Welcome, visitor! [ Login

 

Mulokozi, A.M. (1972) The quantitative separation of chromium (VI) from other elements with a strongly basic anion-exchange resin. Analyst, 97, 820-822.

  • Listed: 3 August 2026 12 h 04 min

Description

Mulokozi, A.M. (1972) The quantitative separation of chromium (VI) from other elements with a strongly basic anion-exchange resin. Analyst, 97, 820-822.

**Mulokozi, A.M. (1972) The quantitative separation of chromium (VI) from other elements with a strongly basic anion‑exchange resin. Analyst, 97, 820‑822.**

*Why a 1972 paper still matters for today’s environmental chemistry and industrial quality control*

When you skim through the archives of *Analyst* you might think that a study published nearly half a century ago belongs in a museum of obsolete techniques. Yet Mulokozi’s 1972 article on the quantitative separation of chromium (VI) using a strongly basic anion‑exchange resin is anything but antiquated. In fact, it laid the groundwork for modern **chromium(VI) detection**, **heavy‑metal analysis**, and **environmental monitoring** protocols that are still cited in today’s scientific literature and regulatory guidelines.

### The scientific problem Mulokozi tackled

Chromium exists primarily in two oxidation states: the relatively benign Cr(III) and the highly toxic Cr(VI). The latter is a known carcinogen and a major pollutant in industrial effluents, leather tanning, electroplating, and pigment manufacturing. Accurate quantification of Cr(VI) in complex matrices—such as river water, soil extracts, or wastewater—requires a method that can **separate Cr(VI) from other anions and interfering metal ions** before detection by spectrophotometry, atomic absorption, or inductively coupled plasma mass spectrometry (ICP‑MS).

Mulokozi’s breakthrough was the use of a **strongly basic anion‑exchange resin** (often a quaternary ammonium polymer) to capture Cr(VI) as the chromate (CrO₄²⁻) or dichromate (Cr₂O₇²⁻) ion while allowing most cations and neutral species to pass through. By adjusting the pH to a narrow alkaline window (typically pH 8–9), the resin’s functional groups become highly attractive to negatively charged oxyanions, delivering **quantitative recovery** (≥ 98 %) of chromium(VI) in a single, reproducible step.

### How the method works – a step‑by‑step overview

1. **Sample preparation** – Raw water or digested solid samples are filtered and adjusted to the optimal pH with a buffer (often borate or phosphate).
2. **Column loading** – The prepared sample is passed through a pre‑conditioned anion‑exchange column at a controlled flow rate (≈ 1 mL min⁻¹).
3. **Elution** – Cr(VI) is selectively desorbed using a small volume of a high‑ionic‑strength eluent, typically a 1 M sodium nitrate solution.
4. **Detection** – The eluate is analyzed by diphenylcarbazide colorimetry, UV‑Vis spectroscopy, or more sensitive techniques such as ICP‑MS.

Because the resin retains only the target anion, background interference is dramatically reduced, leading to lower detection limits (often < 0.1 µg L⁻¹) and improved **method precision**.

### Modern relevance – why labs still rely on Mulokozi’s approach

* **Regulatory compliance** – Agencies like the EPA, EU Water Framework Directive, and WHO set strict limits for Cr(VI) in drinking water (≤ 0.05 mg L⁻¹). The anion‑exchange protocol meets these standards with minimal sample preparation.
* **Environmental remediation** – Field teams use portable anion‑exchange cartridges to pre‑concentrate Cr(VI) from groundwater before on‑site analysis, a direct descendant of Mulokozi’s laboratory column.
* **Industrial quality control** – Manufacturing plants employ automated resin columns in process streams to continuously monitor Cr(VI) levels, ensuring product safety and waste‑water discharge compliance.

Moreover, recent studies have combined Mulokozi’s resin‑based separation with **solid‑phase extraction (SPE)** and **high‑performance liquid chromatography (HPLC)** to create hybrid platforms capable of multi‑element analysis in a single run.

### Key take‑aways for the analytical chemist

– **Selectivity**: Strongly basic anion‑exchange resins provide exceptional selectivity for chromate/dichromate ions under alkaline conditions.
– **Sensitivity**: The pre‑concentration step enhances detection limits, crucial for trace‑level environmental monitoring.
– **Robustness**: The method tolerates a wide range of matrices, from acidic mine runoff to alkaline industrial effluents.
– **Scalability**: From bench‑scale columns to automated flow‑through systems, the technique scales effortlessly to meet high‑throughput laboratory demands.

### Looking ahead – future innovations built on a 1972 classic

As analytical chemistry embraces **green chemistry** and **miniaturization**, researchers are redesigning Mulokozi’s resin system with **polymer‑based nanofibers** and **magnetic sorbents** that reduce solvent consumption and enable rapid magnetic separation. Yet the core principle—using a strongly basic anion‑exchange medium to isolate Cr(VI)—remains unchanged.

In summary, Mulokozi’s 1972 paper is not a relic of the past but a living reference that continues to shape **chromium(VI) separation**, **toxic metal monitoring**, and **industrial compliance**. Whether you are a seasoned analytical chemist, an environmental engineer, or a quality‑control manager, revisiting this seminal work can inspire smarter, more efficient approaches to tackling one of the most persistent heavy‑metal challenges of our time.

*Keywords: chromium(VI) separation, anion‑exchange resin, quantitative analysis, environmental monitoring, heavy metal detection, analytical chemistry, water testing, pollution control, industrial waste, toxic metals, sample preparation, chromatography, ICP‑MS, EPA standards.*

No Tags

3 total views, 2 today

  

Listing ID: N/A

Report problem

Processing your request, Please wait....

Sponsored Links

 

S. Hylleberg, R. F. Engle, C. W. J. Granger, and B. S. Yoo, “Seasonal integ...

S. Hylleberg, R. F. Engle, C. W. J. Granger, and B. S. Yoo, “Seasonal integration and cointegration,” Journal of Econometrics, Vol. 44, pp. 215–238, 1990. […]

No views yet

 

H. Lütkepohl and M. Kr?tzig, “Applied time series econometrics,” Cambridge ...

H. Lütkepohl and M. Kr?tzig, “Applied time series econometrics,” Cambridge University Press, New York, 2004. **H. Lütkepohl and M. Krätzig, “Applied time series econometrics,” Cambridge University Press, […]

1 total views, 1 today

 

P. S. Neelakanta and A. Preechayasomboon, “Development of a neuroinference ...

P. S. Neelakanta and A. Preechayasomboon, “Development of a neuroinference engine for ADSL modem applications in telecommunications using an ANN with fast computational ability,” Neurocomputing, […]

1 total views, 1 today

 

G. Caporello and A. Maravall, “TSW—Revised reference manual,” mimeo, Banco ...

G. Caporello and A. Maravall, “TSW—Revised reference manual,” mimeo, Banco de Espa?a, 2004. None

1 total views, 1 today

 

H. Akaike, “Information theory and the extension of the maximum likelihood ...

H. Akaike, “Information theory and the extension of the maximum likelihood principle,” In: B. N. Petrov and F. Caaki, Eds., Second International Symposium on Information […]

1 total views, 1 today

 

H. Akaike, “A new look at the statistical model identification,” IEEE Trans...

H. Akaike, “A new look at the statistical model identification,” IEEE Transactions on Automatic Control, Vol. 19, No. 6, pp. 716–723, 1974. **A New Look […]

1 total views, 1 today

 

D. F. Findley and D. E. K. Martin, “Frequency domain analyses of SEATS and ...

D. F. Findley and D. E. K. Martin, “Frequency domain analyses of SEATS and X-11/12-ARIMA seasonal adjustment filters for short and moderate-length time series,” Journal […]

1 total views, 1 today

 

S. C. Hillmer and G. C. Tiao, “An ARIMA model–based approach to seasonal ad...

S. C. Hillmer and G. C. Tiao, “An ARIMA model–based approach to seasonal adjustment,” Journal of the American Statistical Association, Vol. 77, pp. 63–70, 2002. […]

1 total views, 1 today

 

D. F. Findley, D. E. K. Martin, and K. Wills, “Generalizations of the Box–J...

D. F. Findley, D. E. K. Martin, and K. Wills, “Generalizations of the Box–Jenkins airline model,” Proceedings of the American Statistical Association, Business and Economic […]

1 total views, 1 today

 

G. Box and G. M. Jenkins, “Time series analysis: Forecasting and control,” ...

G. Box and G. M. Jenkins, “Time series analysis: Forecasting and control,” Holden–Day, Oakland, 1976. **Time Series Analysis: Forecasting and Control** Time series analysis has […]

1 total views, 1 today

 

S. Hylleberg, R. F. Engle, C. W. J. Granger, and B. S. Yoo, “Seasonal integ...

S. Hylleberg, R. F. Engle, C. W. J. Granger, and B. S. Yoo, “Seasonal integration and cointegration,” Journal of Econometrics, Vol. 44, pp. 215–238, 1990. […]

No views yet

 

H. Lütkepohl and M. Kr?tzig, “Applied time series econometrics,” Cambridge ...

H. Lütkepohl and M. Kr?tzig, “Applied time series econometrics,” Cambridge University Press, New York, 2004. **H. Lütkepohl and M. Krätzig, “Applied time series econometrics,” Cambridge University Press, […]

1 total views, 1 today

 

P. S. Neelakanta and A. Preechayasomboon, “Development of a neuroinference ...

P. S. Neelakanta and A. Preechayasomboon, “Development of a neuroinference engine for ADSL modem applications in telecommunications using an ANN with fast computational ability,” Neurocomputing, […]

1 total views, 1 today

 

G. Caporello and A. Maravall, “TSW—Revised reference manual,” mimeo, Banco ...

G. Caporello and A. Maravall, “TSW—Revised reference manual,” mimeo, Banco de Espa?a, 2004. None

1 total views, 1 today

 

H. Akaike, “Information theory and the extension of the maximum likelihood ...

H. Akaike, “Information theory and the extension of the maximum likelihood principle,” In: B. N. Petrov and F. Caaki, Eds., Second International Symposium on Information […]

1 total views, 1 today

 

H. Akaike, “A new look at the statistical model identification,” IEEE Trans...

H. Akaike, “A new look at the statistical model identification,” IEEE Transactions on Automatic Control, Vol. 19, No. 6, pp. 716–723, 1974. **A New Look […]

1 total views, 1 today

 

D. F. Findley and D. E. K. Martin, “Frequency domain analyses of SEATS and ...

D. F. Findley and D. E. K. Martin, “Frequency domain analyses of SEATS and X-11/12-ARIMA seasonal adjustment filters for short and moderate-length time series,” Journal […]

1 total views, 1 today

 

S. C. Hillmer and G. C. Tiao, “An ARIMA model–based approach to seasonal ad...

S. C. Hillmer and G. C. Tiao, “An ARIMA model–based approach to seasonal adjustment,” Journal of the American Statistical Association, Vol. 77, pp. 63–70, 2002. […]

1 total views, 1 today

 

D. F. Findley, D. E. K. Martin, and K. Wills, “Generalizations of the Box–J...

D. F. Findley, D. E. K. Martin, and K. Wills, “Generalizations of the Box–Jenkins airline model,” Proceedings of the American Statistical Association, Business and Economic […]

1 total views, 1 today

 

G. Box and G. M. Jenkins, “Time series analysis: Forecasting and control,” ...

G. Box and G. M. Jenkins, “Time series analysis: Forecasting and control,” Holden–Day, Oakland, 1976. **Time Series Analysis: Forecasting and Control** Time series analysis has […]

1 total views, 1 today