Why Labs Struggle Without Analytical Quality by Design (AQbD)
April 26, 2025 · Chemistry
Key Takeaways
Traditional analytical method development often relies on trial-and-error or historical luck—not scientific design.
Without a structured framework like AQbD, methods are fragile, less transferable, and harder to fix when things go wrong.
Learning AQbD gives chemists a real roadmap for designing stronger methods—from day one.
What This Post Will Cover
The real-world problems with traditional method development
How AQbD reframes analytical work to prevent failure
Practical examples you’ll recognize from any busy lab
Three actionable steps to start thinking like an AQbD chemist
🎯 The Punchline: Old-School Method Development Leaves Too Much to Chance
In most labs—even good ones—analytical methods are developed like recipes passed down in a kitchen. Maybe it works. Maybe it doesn’t. But no one really knows why.
Without structure, most methods:
Break when small conditions change
Struggle to transfer between labs or instruments
Cause validation headaches and costly troubleshooting later
That’s where Analytical Quality by Design (AQbD) steps in—not just as a buzzword, but as a new way of thinking that’s systematic, predictable, and sustainable.
🧪 Problem: How Traditional Method Development Fails Us
In many labs, here’s how a method gets born:
You pick a column because someone else used it.
You pick a mobile phase because it worked “ok” before.
You tweak things until the peaks look decent.
You validate it and hope nothing drifts over time.
But what happens later?
You switch to a new batch of column → retention time shifts
You tweak flow rate slightly → baseline noise explodes
You move to another lab → method falls apart completely
Because the method was built ontrial and error, not knowledge and structure.
🛠️ Solution: What AQbD Brings to the Table
AQbD asks you to design your method intentionally from the start by answering three big questions:
Instead of guessing, you plan experiments to map out the “safe space” where the method is rugged and reproducible.
📊 Real-World Lab Examples
Old-School Approach
AQbD Approach
Pick the first C18 column you find
Screen different stationary phases systematically
Start mobile phase pH wherever you feel like
Select pH based on analyte’s pKa and stability
Validate only after development
Build validation into method design via robustness testing
🧠 Chemist’s Thinking Process: From Guessing to Designing
Traditional Mindset:
“Let’s see if this works.”
AQbD Mindset:
“Let’s define the performance we need, and design a method that can survive real-world variability.”
🧰 Actionable Steps to Start Thinking Like an AQbD Chemist
✅ 1. Always Ask: What is My ATP?
Before you touch an HPLC column or pipette, define:
What performance must the method achieve? (e.g., accuracy, specificity, LOD, LOQ)
✅ 2. Map Critical Variables Early
Even if you’re not doing full DOE yet, ask:
What variables (pH, flow, column, temp) could make or break my method?
✅ 3. Stress Your Method Intentionally
Once you get a “good looking” method, push it: slightly change flow, pH, column lot, etc.
See if it still performs well—that’s what real robustness looks like.
🚀 Closing
The labs that succeed today—and even more so in the future—aren’t just the ones who know how to troubleshoot after a method fails. They’re the ones who design methods strong enough to avoid failure from the beginning.AQbD isn’t extra work. It’s how you make your methods last longer, transfer better, and give you fewer headaches down the road.
Demystifying LoD and LoQ: What Every Chemist Needs to Know
April 26, 2025 · Chemistry
The punchline:
In analytical chemistry, knowing how low you can go isn’t just academic—it’s essential. Your method’s limit of detection (LoD) and limit of quantification (LoQ) determine whether you can trust that tiny peak or that faint signal. Get these wrong, and you might miss a critical contaminant or report false positives. This guide breaks down the calculations and gives you actionable steps to implement today.
What We’ll Cover
Real-world implications of LoD and LoQ
Different approaches to calculating these limits
Step-by-step instructions you can use in your lab
How to properly report results near or below detection limits
Regulatory expectations
Common pitfalls and how to avoid them
Let’s Get Real About Detection Limits
Picture this: You’re analyzing water samples for a trace contaminant. The regulatory limit is 5 ppb. Your method can reliably measure down to 10 ppb. See the problem? Your method literally can’t detect violations until they’re twice the legal limit. Yikes.
Or imagine telling a client their product contains “none” of a toxic impurity, only to have another lab detect it later. Not a great look for you or your lab.
This is why understanding LoD and LoQ matters. It’s not just math—it’s about knowing what your method can and cannot do reliably.
What These Terms Actually Mean
Limit of Detection (LoD): The lowest concentration where you can say “yes, it’s definitely there” but not necessarily “there’s exactly this much of it.”
Limit of Quantification (LoQ): The lowest concentration where you can confidently say “there’s X amount of it” with reasonable accuracy.
Think of it like this: LoD is where you can detect the signal of someone whispering in a crowded room. LoQ is where you can not only hear them but understand exactly what they’re saying.
Four Ways to Calculate LoD and LoQ (With Real Examples)
1. The Signal-to-Noise Approach
What it is: Comparing your analyte’s signal to the background noise
Perfect for: Chromatography methods where baseline noise is visible
Example: Let’s say you’re running an HPLC analysis for caffeine in beverages. You inject a standard with 0.5 μg/mL caffeine and measure:
The height of the caffeine peak = 15 mV
The height of the baseline noise = 1 mV
Your S/N ratio is 15:1
Calculations:
LoD = 3 × (0.5 μg/mL) / 15 = 0.1 μg/mL
LoQ = 10 × (0.5 μg/mL) / 15 = 0.33 μg/mL
Do this:
Run a blank sample and measure the amplitude of the noise
Run a low concentration standard (start with about 10× what you think your limit might be)
Measure the signal of your analyte peak
Divide signal by noise to get your S/N ratio
Apply the formulas above
2. The Calibration Curve Method
What it is: Using statistics from your calibration curve
Perfect for: When you already need to create calibration curves anyway
Example: You’re developing a UV-Vis method for nickel in wastewater. After running standards from 0.05 to 2.0 mg/L, your linear regression gives:
Slope = 0.241 absorbance units per mg/L
Standard deviation of y-intercept = 0.0036 absorbance units
Calculations:
LoD = 3.3 × (0.0036 / 0.241) = 0.049 mg/L
LoQ = 10 × (0.0036 / 0.241) = 0.149 mg/L
Key distinction: This method uses the standard deviation of the y-intercept from regression analysis, capturing the uncertainty in your entire calibration model.
Do this:
Prepare calibration standards that include low concentrations
Plot your calibration curve using statistical software (e.g. Excel)
Get the slope and standard deviation of the y-intercept
Apply the formulas above
3. The Blank Standard Deviation Method
What it is: Running multiple blanks and using statistics
Perfect for: Methods where blank responses are measurable and variable
Example: You’re measuring trace metals in drinking water by ICP-MS. You analyze 10 blank samples and get:
Mean blank signal = 2.3 counts
Standard deviation of blanks = 0.8 counts
Calibration curve slope = 45 counts per ng/mL
Calculations:
LoD = (2.3 + 3 × 0.8) / 45 = 0.104 ng/mL
LoQ = (2.3 + 10 × 0.8) / 45 = 0.231 ng/mL
Key distinction: This method measures the variability when NO analyte is present. It directly measures your system’s baseline noise.
Do this:
Prepare and analyze at least 10 blank samples (use your actual sample matrix if possible)
Calculate the mean and standard deviation
Apply the formulas above
Verify by testing a sample at your calculated LoD
4. The Low Concentration Replicate Method
What it is: Testing replicates at a low concentration
Perfect for: When you can’t measure meaningful blank signals
Example: You’re developing a GC method for pesticide residues. You prepare 7 replicates at 5 ng/g and get:
Mean response = 1240 area units
Standard deviation = 145 area units
Slope of calibration curve = 248 area units per ng/g
Calculations:
LoD = 3.3 × (145 / 248) = 1.93 ng/g
LoQ = 10 × (145 / 248) = 5.85 ng/g
Key distinction: This method measures variability when a LOW AMOUNT of analyte is present. It captures real-world variability at concentrations near your expected limits.
Do this:
Prepare at least 7 replicates at a low concentration
Calculate the standard deviation
Apply the formulas above
Comparison of Methods: Which One Should You Choose?
Method
What You Measure
When to Use
Advantages
Limitations
Signal-to-Noise
S/N ratio of low standard
Chromatographic methods with visible baseline
Simple, visual, intuitive
Subjective; depends on how noise is measured
Calibration Curve
SD of y-intercept
Methods with good linear calibration
Uses all calibration data; accounts for model uncertainty
May underestimate limits if intercept has low variance
Blank SD
Variation of blank samples
Methods with measurable blank response
Directly measures system noise; good for clean matrices
May not account for matrix effects when analyte is present
Low Concentration
Variation at low spike level
Complex matrices; when blanks have no signal
Accounts for real-world variation near detection limit
Requires more sample preparation; spike level selection is critical
Visual Guide to Choosing Your Method
Method Selection Flowchart
(Description of flowchart: A decision tree that helps Chemist select the appropriate method based on their analytical technique. The chart starts with “Can you measure blank responses?” and branches into different paths based on yes/no answers, leading to recommendations for which method to use.)
The Guide to Reporting Results
Let’s talk about something many chemists struggle with what to do when your results come back weird, like negative values or below your detection limits.
What To Do with Negative Values
You run your analysis and get -0.13 ppm. What gives? Can you have negative concentration?
What’s happening: Instrument calibration creates a best-fit line. For very low concentrations, normal variation around the blank value can produce mathematically negative results. This is perfectly normal!
How to report it correctly:
Pharmaceutical industry (USP/ICH): Report as “< [LoQ]”. Example: “< 0.05 ppm”
Environmental testing (EPA): Several options depending on the regulation:
Option 1: Report the negative value as is (-0.13 ppm) with a flag
Option 2: Report as “< [MDL]” (e.g., “< 0.02 ppm”)
Option 3: Report as zero with qualifier (e.g., “0 ppm U”)
Food testing (FDA/AOAC): Typically report as “< [LoQ]” or “ND” (Not Detected)
Example: You’re testing for lead in drinking water. Your LoD is 0.002 ppm and LoQ is 0.005 ppm. You get a result of -0.001 ppm.
Incorrect reporting: “0 ppm” (implies certainty that none is present) or “-0.001 ppm” (implies impossible negative concentration)
Results Between LoD and LoQ
What about when your result is 0.003 ppm, which is above your LoD (0.002 ppm) but below your LoQ (0.005 ppm)?
How to report it correctly:
Most regulated industries: Report as “< [LoQ]” (e.g., “< 0.005 ppm”)
Some environmental applications: Report the actual value with a qualifier (e.g., “0.003 ppm J” where J indicates estimated value)
Research settings: Report as “Detected, not quantified” or the actual value with appropriate uncertainty
Pro Tip: Creating a Decision Table
Create this table and post it in your lab:
Result Range
How to Report
Example
Result < 0
“< [LoQ]”
“< 0.05 ppm”
0 ≤ Result < LoD
“< [LoD]” or “ND”
“< 0.02 ppm” or “ND”
LoD ≤ Result < LoQ
“< [LoQ]” or actual value with qualifier
“< 0.05 ppm” or “0.03 J ppm”
Result ≥ LoQ
Report actual value
“0.08 ppm”
The Reporting Checklist
✓ Include LoD and LoQ values in your report footnotes ✓ Specify how LoD and LoQ were determined ✓ Use consistent reporting conventions throughout ✓ Include appropriate qualifiers for results near limits ✓ Never report “zero” or “none detected” without specifying limits
What Regulators Actually Want
Let’s cut through the regulatory jargon:
FDA/ICH (Pharmaceuticals): They want to see that you used one of the approaches above, verified it experimentally, and documented everything. They particularly like the calibration curve and S/N approaches.
EPA (Environmental): They’ve popularized the MDL (Method Detection Limit) approach, which involves analyzing at least 7 spiked samples and multiplying the standard deviation by a t-value. They want to see that you reevaluate annually.
AOAC (Food): They often prefer the S/N approach for chromatographic methods but accept other approaches if justified.
The common thread? Documentation, justification, and verification.
Five Mistakes New Chemists Make (And How to Avoid Them)
The Pure Standard Trap
Mistake: Determining limits using pure standards in solvent
Reality: Your real samples have matrix effects
Fix: Use matrix-matched standards or standard addition
The Single Calculation Error
Mistake: Calculating once and never verifying
Reality: Theoretical calculations need experimental confirmation
Fix: Always test samples at your calculated limits to confirm performance
The Wrong Range Problem
Mistake: Using a calibration range that’s too high
Reality: Your calibration should extend down to near your expected LoQ
Fix: Include at least 2-3 calibration points below your expected LoQ
The Sample Size Shortcut
Mistake: Using only 3-4 replicates
Reality: Small sample sizes give unreliable statistics
Fix: Use at least 7 replicates (most regulatory methods require this minimum)
The Method Mismatch
Mistake: Using an LoD/LoQ approach that doesn’t fit your technique
Reality: Different analytical methods need different approaches
Fix: See the flowchart above for guidance
Practical Tools for Your Lab
Here’s a simple Excel template you can create:
Replicate
Blank Response (Noise)
Low Standard Response (Signal)
Concentration of Low Standard
1
[enter data]
[enter data]
[enter value]
2
[enter data]
[enter data]
[enter value]
…
…
…
…
10
[enter data]
[enter data]
[enter value]
Mean
[formula]
[formula]
[formula]
Std Dev
[formula]
[formula]
N/A
S/N Ratio
N/A
[formula]
N/A
LoD
[formula]
[formula]
N/A
LoQ
[formula]
[formula]
N/A
No More Guesswork: A Verification Protocol
After calculating your limits, here’s how to verify them:
Prepare samples at exactly your calculated LoD concentration
Analyze at least 3 replicates
For all replicates, you should detect the analyte (this confirms your LoD)
Prepare samples at exactly your calculated LoQ concentration
Analyze at least 6 replicates
Calculate the %RSD – it should be ≤20% (this confirms your LoQ)
The Bottom Line
Determining LoD and LoQ isn’t just checking a regulatory box—it’s about knowing your method’s capabilities and limitations. It’s about confidence in your results and integrity in your reporting.
When you properly determine these limits, you’re not just following rules—you’re practicing good science. And in a world where analytical results drive critical decisions about product safety, environmental protection, and human health, good science matters.So which approach will you implement in your lab tomorrow?
Case Study: When Vitamin C Peaks Split Without Warning – What’s Really Going On?
April 26, 2025 · Chemistry
Key Takeaways
Vitamin C’s behavior is highly pH-sensitive and system-sensitive.
Peak splitting is a symptom — the root cause could be chemical, mechanical, or both.
Smart troubleshooting requires methodical questioning, not random trial and error.
Start with the quickest checks and eliminate simple causes first before diving deeper.
What This Post Will Cover:
Chemical causes of Vitamin C peak splitting
Mechanical causes you can’t afford to ignore
The step-by-step thought process a chemist should follow
Actionable fixes you can apply today
Visuals and summaries to make it easy to remember
🎯 The Punchline: Peak Splitting is the Symptom—Not the Root Cause
Whether it’s a buffer prep mistake, injector contamination, or unexpected pH drift, split peaks are a messenger, telling you something critical has shifted.
You don’t fix split peaks—you fix the system causing them.
🧪 Part 1: Chemical Causes of Peak Splitting
1. pH Drift and Ionization Change
Vitamin C (pKa ~4.2) is very sensitive to small pH shifts.
Working too close to pKa (~pH 4–5) → mixture of ionized and non-ionized forms → two elution behaviors.
Ask Yourself:
What pH did I actually measure after adding organic solvent?
Am I running within 1 pH unit of Vitamin C’s pKa?
✅ Quick Test: Measure final mobile phase pH with organic included. If > pH 3.5–4.0, suspect drift.
2. Buffer Inconsistency
Weak buffers (e.g., <10 mM phosphate) or different salt hydrates = inconsistent ionic strength.
Ask Yourself:
Did I use the same salt (hydrated vs. anhydrous) every time?
Was the buffer freshly prepared or sitting too long?
✅ Quick Test: Remake fresh buffer and compare performance side-by-side.
3. Sample Solvent Mismatch
If Vitamin C is injected in water or strong acid, mismatch with mobile phase causes fronting/splitting.
Ask Yourself:
Is my sample solvent weaker or stronger than the mobile phase?
Did I dilute my sample in the exact same buffer?
✅ Quick Test: Re-dissolve Vitamin C sample in mobile phase and re-inject.
🛠️ Part 2: Mechanical Causes of Peak Splitting
4. Dirty Injector, Needle, or Seals
Residue buildup causes inconsistent sample transfer → partial plugs or tailing.
Ask Yourself:
Was a proper needle wash or purge done before runs?
Has the wash seal been replaced in the last 6 months?
✅ Quick Test: Run a blank injection. If you see ghost peaks or splitting, suspect injector contamination.
Did I flush the column with enough mobile phase before starting?
Do first injections behave differently from later ones?
✅ Quick Test: Run 2–3 diluent-only injections before injecting sample; observe if peak shape improves.
🧠 The Chemist’s Thought Process: Step-by-Step Troubleshooting
Step
Question to Ask
Quick Test or Action
1
Is pH far enough from Vitamin C’s pKa?
Measure final mobile phase pH
2
Is buffer concentration and salt type consistent?
Prepare fresh buffer batch and compare
3
Is sample solvent matched to mobile phase?
Re-dissolve sample in mobile phase
4
Could injector/seal contamination be involved?
Run blank and observe ghost/split peaks
5
Was column properly conditioned?
Pre-run multiple blank injections
🧰 Actionable Fixes to Stabilize Vitamin C Assays
✅ 1. Set mobile phase pH around 2.5–3.0
Keep Vitamin C fully protonated and stable.
✅ 2. Use ≥10 mM phosphate buffer, specify salt form
Consistency matters.
✅ 3. Match sample solvent to mobile phase
No surprises at injection.
✅ 4. Refresh injector cleaning schedule
Clean injector needle and replace wash seals if needed.
✅ 5. Condition the column before sample runs
Always start with mobile phase equilibration.
Why Buffer Choice Matters: Diagnosing and Preventing pH Drift in RP-HPLC
April 25, 2025 · Chemistry
Key Takeaways
Buffers are essential when separating ionic compounds—but they’re not foolproof.
Even when your SOP is followed, retention drift, peak shape changes, and column degradation can still occur.
The #1 overlooked culprit? Subtle (but critical) pH instability in real mobile phase conditions—not just what your buffer says on paper.
Knowing when, where, and why your pH shifts can help you stop chasing ghosts and start stabilizing your methods.
What This Post Will Cover
The common pH-related traps when using buffered eluents
Why retention time shifts even when your method seems “locked”
What buffer strength, pKa proximity, and silica chemistry all have in common
How to catch pH drift early (and cheaply)
A list of no-nonsense steps to tighten your method’s stability
The Punchline: Buffered Doesn’t Always Mean Stable
We use buffers to “lock in” the pH and control the ionization state of our analytes—but that control is fragile.
You could have:
The right buffer but the wrong strength
A buffer that’s perfect in water, but drifts in high methanol or acetonitrile
A mismatch between column surface chemistry and the mobile phase
An analyte running too close to its pKa, causing it to fluctuate in and out of forms
And what shows up on your chromatogram?
Peaks tail
Retention drifts
Reproducibility drops
Your column dies early
Common Buffer Pitfalls in Ionic Separations
1. Wrong buffer for the pH range
Example: Using phosphate buffer near pH 5, which is at the edge of its useful range
2. Too weak a buffer
Using 5 mM phosphate to separate strong bases? Not enough buffering capacity—go 10–20 mM
3. Operating near the analyte’s pKa
If your mobile phase is within ±1 pH unit of your analyte’s pKa, it may be partially ionized → unstable retention, distorted peaks
4. Unspecified hydration states of buffer salts
Sodium phosphate monobasic comes in multiple hydrate forms → ionic strength varies unless SOP specifies the exact type
5. Mismatch between silica and mobile phase pH
Example: Column base silica pH = 3.5, but eluent is pH 7.5 → column aging, instability, tailing
6. Organic solvents shift pH upward
A buffer at pH 7.6 in water becomes 8.4 with 70% MeOH
A/B solvent pH mismatch = unintended pH gradients, especially in gradient methods
How to Diagnose It: The 3-Point pH Check
To find out if pH drift is the real problem, measure:
pH of the final mobile phase, after adding MeOH/ACN
pH after standing for 2, 8, or 24 hours
pH of the eluate post-column
If these three pH values differ, your method is vulnerable to pH-based instability.
Actionable Steps to Stabilize Your Method
✅ 1. Choose a buffer that fits your pH range
Phosphate: good for pH 2–7.5
Acetate: pH 3.8–5.8
Ammonium bicarbonate: pH 6.8–8.5 (but volatile)
✅ 2. Strength matters: use ≥10 mM for ionic analytes
Especially if you’re separating strong acids or bases
✅ 3. Avoid working too close to your analyte’s pKa
Keep your mobile phase at least 1.5–2 units above or below pKa for fully ionized/neutral form
✅ 4. Always specify the exact salt form
Include hydrate state in your SOPs (e.g., NaH₂PO₄·2H₂O)
✅ 5. Check your silica compatibility
Don’t use old-school silica (pH limit 2–7.5) with borderline mobile phases
Use hybrid or polymeric columns for pH extremes
✅ 6. Validate pH stability before trusting your method
Run the 3-point pH check
Reassess after method sits overnight or through long gradients
✅ 7. Use isocratic recycling (if applicable)
Helps conserve buffer and stabilize retention over long runs
The pH Shift: Why Your Column Misbehaves in High Organic
April 23, 2025 · Chemistry
Main Takeaways
The pH of your mobile phase isn’t fixed—it changes dramatically as you increase methanol or acetonitrile.
Even if your aqueous buffer reads pH 7.5, the true pH in 80% MeOH could be 8.3–8.5.
This subtle shift can wreck your separation:
Longer retention for bases
Peak tailing from activated silanol groups
Method instability
Shorter column life
You can’t just trust the buffer pH—you have to think about how it behaves in organic.
What This Post Will Unpack
Why pH shifts upward with organic solvent
What happens to bases and silanols when it does
Why your peaks tail, your runtime increases, and your column ages fast
What you can do to predict and prevent this shift
A visual breakdown of what’s really happening in the column
The Punchline: The Method Didn’t Fail – The Chemistry Shifted
You thought your buffer was stable. You measured pH 7.5. Everything was going smoothly—until you cranked the MeOH to 80% for a faster run and suddenly:
Retention times jumped
Peaks went ugly
Your UV signals started looking “off”
And your once-trusty column started misbehaving
But it wasn’t you—it was the solvent effect. The moment you crossed ~30% methanol, your mobile phase started creeping alkaline. By 70–80%, it wasn’t pH 7.5 anymore—it was acting like pH 8.5.
Why That Matters So Much
Basic analytes are now neutral → they interact more with the C18 → longer retention
Silanol groups on the stationary phase become negatively charged (Si–O⁻) → they grab onto basic compounds → peak tailing
Column degradation kicks in faster silica doesn’t like life above pH 7.5
UV response may shift because ionization affects absorbance
This is why you can do everything “by the book” and still watch your method fall apart.
How to Outsmart It
Never trust the aqueous pH alone—always consider the final % of organic in your mobile phase.
If you’re going above 30% MeOH or ACN, assume pH shift and plan accordingly.
For high-organic gradients:
Use high-pH-stable columns
Add silanol blockers like triethylamine
Or switch to zwitterionic or polymeric phases if needed
You can even measure pH post-column to see what’s really happening.
Visual Breakdown: How Organic Solvent Quietly Changes Everything
The diagram below shows:
The rising pH with increasing organic solvent (red line)
What happens to a basic analyte as pH increases (top-left→bottom-left)
How silanol activation starts to grab onto analytes and distort your peaks
Actionable Steps for Chromatographers: Managing pH Shifts in High Organic
If you’re working with buffered mobile phases in RP-HPLC and using more than ~30% organic solvent (especially MeOH or ACN), here’s how to stay ahead of the curve:
✅ 1. Don’t assume the aqueous pH is your final pH
Measure or model the true pH of your mobile phase after adding organic.
Remember: a phosphate buffer at pH 7.0 in water can behave like pH 8.3 at 80% methanol.
✅ 2. Watch how your analytes change form
Know the pKa of your compound.
Higher pH = more analytes in neutral (hydrophobic) form → longer retention.
This is especially important for basic compounds.
✅ 3. Plan for silanol activation
At high pH, silanol groups deprotonate and interact with basic analytes → tailing.
Use:
Triethylamine or other silanol suppressors
Endcapped or hybrid columns that are more stable at higher pH
✅ 4. Check column compatibility
Make sure your stationary phase can handle pH shifts—standard silica degrades faster above pH 7.5.
If you’re working in that range often, invest in a high-pH stable column.
✅ 5. Validate changes before finalizing a method
Anytime you modify the organic content significantly, re-check retention, peak shape, and resolution.
Why pH Matters in RP-HPLC: The Hidden Lever Behind Retention and Peak Shape
April 22, 2025 · Chemistry
Key Takeaways (For the Fast Readers)
Mobile phase pH isn’t just a detail—it can completely change how your analyte behaves.
Acidic conditions tame weak bases and silanol groups, improving peak shape.
Strong bases? They’ll run wild unless you raise the pH and neutralize them.
Silanol groups can be sneaky—use modifiers like triethylamine to keep them from hijacking your peaks.
What This Post Will Unpack
Why pH is a powerful control knob in RP-HPLC
What happens to acids vs. bases across the pH scale
How silanol groups on your column might be sabotaging your method
Two real-world examples: one weak base, one strong base
A simple comparison table you’ll want to screenshot
The Big Picture: How pH Impacts Retention
Picture your analyte navigating a hallway. The walls? That’s your stationary phase (non-polar C18). The air around it? That’s your mobile phase. If the analyte is charged, it floats in the air (mobile phase) and speeds through. If it’s neutral, it sticks to the walls, slows down, and you see retention.
At low pH, bases are protonated—they become polar and prefer the mobile phase. At high pH, acids deprotonate and do the same. Neutral compounds, meanwhile, grip the C18 chains and hang out longer.
So, retention is not just about size or structure—it’s about electrical personality in the mobile phase.
Example 1: Glucosamine HCl (A Well-Behaved Weak Base)
Imagine you’re tasked with analyzing a glucosamine supplement. Glucosamine is a small, polar molecule with an amine group (pKa ~7.5). Here’s the trick:
At low pH (~2–3), it stays protonated (charged), loves the mobile phase, and flies right through the column.
That’s actually a good thing—it gives you sharp, clean peaks with no tailing, especially if you throw in TFA or a phosphate buffer.
Bottom line: Low pH = fast, clean elution. No drama. Ideal for high-throughput QC labs.
Example 2: Albuterol (A Strong Base with an Attitude)
Now let’s take on albuterol sulfate—a strong base used in bronchodilators.
At low pH, it’s fully charged and wants nothing to do with the C18 phase.
So, it elutes too quickly, with poor retention and resolution.
Push the pH up to 8–9, and now it’s mostly neutral.
It finally sticks to the column—but wait! Now silanol groups (weak acids) on the stationary phase are negatively charged (deprotonate) and trying to bond with it.
To fix that, we add triethylamine to mask the silanols or use end-capped/hybrid columns to stop the tailing.
Quick Comparison: Weak vs. Strong Bases
Feature
Weak Base (e.g., Glucosamine)
Strong Base (e.g., Albuterol)
Ionization at low pH
Partially protonated
Fully protonated
Retention at low pH
Moderate
Very low
Ideal mobile phase pH
Acidic (2–4)
Neutral to alkaline (7–9)
Peak shape risks
Low
High (tailing at high pH)
Additives
TFA, phosphate
Triethylamine, ammonia
Case Study: How I Predicted Glucosamine’s Behavior Before Even Touching the Column
When I sat down to optimize an HPLC method for glucosamine HCl, I didn’t just guess a pH and hope for the best. I let the molecule tell me what it needed.
First, I looked at the structure: glucose based amino sugar (lots of polarity from OH) and a primary amine that gives it weak basicity. That told me two things:
It’s hydrophilic—not exactly eager to stick to a non-polar C18 chain.
The amine group has a pKa of about 7.5, which meant I could predict its charge across a pH range.
Then I played out the following scenarios:
At pH 2–3, well below the pKa, the amine is fully protonated (–NH₃⁺), meaning glucosamine is very polar and charged. That’s perfect if I want it to whiz through the column and avoid silanol interaction. Great for peak symmetry. The downside? Retention will be short.
At pH ~7.5, I knew it would be about 50% ionized and 50% neutral. That neutral portion might start interacting with the stationary phase, slowing it down. But the ionized half could still tangle with any active silanol groups, leading to tailing if I wasn’t careful.
At pH 9+, it becomes mostly neutral—so it sticks better to the C18. Sounds good, right? But now the silanol groups on the column are negatively charged, and they love grabbing onto basic amines. That could lead to the dreaded peak tailing unless I use triethylamine or an end-capped column.
So, I asked myself: What do I need from this method?
Fast runtime
Symmetrical peak
No silanol drama
My answer? Go acidic. I chose a phosphate buffer at pH ~2.5, added a splash of acetonitrile, and let it run. Glucosamine eluted cleanly in about 2.5 minutes, peak as sharp as my pre-run hypothesis.
This wasn’t luck. It was understanding how ionization, pKa, and surface chemistry dance together in reversed-phase HPLC—and using that dance to choreograph a smooth method.
What’s Really Driving All This? pKa and Silanols
Let’s zoom in on what’s actually controlling the show: pKa and those sneaky silanol groups on the column.
pKa: The Charge Switch
Think of pKa as your molecule’s personal tipping point—the pH where it’s 50% ionized, 50% neutral. Knowing the pKa of your analyte lets you predict:
When it will be charged (likes the mobile phase → faster elution)
When it will be neutral (likes the stationary phase → more retention)
In our examples:
Glucosamine has a pKa ~7.5. So at pH 2–3, it’s fully charged and zips through the column.
Albuterol has a pKa ~9.3 (secondary amine), so it only becomes neutral around pH 8–9—when it starts sticking to the C18.
Silanol Groups: The Hidden Troublemakers
Now flip the lens to your column. Silica-based columns have residual silanol groups (Si–OH). These act like weak acids:
At low pH, they’re protonated (neutral)—harmless.
Above pH 4–5, they start deprotonating into Si–O⁻, becoming negatively charged.
These Si–O⁻ groups love interacting with basic analytes—especially if they’re partially charged or polar—even at high pH.
That’s why in the albuterol case, even though the compound is neutral at high pH, the silanols start grabbing it, leading to tailing unless you block them (e.g., with triethylamine).
ICP-OES Troubleshooting Guide
April 22, 2024 · Chemistry
In the sphere of analytical instrumentation, effective communication is key, and the language of instruments serves as a signal. For chemists, ensuring that the signal received from the ICP-OES instrument is clear, consistent, and concise becomes an utter priority. The sensitivity of the signal must be finely tuned to be easily recognized, precision should be consistently maintained, and accuracy is non-negotiable. In the world of ICP-OES, challenges arise when sensitivity falters, precision becomes inconsistent, the instrument fails to produce accurate results, and the plasma extinguishes too quickly.
In this installment, we will delve into the most common problems faced in ICP-OES operation and explore effective techniques to ensure seamless communication with this analytical tool.
As an ICP-OES chemist, I find myself encountering four distinct categories of challenges, each presenting its own set of hurdles in the quest for seamless results. Join me as we navigate the realms of plasma, enhancing sensitivity, ensuring precision, and achieving the goal of accuracy.
Although ICP-OES method development has been improved by advances in detector versatility and instrumentation, problems still arise. In this installment I will walk you through a systematic means of isolating, identifying, and correcting many typical ICP-OES problems.
Unlock the full potential of your ICP-OES instrument with the four key considerations that can impact your analyses.
Sensitivity (signal/concentration)
It is the ability to detect and measure the levels of analyte in relation to their concentration in the sample.
It determines the instrument’s ability to detect and quantify trace levels of elements in a sample. Optimizing for optimal sensitivity allows for the detection of elements present at lower concentrations, enabling for accurate analysis. Additionally, sensitivity directly impacts the instrument’s detection limits, precision, and the ability to differentiate between elements and background noise.
Sensitivity is influenced by 4 factors:
Sample introduction system
Method parameters
Cleanliness
Quality of standards used for calibration
Use Table 1 to determine which component may be causing the problem. By systematically eliminating potential causes, you can effectively identify the specific issue and take the necessary steps to resolve it.
Precision:
Ever wondered why your results sometimes go haywire? Well, here’s a little secret: about 80% of the time, it’s the mischievous sample introduction system causing all the trouble! Just like a mischievous prankster sneaking into your lab, this system can wreak havoc on your precision by introducing all sorts of unexpected variations.
Precision is the closeness of results from the same homogeneous sample under the same conditions. It is measured as %RSD. For ICP-OES, 2 % RSD is expected. Poor RSD or noisy signal is an indication of poor precision during analysis.
Why precision is a good metric to track?
It is a good indicator for confidence in the system.
Use Table 2 for some direction as to problems and potential solutions.
Accuracy
Imagine a pharmaceutical researcher formulating a life-saving medication. In this process, even the slightest deviation in chemical composition could render the drug ineffective or, worse, harmful to patients. This example underscores why accuracy is paramount in metrology. Every measurement, every reaction, hinges on the ability to reliably determine the composition and properties of substances. Without accuracy, potentially jeopardizing the safety and efficacy of countless pharmaceuticals, environmental assessments, and industrial processes.
Accuracy is the closeness of the measured value to the true value.
Accuracy can be confirmed by:
Using a certified reference material (CRM/SRM)
Using a QC checks, CCV
Use table 3 on the most common causes of poor accuracy and their potential solution.
Drift
One of the important parameters that ensures streamlined analysis is instrument performance stability in producing consistent signal. Fluctuation in producing sustainable signal is what refers to as instrument drift.
Drift is the gradual change in instrument performance over time. It can be displayed as shifts in signal intensity, baseline instability, or variations in analytical sensitivity.
What factors can cause instrument drift?
Change in temperature, fluctuations in gas flow rates, wear and tear in the uptake tubing, not enough time for the sample uptake to reach the plasma and for the signal to stabilize, nebulizer clogging, not so often if the spectrometer was turned off, or there is electronic issue.
Why is monitoring and correcting for instrument drift important?
It is essential for maintaining the accuracy and reliability of analytical results, as even minor fluctuations can impact the precision and reproducibility of measurements.
Use table 4 on the most common causes of drift and their potential solution.
QC Failure
It’s the check standard verification time in the lab. The prepared check standard solutions are analyzed under the same conditions as the samples of interest. It’s like a chemistry detective mission – we’re comparing the concentrations of elements we measure to what we know they should be. This helps us make sure our instrument is operating within the acceptable limits and that the calibration curve is functionable within its working range.
Check standards are prepared solutions containing known concentrations of target elements. They are used to verify the accuracy of the instrument’s measurements and to monitor its performance over time.
Check standard verification is one important metric that provides information on the accuracy and reliability of the analytical results by validating the performance of the instrument and the calibration curve.
In regulated labs, it’s a must to hit the brakes on analysis if our check standard verification doesn’t cut the mustard. Usually, when our check standard flops, it’s because our blank’s got some unwelcome visitors – contamination, anyone? Now, here’s where the chemist detective hat comes on: we got to ask ourselves a couple of key questions. First off, is every single element failing, or is it just a select few? And secondly, if they are failing, are they all off by the same amount? If only some elements are acting up, it might be down to how we prepped our samples or if certain elements are throwing a tantrum in certain pH environments (acidic ones, especially). But if everything’s off by the same degree, we might have an issue with our internal standards. And if some elements are being a bit unpredictable with their results, it could be related to a stability issue that can be caused by improper sample uptake flow or insufficient read delay time.
Example 1:
Example 2:
The above two examples are what we don’t like to see in the lab – QC Failed. Let’s examine the cause of each one. But first few questions to ask yourself:
The quality and the speed of your solution depends on the quality of the questions you’re going to ask yourself to solve the issue.
Has the signal of Mn alignment solution changed from the previous day? No, good.
Has the signal of the analytes changed from the previous run, and were you able to build a good calibration curve? Yes, good.
Is the QC fails with all elements or a few?
What is the RSD of the replicate readings?
What is the chemistry of the element of interest and its matrix?
Is the peak or background correction BGC point position set correctly?
Is the correct wavelength being used?
Is the method parameters set correctly?
Example 1: shows that K has a QC value above the upper limit. This could be broadly grouped into 3 categories: 1) the true K concentration in QC is above the upper limit due to incorrect preparation, or 2) the true K concentration is within the control limits, but the 766.490 emission line result is not accurate due to potential interferents, 3) stability issue.
For example, checking the RSD shows that K 466.490 has an RSD of 8.12%, Commonly expected RSD between 1 -2 %. This could be due to sample read/delay issue, not sufficient time to flush the system.
Example 2: shows that K has a QC value below the lower limit. The RSD is within the allowable range. therefore, precision issue is excluded. This could be a challenge when testing alkali metals using ICP-OES, as low recoveries can occur as they exhibit low spectral intensity. Addition of ionization buffer such as Cs could help in suppressing neighboring spectra and allowing for accurate estimation.
The following example illustrates how signal can be trending and the possible causes for that:
Replicate
Intensity A
Intensity B
Replicate_1
460,000
535,000
Replicate_2
489,000
489,000
Replicate_3
535,000
460,000
As you can see in the above table:
Increasing intensity with time indicates sample uptake/read delay times are insufficient. Sample reading is determined too quickly or taking longer to stabilize due to matric effect.
Decreasing intensity with time indicates previous sample may still be washing out.
Use table 5 on the most common causes of check standard failure and their potential solution.
Plasma
Plasma is the heart of the ICP, due to its paramount role in in excitation and ionization of the sample. Ensuring the plasma generation all the time is utmost importance for analysts. The following displays the most common causalities of plasma disturbance and how to resolve them.
Use table 6 on the most common causes of check standard failure and their potential solution.
The ICP-OES uses a high-temperature plasma torch to atomize and ionize the sample, allowing the analysis of its elemental composition.
Why does a torch melt in ICP-OES?
Melting of the torch mostly happens during the ignition step. However, it can also occur due to excessive heat generated during the analysis process.
Use Table 7 to determine the common causes for the torch material to melt, with the remedial action.
Understanding the key areas of your system configuration is like having a roadmap to success. The sample introduction system is the beating heart of the instrument, orchestrating a delicate dance between various components. To troubleshoot effectively, it’s crucial to grasp how these components interact and where to focus your attention. As a general rule of thumb, 80% of ICP problems rotates around sample introduction, while drift and signal suppression tend to lurk, closer to the injector tube and torch.
Lastly, the goal of troubleshooting is not just to fix problems, but to create systems that are robust and resilient to issues. Prevention is the key to minimizing the occurrence of problems for ICP-OES. The following table provides a comprehensive system maintenance plan for ICP-OES.
Maintenance Task
Frequency
Description
Flush Sample Introduction
Daily
Flush the sample introduction with 2% HNO3
Clean Nebulizer
Weekly
Use fill Eluo neb cleaner with methanol or 2% HNO3 to remove any build ups
Replace Consumables
As needed
E.g. tubings, o-rings, torch parts, as they wear or become damaged
Clean Injector
Monthly
Rinse with 2% HNO3, or a surfactant, and rinse with DI H2O. Place in the oven for 10 minutes
Clean Radial and Axial surfaces
Annually
Clean lenses to maintain signal clarity
Inspect Torch Components
Quarterly
Inspect torch components for wear, damage, or contamination
Check Plasma Stability
Monthly
Monitor plasma stability and torch condition for consistent performance
Mastering the Art of Parenting: A Deep Dive into No-Drama Discipline
January 1, 2024 · The Reading Room
In the US, roughly 16% of children experience some form of abuse, including physical, emotional, and sexual abuse.
18% of parents have a permissive parenting style which meets the clinical definition of bad parenting.
Harsh parenting is associated with lower-control and higher aggressive attitude in children. Adolescents who have less self-control and stronger aggressive attitudes are 26.5 times more likely to commit delinquency than those who don’t have self-control issues and don’t hold aggressive attitudes.
In the intricate dance of life, the mere act of bringing a child into the world does not automatically confer the title of a parent any more than owing a guitar transforms someone into a skilled musician. The journey of parenthood demands a nuanced blend of training, adjustment, and relentless practice to truly master its intricate art.
This realization gains further weight as health professionals, their insight echoed in a poignant article from the BMJ.com, emphasize the paramount significance of parenting in the realm of child health. The sentiment is resounding – parenting stands as the single most crucial public health issue confronting our society today. It carries with it not just a personal weight but a collective moral responsibility to ensure a wholesome upbringing for the next generation.
Yet, as profound as this responsibility is, the path of parenting unfolds without the luxury of a comprehensive manual.
In a world often saturated with rigid structures and authoritarian regimes, comes Elena the grandma as she was known in her village. She was renowned for her unconventional yet effective approach for parenting. The town, steeped in tradition, often marveled at how her children thrived and flourished despite the absence of stringent rules. Elena’s philosophy was encapsulated in a simple yet profound quote, “Who disciplines the least disciplines the most.” It was a saying whispered through the rustling leaves and echoed in the laughter of children playing in the town square.
Elena’s home was not a fortress of strict rules and stern directives. Instead, it was a haven of warmth and understanding. She believed that the key to guiding her children lay in fostering an environment of open communication and mutual respect. Rather than imposing her will upon them, she gently guided them toward understanding the consequences of their actions.
One day, as the townsfolk observed Elena’s seemingly carefree approach, a challenge arose. The townspeople, accustomed to traditional parenting norms, questioned the efficacy of Elena’s methods. They couldn’t fathom how a lack of strict discipline could lead to well-behaved and responsible children.
Undeterred, Elena continued her journey, demonstrating that discipline need not be synonymous with punishment. Instead, it could be a dance of guidance and compassion. Her children, buoyed by this approach, learned to make decisions not out of fear but from a place of intrinsic understanding.
As years passed, Elena’s children became pillars of the community. They exuded kindness, empathy, and a deep sense of responsibility. The townsfolk, once skeptical, began to realize the profound truth behind Elena’s quote. In discipling the least, she had, in fact, instilled in her children the most enduring virtues.
The story of Elena and her children became a beacon of inspiration for the town. Parents began to reevaluate their own approaches, embracing a more compassionate and understanding form of discipline. In doing so, they discovered that the true essence of discipline lay not in rigid rules but in the nurturing of resilient, compassionate, and responsible individuals.
And so, in the heart of that quaint town, the legacy of Elena’s wisdom lived on – a testament to the transformative power of parenting guided by love, understanding, and the belief that sometimes, less is more.
Navigating the twists and turns, highs and lows, is a journey fraught with uncertainties. However, amid this complex landscape, a beacon of guidance emerges – the No-Drama Discipline. Positioned as a groundbreaking guide, it introduces a paradigm shift in the world of effective and compassionate discipline. Its influence extends to reshape the very foundations of how parents and caregivers comprehend and respond to the intricate tapestry of their children’s behavior, marking a transformative milestone in the evolving narrative of parenthood.
The three key principles of the book:
Connect before you redirect.
Teach, Don’t Punish.
Use Mistakes as Opportunities for Growth.
Connect Before you Redirect:
This principle underscores the importance of building a strong, emotional connection with your child before addressing their behavior. By connecting first, you create an environment of trust and safety. This connection can be established through empathy, active listening, and validating the child’s emotions.
Teach, Don’t Punish:
Instead of resorting to punitive measures, the book encourages parents to view discipline as an opportunity for teaching. Understanding the neuroscience behind a child’s behavior is crucial. Parents are encouraged to explain and teach appropriate behavior, fostering a deeper understanding of the consequences of actions. This approach aims to promote learning and growth rather than instilling fear or shame.
Mistakes are Opportunities for Growth:
The book promotes a positive view of mistakes, considering them as opportunities for learning and growth. Instead of focusing solely on correcting the behavior, the authors suggest discussing the mistake with the child, exploring what happened and why. This approach encourages problem-solving and helps the child develop self-awareness and resilience.These three key points form the foundation of a discipline strategy that prioritizes the child’s emotional well-being, strengthens the parent-child relationship, and promotes a positive environment for learning and development. It encourages parents to navigate the intricate landscape of discipline with a discretionary blend of leniency and guidance. In the confrontation between the authoritarian versus relationship power. Relationship power always proves efficacy in parenting. A healthy degree of 90:10 / Relational : Authoritative should be the target.
The Illusion of Confidence: Understanding the Dunning-Kruger Effect
April 23, 2023 · The Reading Room
Have you ever had that exciting feeling that fresh graduates get when they think they totally nailed an interview that required more skills than they have as new grads? But then, months go by and you never hear back from the employer for the job?
You are not alone, I felt that too.
In an attempt to understand why some fresh graduates think they are amazing and desperately need to be hired, even for positions they might not be ready for, while more experienced seniors are often less certain about their abilities despite having higher competencies? Well, I decided to look into one of the reasons behind this phenomenon. It turns out that this feeling of superior competency among new graduates, despite their actual level of skill, could be due to something called the Dunning-Kruger effect. This effect can cause people to overestimate their worth in the job market, even when they don’t have the necessary competencies to back it up.
Why this happens? Why people with less competencies seems to have superior thoughts about the competencies they possess that is not of true representation of their true market value and are fixated on their own current state and not seeing the gaps that needs to be improved?
This is what we are going to discuss in this blog post. When we start on something since, we are aware that we have no knowledge, our perceived knowledge in a way aligns with how much knowledge we have, which is non. But pretty quickly the perceived knowledge skyrockets, usually after 6 – 12 months of being involved in a particular subject. Until an inflection point happens. Even though you are learning more your “actual knowledge” going up but your perception of the knowledge starts going down. As you found contradicting data that challenges your knowledge and then you realize that you want to learn more. This is called Dunning-Kruger effect.
Dunning-Kruger effect is a form of cognitive bias that models the behavioral characteristics in social settings, between perceived knowledge and actual Knowledge, whereby people with limited knowledge in a specific realm tend to hyperbolically pronounce their own knowledge in that realm. The effect was first described in a seminal 1999 paper by the two psychologists David Dunning and Justin Kruger.
The linchpin of the Dunning-Kroger effect is self-awareness. People who don’t have much knowledge about a subject tends to omit the gaps in their knowledge thinking that they are well rounded on the subject.
On the contrary, people who have expertise level of a subject don’t exode an inflated attitude of knowing everything about the subject. As you drink from the fountain of knowledge, you will be thirsty for the ignorance you once had, the more knowledge you acquire, the less the certainty and absolute your talks become and more caveating things as you became very aware of how little you know about something.
The effect is about how we should be humble and aware that true knowledge exists in knowing we know nothing.
How to avoid the Dunning-Kruger effect?
Unlike the misconception that Dunning-Kruger effect is pointing downwardly at people, it is in fact a great psychological tool for oneself as it could be you used as the brakes that slows down the inflated sense of “I know everything” that is associated with surface level knowledge. It helps in creating a cognitive restraint frame of mind when the false sense of I know everything kicks in which in turn paves the way for humbleness.
The following are practical pointers that can be adopted to help avoiding the false sense of superiority in knowledge:
Take an inventory of yourself and examine how do you operate in life; on probability or possibility. The former allows for growth me Ask for genuine feedbacks about my own competencies, reflect on the things that shows to
Ask for genuine feedback about your own competencies on the specific subject matter.
Reflect and examine whether your level of delivery matches your level promoting. That cultivates self-awareness about your true level of expertise. Typically, the less we know about a subject the more we think we know about it.
Lastly, as Socrates used to say, “the only true wisdom is in knowing you know nothing.” Always remember approach things from humbleness as this allows a room for growth, when think of yourself as you don’t know enough you will go and learn more and become better. If you think you know enough that’s when you become like stagnant creek where all creeps in.
From Red to Green: Lessons to Inspire your financial Journey
April 18, 2023 · The Reading Room
The best way to measure your investing success is not by whether you’re beating the market but by whether you’ve put in place a financial plan and a behavioral discipline that are likely to get you where you want to go. – Benjamin Graham
I am appalled by the fear that my daughter may lose her way in a quick rich scheme world that values monetary supply for what it can buy and not for the good it can achieve. An impatient world that promotes buy now pay later policy. That slowly rewires the brain chemistry to value instance gratification and denounces the value of effort.
In the confrontation between the wealthy and the perceived rich, the wealthy always wins because they bank on the value of consistent effort. The wealthy are perspicuous that investing is not the study of finances, it is how people behave with money on the long-term. I think most of us despite where we are in the socioeconomic hierarchy wants to explore different realms to better our lives and live widely. One of the tools to achieve that is through investing. It comes in different shapes. Essentially, it is the proliferation of profit from profit while – enjoying boredom!!! The purpose of this post is not to tell you what to invest in, rather, to share with you my favorite 10 curated snippets from my mentors in the financial sphere.
Save like a pessimist, invest like an optimist
Investing can be boring at times, so operating on the concept of pragmatical pessimism can be helpful during the hiccups. Essentially, one needs to be hopeful for the best and prepared for the worst shall it arise. Save with the idea that the world breaks every few years — a recession, a pandemic, a terrorist attack. These events rock the economy and crush the stock market. It’s been like that forever and it’ll be like that forever. – Morgan Housel.
Marry the right person
Whether you like to believe it or not, money is a big part of a relationship. People have grown up on different money values and can see it very differently. To avoid any conflict down the road, both have to align themselves with common money rules. – Ramit Sethi
Generate multiple streams of income
Never depend on single income. Make investment to create a second source. – Warren Buffet.
When there’s blood in the street that’s when you go to invest
People want quick results. They want to brag about their stock that tripled or their fund that beat the S&P. Letting an Index work its magic over the years isn’t very exciting. It is only very profitable. – JL Collins (https://jlcollinsnh.com/2012/01/06/index-funds/)
Real estate is a prominent asset class in your portfolio
Great returns – How would you like to make 10% on your money plus appreciation? It can be done. Pretty good business with low hours – let’s say you owned a business that made $60K per year. How many hours do you think you’d have to work a week? It would be a ton. As I said, I spend about two to three hours max a month on my real estate investments. If we figured my hourly earnings over the life of my properties, it’s probably astronomical. – ESI Money (https://esimoney.com/why-you-should-invest-in-real-estate/)
Save your money
The main focus on financial samurai is to achieve greater happiness through financial independence. We need to do more of what makes us happy, and less of what makes us sad. Based on my experience earning $3.65hour flipping burgers at McDonald’s to making much more than the President of the United states during my time on Wall St., I absolutely believe that $200,000 a year is the ideal income for maximum happiness. The one enigma I’ve been dealing with for the longest time is wondering what the hell is wrong with me for continuing to want to save so much. How much does someone really need after counting for all the basic necessities for survival, especially if there’s already a decent flow of passive income? I then stumbled across a survey by ally bank which really made a lot of sense. Their conclusion based on more than 1000 people is simple: the more you save, the more likely you are to be happy. But what’s more interesting is that saving money affects happiness more than how much you earn. – FinancialSamurai (https://www.financialsamurai.com/they-key-to-happiness-is-saving-more-not-making-more-money/)
Prioritize things to a happier journey
You can do (have) anything, but not everything. – David Allen Our time is limited, so having an unquenchable desire to joggle so many things at once will be like chasing two rabbits at once. There’s a trade-off for anything you gain, learn the dynamics of prioritizing things. Part of the game – accept it!
Make more money at your job
I once worked for a doctor who paid a cleaning company to come in once a week and clean the office and exam rooms. I approached her and asked if she would pay me instead. So, once a week I would stay for a few hours after hours and clean the office. It was a decent amount of money and really helped to pad my bank account. If you take on more responsibility, make sure it is clear you want to be compensated for it. – Free money finance (https://www.freemoneyfinance.com/2017/07/how-to-make-more-money-at-your-job.html)
Time in the market Vs. Timing the market
Time in the markets is the most precious commodity when it comes to investing. By leaving money in the markets to grow, the initial account contributions can multiply. Keep the money invested for a shorter period and there’s less time for the sum to compound. Even if you choose to expand into other investing, like p2p lending with lending club or with Motif Investing make sure you have time on your side when you are doing it. Decide whether you are willing to make a tradeoff. You can’t have everything now and later. Ask yourself if you’re willing to sacrifice a bit now for the likelihood of having more later. – Good financial cents (https://www.goodfinancialcents.com/why-you-must-start-investing-now/)
Bonus Point:
The linchpin of achieving financial independence is to be agnostic, putting your emotions aside can save you a fortune. When you’re in the FOMO phase you take incalculable greater risks (house money effect), or perhaps you resist recalibrating your portfolio hoping that one day you will make your money from your meme stocks (sunk cost fallacy).
In conclusion, I would like to leave you with the words of the financial expert Dave Ramsey as he argues that attaining financial independence is more about psychology than money, with 80% being attributed to the former and only 20% to the latter. It is up to us whether to utilize our money, for external purposes or prioritize our own personal comfort. Our perception of the value of money influences the decisions we make.
