ground up so the reader can fully understand the underlying mechanics. Python enthusiasts will nerd out. Parents of small children might want to skip a few pages. - Graphs, charts, and graphics. There are plenty of places where a drier text might have foregone the use of graphs. This text does not. It does however refrain from overusing them. - PyTorch. This should be obvious from the title, but this text prioritizes PyTorch instead of TensorFlow. This is especially helpful for those looking for an alternative to Keras and TensorFlow as the PyTorch API is very user-friendly. CONS: - Almost too much code. This isn't a true "con" but anyone wanting to emulate or follow along with the examples would do well to get the digital edition so they can copy and paste. - Length and complexity. Anyone hoping for a "quick read" or a "quick start guide" will be disappointed. This book hovers somewhere between an undergraduate primer and a graduate-level text for length and readability. This is not to say that it's difficult to read, merely that there are other "quick start" / "practical" texts out there that cater more to a lay audience.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on February 26, 2022

recommand products